diff --git a/.github/changed-files.yml b/.github/changed-files.yml index da113c09f..ae83dd29c 100644 --- a/.github/changed-files.yml +++ b/.github/changed-files.yml @@ -16,10 +16,8 @@ cpp: - icicle/**/*.c - icicle/**/*.h - icicle/CMakeLists.txt - - .github/workflows/cpp_cuda.yml - - icicle/cmake/Common.cmake - - icicle/cmake/CurvesCommon.cmake - - icicle/cmake/FieldsCommon.cmake + - icicle/cmake/**/*.cmake + - .github/workflows/cpp.yml examples: - examples/**/* - .github/workflows/examples.yml diff --git a/.github/workflows/check-changed-files.yml b/.github/workflows/check-changed-files.yml index db0dac450..12677daa9 100644 --- a/.github/workflows/check-changed-files.yml +++ b/.github/workflows/check-changed-files.yml @@ -9,9 +9,9 @@ on: rust: description: "Flag for if Rust files changed" value: ${{ jobs.check-changed-files.outputs.rust }} - cpp_cuda: - description: "Flag for if C++/CUDA files changed" - value: ${{ jobs.check-changed-files.outputs.cpp_cuda }} + cpp: + description: "Flag for if C++ files changed" + value: ${{ jobs.check-changed-files.outputs.cpp }} examples: description: "Flag for if example files changed" value: ${{ jobs.check-changed-files.outputs.examples }} @@ -23,7 +23,7 @@ jobs: outputs: golang: ${{ steps.changed_files.outputs.golang }} rust: ${{ steps.changed_files.outputs.rust }} - cpp_cuda: ${{ steps.changed_files.outputs.cpp_cuda }} + cpp: ${{ steps.changed_files.outputs.cpp }} examples: ${{ steps.changed_files.outputs.examples }} steps: - name: Checkout Repo @@ -40,5 +40,5 @@ jobs: run: | echo "golang=${{ steps.changed-files-yaml.outputs.golang_any_modified }}" >> "$GITHUB_OUTPUT" echo "rust=${{ steps.changed-files-yaml.outputs.rust_any_modified }}" >> "$GITHUB_OUTPUT" - echo "cpp_cuda=${{ steps.changed-files-yaml.outputs.cpp_any_modified }}" >> "$GITHUB_OUTPUT" + echo "cpp=${{ steps.changed-files-yaml.outputs.cpp_any_modified }}" >> "$GITHUB_OUTPUT" echo "examples=${{ steps.changed-files-yaml.outputs.examples_any_modified }}" >> "$GITHUB_OUTPUT" diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml index ff9738e1e..1db2f868e 100644 --- a/.github/workflows/codespell.yml +++ b/.github/workflows/codespell.yml @@ -4,7 +4,8 @@ on: pull_request: branches: - main - - V2 + - V3 + - yshekel/V3 jobs: spelling-checker: @@ -15,6 +16,6 @@ jobs: - uses: codespell-project/actions-codespell@v2 with: # https://github.com/codespell-project/actions-codespell?tab=readme-ov-file#parameter-skip - skip: ./**/target,./**/build,./docs/*.js,./docs/*.json + skip: ./**/target,./**/build,./docs/*.js,./docs/*.json,./*.svg # https://github.com/codespell-project/actions-codespell?tab=readme-ov-file#parameter-ignore_words_file ignore_words_file: .codespellignore diff --git a/.github/workflows/cpp_cuda.yml b/.github/workflows/cpp.yml similarity index 59% rename from .github/workflows/cpp_cuda.yml rename to .github/workflows/cpp.yml index bb57823af..fbd72c101 100644 --- a/.github/workflows/cpp_cuda.yml +++ b/.github/workflows/cpp.yml @@ -3,12 +3,11 @@ name: C++/CUDA on: pull_request: branches: - - main - - V2 + - V3 + - yshekel/V3 # TODO remove when merged to V3 push: branches: - - main - - V2 + - V3 concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -26,13 +25,18 @@ jobs: - name: Checkout uses: actions/checkout@v4 - name: Check clang-format - if: needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' run: if [[ $(find ./ \( -path ./icicle/build -prune -o -path ./**/target -prune -o -path ./examples -prune \) -iname *.h -or -iname *.cuh -or -iname *.cu -or -iname *.c -or -iname *.cpp | xargs clang-format --dry-run -ferror-limit=1 -style=file 2>&1) ]]; then echo "Please run clang-format"; exit 1; fi + extract-cuda-backend-branch: + uses: ./.github/workflows/extract-backends.yml + with: + pr-number: ${{ github.event.pull_request.number }} + test-linux-curve: name: Test on Linux runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] + needs: [check-changed-files, check-format, extract-cuda-backend-branch] strategy: matrix: curve: @@ -50,25 +54,33 @@ jobs: steps: - name: Checkout Repo uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: Build curve working-directory: ./icicle - if: needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' run: | mkdir -p build && rm -rf build/* - cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DCURVE=${{ matrix.curve.name }} ${{ matrix.curve.build_args }} -S . -B build + cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DCURVE=${{ matrix.curve.name }} ${{ matrix.curve.build_args }} -DCUDA_BACKEND=local -S . -B build cmake --build build -j - name: Run C++ curve Tests working-directory: ./icicle/build/tests - if: needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' run: ctest test-linux-field: name: Test on Linux runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] + needs: [check-changed-files, check-format, extract-cuda-backend-branch] strategy: matrix: - field: + field: - name: babybear build_args: -DEXT_FIELD=ON - name: stark252 @@ -78,14 +90,22 @@ jobs: steps: - name: Checkout Repo uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: Build field working-directory: ./icicle - if: needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' run: | mkdir -p build && rm -rf build/* - cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DFIELD=${{ matrix.field.name }} ${{ matrix.field.build_args }} -S . -B build + cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DFIELD=${{ matrix.field.name }} ${{ matrix.field.build_args }} -DCUDA_BACKEND=local -S . -B build cmake --build build -j - name: Run C++ field Tests working-directory: ./icicle/build/tests - if: needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' run: ctest \ No newline at end of file diff --git a/.github/workflows/examples.yml b/.github/workflows/examples.yml index c6fe2c24f..c5240e6db 100644 --- a/.github/workflows/examples.yml +++ b/.github/workflows/examples.yml @@ -1,8 +1,7 @@ # This workflow is a demo of how to run all examples in the Icicle repository. -# For each language directory (c++, Rust, etc.) the workflow -# (1) loops over all examples (msm, ntt, etc.) and -# (2) runs ./compile.sh and ./run.sh in each directory. -# The script ./compile.sh should compile the example and ./run.sh should run it. +# For each language directory (c++, Rust, etc.) the workflow +# (1) loops over all examples (msm, ntt, etc.) and +# (2) runs ./run.sh in each directory. # Each script should return 0 for success and 1 otherwise. name: Examples @@ -10,12 +9,11 @@ name: Examples on: pull_request: branches: - - main - - V2 + - V3 + - yshekel/V3 # TODO remove when merged to V3 push: branches: - - main - - V2 + - V3 concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -25,26 +23,38 @@ jobs: check-changed-files: uses: ./.github/workflows/check-changed-files.yml + extract-cuda-backend-branch: + uses: ./.github/workflows/extract-backends.yml + with: + pr-number: ${{ github.event.pull_request.number }} + run-examples: runs-on: [self-hosted, Linux, X64, icicle, examples] - needs: check-changed-files + needs: [check-changed-files, extract-cuda-backend-branch] steps: - name: Checkout uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: c++ examples working-directory: ./examples/c++ - if: needs.check-changed-files.outputs.cpp_cuda == 'true' || needs.check-changed-files.outputs.examples == 'true' + if: needs.check-changed-files.outputs.cpp == 'true' || needs.check-changed-files.outputs.examples == 'true' run: | # loop over all directories in the current directory for dir in $(find . -mindepth 1 -maxdepth 1 -type d); do if [ -d "$dir" ]; then echo "Running command in $dir" cd $dir - ./compile.sh - ./run.sh + ./run.sh -d CUDA cd - fi - done + done - name: Rust examples working-directory: ./examples/rust if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.examples == 'true' @@ -54,7 +64,7 @@ jobs: if [ -d "$dir" ]; then echo "Running command in $dir" cd $dir - cargo run --release + ./run.sh -d CUDA cd - fi - done \ No newline at end of file + done \ No newline at end of file diff --git a/.github/workflows/extract-backends.yml b/.github/workflows/extract-backends.yml new file mode 100644 index 000000000..80aac40d9 --- /dev/null +++ b/.github/workflows/extract-backends.yml @@ -0,0 +1,36 @@ +name: Extract Icicle Backend Branch + +on: + workflow_call: + inputs: + pr-number: + description: 'The PR number to fetch the description for' + required: true + type: number + outputs: + cuda-backend-branch: + description: "Branch name for cuda backend" + value: ${{ jobs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} + +jobs: + extract-cuda-backend-branch: + name: Extract cuda branch name + runs-on: ubuntu-22.04 + outputs: + cuda-backend-branch: ${{ steps.extract.outputs.cuda-backend-branch }} + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Extract Private Branch from PR Description + id: extract + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + DESCRIPTION=$(gh pr view ${{ inputs.pr-number }} --json body -q '.body') + echo "PR Description: $DESCRIPTION" + CUDA_BE_BRANCH=$(echo "$DESCRIPTION" | grep -oP 'cuda-backend-branch:\s*\K[^\s]+') || true + if [ -z "$CUDA_BE_BRANCH" ]; then + CUDA_BE_BRANCH="main" # Default branch if not specified + fi + echo "Extracted CUDA Backend Branch: $CUDA_BE_BRANCH" + echo "cuda-backend-branch=$CUDA_BE_BRANCH" >> "$GITHUB_OUTPUT" \ No newline at end of file diff --git a/.github/workflows/golang.yml b/.github/workflows/golang.yml index d6d76b832..997728a0c 100644 --- a/.github/workflows/golang.yml +++ b/.github/workflows/golang.yml @@ -3,12 +3,11 @@ name: GoLang on: pull_request: branches: - - main - - V2 + - V3 + - yshekel/V3 # TODO remove when merged to V3 push: branches: - - main - - V2 + - V3 concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -33,26 +32,39 @@ jobs: if: needs.check-changed-files.outputs.golang == 'true' run: if [[ $(go list ./... | xargs go fmt) ]]; then echo "Please run go fmt"; exit 1; fi + extract-cuda-backend-branch: + uses: ./.github/workflows/extract-backends.yml + with: + pr-number: ${{ github.event.pull_request.number }} + build-curves-linux: name: Build and test curves on Linux runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] + needs: [check-changed-files, check-format, extract-cuda-backend-branch] strategy: matrix: curve: - name: bn254 - build_args: -g2 -ecntt + build_args: - name: bls12_381 - build_args: -g2 -ecntt + build_args: - name: bls12_377 - build_args: -g2 -ecntt + build_args: - name: bw6_761 - build_args: -g2 -ecntt + build_args: - name: grumpkin build_args: steps: - name: Checkout Repo uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: Setup go uses: actions/setup-go@v5 with: @@ -61,27 +73,36 @@ jobs: working-directory: ./wrappers/golang if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' # builds a single curve with the curve's specified build args - run: ./build.sh -curve=${{ matrix.curve.name }} ${{ matrix.curve.build_args }} + run: | + ./build.sh -curve=${{ matrix.curve.name }} ${{ matrix.curve.build_args }} -cuda_backend=local - name: Test working-directory: ./wrappers/golang/curves if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' run: | CURVE=$(echo ${{ matrix.curve.name }} | sed -e 's/_//g') - export CPATH=$CPATH:/usr/local/cuda/include + export ICICLE_BACKEND_INSTALL_DIR=/usr/local/lib go test ./$CURVE/tests -count=1 -failfast -p 2 -timeout 60m -v build-fields-linux: name: Build and test fields on Linux runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] + needs: [check-changed-files, check-format, extract-cuda-backend-branch] strategy: matrix: field: - name: babybear - build_args: -field-ext + build_args: steps: - name: Checkout Repo uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: Setup go uses: actions/setup-go@v5 with: @@ -90,73 +111,12 @@ jobs: working-directory: ./wrappers/golang if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' # builds a single field with the fields specified build args - run: ./build.sh -field=${{ matrix.field.name }} ${{ matrix.field.build_args }} + run: | + ./build.sh -field=${{ matrix.field.name }} ${{ matrix.field.build_args }} -cuda_backend=local - name: Test working-directory: ./wrappers/golang/fields if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' run: | FIELD=$(echo ${{ matrix.field.name }} | sed -e 's/_//g') - export CPATH=$CPATH:/usr/local/cuda/include + export ICICLE_BACKEND_INSTALL_DIR=/usr/local/lib go test ./$FIELD/tests -count=1 -failfast -p 2 -timeout 60m -v - - build-hashes-linux: - name: Build and test hashes on Linux - runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] - strategy: - matrix: - hash: - - name: keccak - build_args: - steps: - - name: Checkout Repo - uses: actions/checkout@v4 - - name: Setup go - uses: actions/setup-go@v5 - with: - go-version: '1.20.0' - - name: Build - working-directory: ./wrappers/golang - if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # builds a single hash algorithm with the hash's specified build args - run: ./build.sh -hash=${{ matrix.hash.name }} ${{ matrix.hash.build_args }} - - name: Test - working-directory: ./wrappers/golang/hash - if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - run: | - HASH=$(echo ${{ matrix.hash.name }} | sed -e 's/_//g') - export CPATH=$CPATH:/usr/local/cuda/include - go test ./$HASH/tests -count=1 -failfast -p 2 -timeout 60m -v - - # TODO: bw6 on windows requires more memory than the standard runner has - # Add a large runner and then enable this job - # build-windows: - # name: Build on Windows - # runs-on: windows-2022 - # needs: [check-changed-files, check-format] - # strategy: - # matrix: - # curve: [bn254, bls12_381, bls12_377, bw6_761] - # steps: - # - name: Checkout Repo - # uses: actions/checkout@v4 - # - name: Setup go - # uses: actions/setup-go@v5 - # with: - # go-version: '1.20.0' - # - name: Download and Install Cuda - # if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # id: cuda-toolkit - # uses: Jimver/cuda-toolkit@v0.2.11 - # with: - # cuda: '12.0.0' - # method: 'network' - # # https://docs.nvidia.com/cuda/archive/12.0.0/cuda-installation-guide-microsoft-windows/index.html - # sub-packages: '["cudart", "nvcc", "thrust", "visual_studio_integration"]' - # - name: Build libs - # if: needs.check-changed-files.outputs.golang == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # working-directory: ./wrappers/golang - # env: - # CUDA_PATH: ${{ steps.cuda-toolkit.outputs.CUDA_PATH }} - # shell: pwsh - # run: ./build.ps1 ${{ matrix.curve }} ON # builds a single curve with G2 enabled diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index af8f024b4..da1332470 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -22,6 +22,14 @@ jobs: uses: actions/checkout@v4 with: ssh-key: ${{ secrets.DEPLOY_KEY }} + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: main - name: Setup Cache id: cache uses: actions/cache@v4 @@ -46,5 +54,15 @@ jobs: env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | + mkdir -p release_output && rm -rf ./release_output/* + ./scripts/release/build_all.sh ./release_output LATEST_TAG=$(git describe --tags --abbrev=0) gh release create $LATEST_TAG --generate-notes -d --verify-tag -t "Release $LATEST_TAG" + - name: Upload release tars + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + LATEST_TAG=$(git describe --tags --abbrev=0) + for file in ./release_output/*.tar.gz; do + gh release upload $LATEST_TAG "$file" + done diff --git a/.github/workflows/rust.yml b/.github/workflows/rust.yml index 09b3d2061..fd96fc9b9 100644 --- a/.github/workflows/rust.yml +++ b/.github/workflows/rust.yml @@ -3,12 +3,11 @@ name: Rust on: pull_request: branches: - - main - - V2 + - V3 + - yshekel/V3 # TODO remove when merged to V3 push: branches: - - main - - V2 + - V3 concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -26,7 +25,7 @@ jobs: - name: Checkout uses: actions/checkout@v4 - name: Check rustfmt - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' + if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp == 'true' working-directory: ./wrappers/rust # "-name target -prune" removes searching in any directory named "target" # Formatting by single file is necessary due to generated files not being present @@ -35,78 +34,32 @@ jobs: # causing rustfmt to fail. run: if [[ $(find . -path ./icicle-curves/icicle-curve-template -prune -o -name target -prune -o -iname *.rs -print | xargs cargo fmt --check --) ]]; then echo "Please run cargo fmt"; exit 1; fi - build-linux: - name: Build on Linux - runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, check-format] - steps: - - name: Checkout Repo - uses: actions/checkout@v4 - - name: Build - working-directory: ./wrappers/rust - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # Building from the root workspace will build all members of the workspace by default - run: cargo build --release --verbose - + extract-cuda-backend-branch: + uses: ./.github/workflows/extract-backends.yml + with: + pr-number: ${{ github.event.pull_request.number }} + test-linux: name: Test on Linux runs-on: [self-hosted, Linux, X64, icicle] - needs: [check-changed-files, build-linux] + needs: [check-changed-files, check-format, extract-cuda-backend-branch] steps: - name: Checkout Repo uses: actions/checkout@v4 + - name: Checkout CUDA Backend + uses: actions/checkout@v4 + with: + repository: ingonyama-zk/icicle-cuda-backend + path: ./icicle/backend/cuda + token: ${{ secrets.GITHUB_TOKEN }} + ssh-key: ${{ secrets.CUDA_PULL_KEY }} + ref: ${{ needs.extract-cuda-backend-branch.outputs.cuda-backend-branch }} - name: Run tests working-directory: ./wrappers/rust - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # Running tests from the root workspace will run all workspace members' tests by default - # We need to limit the number of threads to avoid running out of memory on weaker machines - # ignored tests are polynomial tests. Since they conflict with NTT tests, they are executed separately + if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp == 'true' + # tests are split to phases since NTT domain is global but tests have conflicting requirements run: | - cargo test --workspace --exclude icicle-babybear --exclude icicle-stark252 --exclude icicle-m31 --release --verbose --features=g2 -- --test-threads=2 --ignored - cargo test --workspace --exclude icicle-babybear --exclude icicle-stark252 --exclude icicle-m31 --release --verbose --features=g2 -- --test-threads=2 - - - name: Run baby bear tests - working-directory: ./wrappers/rust/icicle-fields/icicle-babybear - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - run: | - cargo test --release --verbose -- --ignored - cargo test --release --verbose - - - name: Run stark252 tests - working-directory: ./wrappers/rust/icicle-fields/icicle-stark252 - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - run: | - cargo test --release --verbose -- --ignored - cargo test --release --verbose - - - name: Run m31 tests - working-directory: ./wrappers/rust/icicle-fields/icicle-m31 - if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - run: | - cargo test --release --verbose -- --ignored - cargo test --release --verbose - - # build-windows: - # name: Build on Windows - # runs-on: windows-2022 - # needs: check-changed-files - # steps: - # - name: Checkout Repo - # uses: actions/checkout@v4 - # - name: Download and Install Cuda - # if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # id: cuda-toolkit - # uses: Jimver/cuda-toolkit@v0.2.11 - # with: - # cuda: '12.0.0' - # method: 'network' - # # https://docs.nvidia.com/cuda/archive/12.0.0/cuda-installation-guide-microsoft-windows/index.html - # sub-packages: '["cudart", "nvcc", "thrust", "visual_studio_integration"]' - # - name: Build targets - # working-directory: ./wrappers/rust - # if: needs.check-changed-files.outputs.rust == 'true' || needs.check-changed-files.outputs.cpp_cuda == 'true' - # env: - # CUDA_PATH: ${{ steps.cuda-toolkit.outputs.CUDA_PATH }} - # CUDA_ARCH: 50 # Using CUDA_ARCH=50 env variable since the CI machines have no GPUs - # # Building from the root workspace will build all members of the workspace by default - # run: cargo build --release --verbose + cargo build --workspace --release --features=cuda_backend + cargo test --workspace --release --verbose --features=cuda_backend -- --skip phase + cargo test phase2 --workspace --release --features=cuda_backend + cargo test phase3 --workspace --release --features=cuda_backend diff --git a/.gitignore b/.gitignore index efe26533f..fd8086c28 100644 --- a/.gitignore +++ b/.gitignore @@ -17,4 +17,6 @@ **/Cargo.lock **/icicle/build/ **/wrappers/rust/icicle-cuda-runtime/src/bindings.rs -**/build* +**/build/* +**tar.gz +icicle/backend/cuda diff --git a/Frostythelion.gif b/Frostythelion.gif new file mode 100644 index 000000000..ad184dec5 Binary files /dev/null and b/Frostythelion.gif differ diff --git a/INGOLOGO.svg b/INGOLOGO.svg new file mode 100644 index 000000000..2c01fcd3b --- /dev/null +++ b/INGOLOGO.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/McPaw.gif b/McPaw.gif new file mode 100644 index 000000000..d688da6ab Binary files /dev/null and b/McPaw.gif differ diff --git a/README.md b/README.md index f94fac89c..a2bf0c134 100644 --- a/README.md +++ b/README.md @@ -1,97 +1,287 @@ # ICICLE -
ICICLE is a library for ZK acceleration using CUDA-enabled GPUs.
+
+ ICICLE is a high-performance cryptographic acceleration library designed to optimize cryptographic computations across various hardware platforms, including CPUs, GPUs, and other accelerators. +

- ICICLE +
+ ICICLE

+

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- Chat with us on Discord + Chat with us on Discord + + + Follow us on LinkedIn + + + Follow us on Twitter - - Follow us on Twitter - GitHub Release + GitHub Release

+ + ## Background Zero Knowledge Proofs (ZKPs) are considered one of the greatest achievements of modern cryptography. Accordingly, ZKPs are expected to disrupt a number of industries and will usher in an era of trustless and privacy preserving services and infrastructure. -We believe GPUs are as important for ZK as for AI. +We believe that ICICLE will be a cornerstone in the acceleration of ZKPs: -- GPUs are a perfect match for ZK compute - around 97% of ZK protocol runtime is parallel by nature. -- GPUs are simple for developers to use and scale compared to other hardware platforms. -- GPUs are extremely competitive in terms of power / performance and price (3x cheaper). -- GPUs are popular and readily available. +- **Versatility**: Supports multiple hardware platforms, making it adaptable to various computational environments. +- **Efficiency:** Designed to leverage the parallel nature of ZK computations, whether on GPUs, CPUs, or other accelerators. +- **Scalability:** Provides an easy-to-use and scalable solution for developers, allowing them to optimize cryptographic operations with minimal effort. ## Getting Started -ICICLE is a CUDA implementation of general functions widely used in ZKP. +This guide will help you get started with ICICLE in C++, Rust, and Go. > [!NOTE] -> Developers: We highly recommend reading our [documentation] +> **Developers**: We highly recommend reading our [documentation](https://dev.ingonyama.com/) for a comprehensive explanation of ICICLE’s capabilities. > [!TIP] -> Try out ICICLE by running some [examples] using ICICLE in C++ and our Rust bindings +> Try out ICICLE by running some [examples] available in C++, Rust, and Go bindings. Check out our install-and-use examples in [C++](https://github.com/ingonyama-zk/icicle/tree/yshekel/V3/examples/c%2B%2B/install-and-use-icicle), [Rust](https://github.com/ingonyama-zk/icicle/tree/yshekel/V3/examples/rust/install-and-use-icicle) and [Go](TODO) ### Prerequisites -- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) version 12.0 or newer. -- [CMake]((https://cmake.org/files/)), version 3.18 and above. Latest version is recommended. -- [GCC](https://gcc.gnu.org/install/download.html) version 9, latest version is recommended. -- Any Nvidia GPU (which supports CUDA Toolkit version 12.0 or above). +- Any compatible hardware: ICICLE supports various hardware, including CPUs, Nvidia GPUs, and other accelerators. +- [CMake](https://cmake.org/files/), Version 3.18 or above. Latest version recommended. Required only if building from source. +- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads), Required only if using NVIDIA GPUs (version 12.0 or newer). > [!NOTE] -> It is possible to use CUDA 11 for cards which don't support CUDA 12, however we don't officially support this version and in the future there may be issues. +> For older GPUs that only support CUDA 11, ICICLE may still function, but official support is for CUDA 12 onwards. -### Accessing Hardware -If you don't have access to an Nvidia GPU we have some options for you. +### Accessing Hardware -Checkout [Google Colab](https://colab.google/). Google Colab offers a free [T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/) instance and ICICLE can be used with it, reference this guide for setting up your [Google Colab workplace][GOOGLE-COLAB-ICICLE]. +If you don't have access to an Nvidia GPU we have some options for you. + +[Google Colab](https://colab.google/) offers a free [T4 GPU](https://www.nvidia.com/en-us/data-center/tesla-t4/) instance and ICICLE can be used with it, reference this guide for setting up your [Google Colab workplace][GOOGLE-COLAB-ICICLE]. If you require more compute and have an interesting research project, we have [bounty and grant programs][GRANT_PROGRAM]. +## Building ICICLE from source + +ICICLE provides build systems for C++, Rust, and Go. Each build system incorporates the core ICICLE library, which contains the essential cryptographic primitives. Refer to the [Getting started page](https://dev.ingonyama.com/icicle/introduction) for full details about building and using ICICLE. + +> [!WARNING] +> Ensure ICICLE libraries are installed correctly when building or installing a library/application that depends on ICICLE so that they can be located at runtime. + +### Rust + +In cargo.toml, specify the ICICLE libs to use: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here if need additional fields/curves +``` + +You can specify `branch=branch-name`, `tag=tag-name`, or `rev=commit-id`. + +Build the Rust project: + +```bash +cargo build --release +``` + +### Go + +There are two ways to build from source in Go: + +1. Clone the repo, update your go.mod to point to the local clone, and build ICICLE within the clone + +```sh +git clone https://github.com/ingonyama-zk/icicle.git +``` + +Add ICICLE v3 to your go.mod file: + +```go +require github.com/ingonyama-zk/icicle/v3 v3.0.0 + +replace github.com/ingonyama-zk/icicle/v3 => ../path/to/cloned/icicle +``` + +Navigate to the cloned repo's golang bindings and build the library using the supplied [build script][ICICLE-GO-BUILD-SCRIPT] + +```sh +cd icicle/wrappers/golang +chmod +x build.sh +./build.sh -curve=bn254 +``` + +2. Update your go.mod to include ICICLE as a dependency, navigate to the dependency in your GOMODCACHE and build ICICLE there + +```sh +go get github.com/ingonyama-zk/icicle/v3 +cd $(go env GOMODCACHE)/github.com/ingonyama-zk/icicle/v3@/wrappers/golang +chmod +x build.sh +./build.sh -curve=bn254 +``` + +> [!NOTE] +> To specify the field, use the flag -field=, where can be one of the following: babybear, stark252, m31. +> To specify a curve, use the flag -curve=, where can be one of the following: bn254, bls12_377, bls12_381, bw6_761, grumpkin. + +Once ICICLE has been built, you can add specific packages when you need them in your application: + +```go +import ( + runtime "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + core "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254MSM "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) +``` + +### C++ + +ICICLE can be built and tested in C++ using CMake. The build process is straightforward, but there are several flags you can use to customize the build for your needs. + +**Clone the ICICLE repository:** -### Build systems +```bash +git clone https://github.com/ingonyama-zk/icicle.git +cd icicle +``` + +**Configure the build:** -ICICLE has three build systems. +```bash +mkdir -p build && rm -rf build/* +cmake -S icicle -B build -DFIELD=babybear +``` -- [ICICLE core][ICICLE-CORE], C++ and CUDA -- [ICICLE Rust][ICICLE-RUST] bindings, requires [Rust](https://www.rust-lang.org/) version 1.70 and above -- [ICICLE Golang][ICICLE-GO] bindings, requires [Go](https://go.dev/) version 1.20 and above +> [!NOTE] +> To specify the field, use the flag -DFIELD=field, where field can be one of the following: babybear, stark252, m31. +> To specify a curve, use the flag -DCURVE=curve, where curve can be one of the following: bn254, bls12_377, bls12_381, bw6_761, grumpkin. -ICICLE core always needs to be built as part of the other build systems as it contains the core ICICLE primitives implemented in CUDA. Reference these guides for the different build systems, [ICICLE core guide][ICICLE-CORE-README], [ICICLE Rust guide][ICICLE-RUST-README] and [ICICLE Golang guide][ICICLE-GO-README]. +**Build the project:** -### Compiling ICICLE +```bash +cmake --build build -j # -j is for multi-core compilation +``` -Running ICICLE via Rust bindings is highly recommended and simple: -- Clone this repo - - go to our [Rust bindings][ICICLE-RUST] - - Enter a [curve](./wrappers/rust/icicle-curves) implementation - - run `cargo build --release` to build or `cargo test` to build and execute tests +**Link you application (or library) to ICICLE:** -In any case you would want to compile and run core icicle c++ tests, just follow these setps: -- Clone this repo - - go to [ICICLE core][ICICLE-CORE] - - execute the small [script](https://github.com/ingonyama-zk/icicle/tree/main/icicle#running-tests) to compile via cmake and run c++ and cuda tests +```cmake +target_link_libraries(yourApp PRIVATE icicle_field_babybear icicle_device) +``` -## Docker +**Install (optional):** -We offer a simple Docker container so you can simply run ICICLE without setting everything up locally. +To install the libs, specify the install prefix `-DCMAKE_INSTALL_PREFIX=/install/dir/`. Then after building, use cmake to install the libraries: +```sh +cmake -S icicle -B build -DFIELD=babybear -DCMAKE_INSTALL_PREFIX=/path/to/install/dir/ +cmake --build build -j # build +cmake --install build # install icicle to /path/to/install/dir/ ``` -docker build -t . -docker run --gpus all -it /bin/bash + +**Run tests (optional):** + +Add `-DBUILD_TESTS=ON` to the cmake command, build and execute tests: + +```bash +cmake -S icicle -B build -DFIELD=babybear -DBUILD_TESTS=ON +cmake --build build -j +cd build/tests +ctest ``` +or choose the test-suite + +```bash +./build/tests/test_field_api # or another test suite +# can specify tests using regex. For example for tests with ntt in the name: +./build/tests/test_field_api --gtest_filter="*ntt*" +``` + +> [!NOTE] +> Most tests assume a CUDA backend exists and will fail otherwise, if a CUDA device is not found. + +**Build Flags:** + +You can customize your ICICLE build with the following flags: + +- `-DCPU_BACKEND=ON/OFF`: Enable or disable built-in CPU backend. `default=ON`. +- `-DCMAKE_INSTALL_PREFIX=/install/dir`: Specify install directory. `default=/usr/local`. +- `-DBUILD_TESTS=ON/OFF`: Enable or disable tests. `default=OFF`. +- `-DBUILD_BENCHMARKS=ON/OFF`: Enable or disable benchmarks. `default=OFF`. + +## Install CUDA backend + +To install the CUDA backend + +1. [Download the release binaries](https://github.com/ingonyama-zk/icicle/releases/). +2. Install it, by extracting the binaries to `/opt/` or any other custom install path. +3. In your application, load the cuda backend and select a CUDA device. +4. All subsequent API will now use the selected device. + + +Rust: + +```rust +use icicle_runtime::{runtime, Device}; + +runtime::load_backend_from_env_or_default().unwrap(); +// or load programmatically +runtime::load_backend("/path/to/backend/installdir").unwrap(); +// Select CUDA device +let device = Device::new("CUDA", 1 /*gpu-id*/); +icicle_runtime::set_device(&device).unwrap(); + +// Any call will now execute on GPU-1 +``` + +Go: + +```go +import( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +result := runtime.LoadBackendFromEnvOrDefault() +// or load from custom install dir +result := runtime.LoadBackend("/path/to/backend/installdir", true) +// Select CUDA device +device := runtime.CreateDevice("CUDA", 0) // or other +result := runtime.SetDevice(device) + +// Any call will now execute on GPU-0 +``` +C++: + +```cpp +#include "icicle/runtime.h" + +// Load the installed backend +eIcicleError result = icicle_load_backend_from_env_or_default(); +// or load it programmatically +eIcicleError result = icicle_load_backend("/path/to/backend/installdir", true); + +// Select CUDA device +icicle::Device device = {"CUDA", 0 /*gpu-id*/}; +eIcicleError result = icicle_set_device(device); + +// Any call will now execute on GPU-0 +``` + +Full details can be found in our [getting started docs](https://dev.ingonyama.com/icicle/introduction) + ## Contributions -Join our [Discord Server][DISCORD] and find us on the icicle channel. We will be happy to work together to support your use case and talk features, bugs and design. +Join our [Discord Server][DISCORD] and find us on the ICICLE channel. We will be happy to work together to support your use case, and talk features, bugs and design. ### Development Contributions @@ -123,12 +313,14 @@ This will ensure our custom hooks are run and will make it easier to follow our ## Help & Support -For help and support talk to our devs in our discord channel ["ICICLE"](https://discord.gg/EVVXTdt6DF) - +For help and support talk to our devs in our discord channel [#ICICLE](https://discord.gg/EVVXTdt6DF) or contact us at . ## License -ICICLE is distributed under the terms of the MIT License. +ICICLE frontend is distributed under the terms of the MIT License. + +> [!NOTE] +> ICICLE backends, excluding the CPU backend, are distributed under a special license and are not covered by the MIT license. See [LICENSE-MIT][LMIT] for details. @@ -137,18 +329,21 @@ See [LICENSE-MIT][LMIT] for details. [BLS12-377]: ./icicle/curves/ [BN254]: ./icicle/curves/ [BW6-671]: ./icicle/curves/ -[NVCC]: https://docs.nvidia.com/cuda/#installation-guides +[Grumpkin]: ./icicle/curves/ +[babybear]: ./icicle/fields/ +[stark252]: ./icicle/fields/ +[m31]: ./icicle/fields/ [LMIT]: ./LICENSE [DISCORD]: https://discord.gg/Y4SkbDf2Ff [googletest]: https://github.com/google/googletest/ [HOOKS_DOCS]: https://git-scm.com/docs/githooks [HOOKS_PATH]: ./scripts/hooks/ -[CMAKELISTS]: https://github.com/ingonyama-zk/icicle/blob/f0e6b465611227b858ec4590f4de5432e892748d/icicle/CMakeLists.txt#L28 [GOOGLE-COLAB-ICICLE]: https://dev.ingonyama.com/icicle/colab-instructions [GRANT_PROGRAM]: https://medium.com/@ingonyama/icicle-for-researchers-grants-challenges-9be1f040998e [ICICLE-CORE]: ./icicle/ [ICICLE-RUST]: ./wrappers/rust/ [ICICLE-GO]: ./wrappers/golang/ +[ICICLE-GO-BUILD-SCRIPT]: ./wrappers/golang/build.sh [ICICLE-CORE-README]: ./icicle/README.md [ICICLE-RUST-README]: ./wrappers/rust/README.md [ICICLE-GO-README]: ./wrappers/golang/README.md diff --git a/docs/docs/icicle/arch_overview.md b/docs/docs/icicle/arch_overview.md new file mode 100644 index 000000000..5529ba4cb --- /dev/null +++ b/docs/docs/icicle/arch_overview.md @@ -0,0 +1,34 @@ +# Architecture Overview + +## Introduction + +ICICLE V3 is designed with flexibility and extensibility in mind, offering a robust framework that supports multiple compute backends and accommodates various cryptographic needs. This section provides an overview of ICICLE's architecture, highlighting its open and closed components, multi-device support, and extensibility. + +## Open Frontend and CPU Backend + +- **Frontend (FE):** The ICICLE frontend is open-source and designed to provide a unified API across different programming languages, including C++, Rust, and Go. This frontend abstracts the complexity of working with different backends, allowing developers to write backend-agnostic code that can be deployed across various platforms. +- **CPU Backend:** ICICLE includes an open-source CPU backend that allows for development and testing on standard hardware. This backend is ideal for prototyping and for environments where specialized hardware is not available. + +## Closed CUDA Backend + +- **CUDA Backend:** ICICLE also includes a high-performance CUDA backend that is closed-source. This backend is optimized for NVIDIA GPUs and provides significant acceleration for cryptographic operations. +- **Installation and Licensing:** The CUDA backend needs to be downloaded and installed. Refer to the [installation guide](./install_cuda_backend.md) for detailed instructions. + +## Extensible Design + +ICICLE is designed to be extensible, allowing developers to integrate new backends or customize existing ones to suit their specific needs. The architecture supports: + +- **Custom Backends:** Developers can create their own backends to leverage different hardware or optimize for specific use cases. The process of building and integrating a custom backend is documented in the [Build Your Own Backend](./build_your_own_backend.md) section. +- **Pluggable Components:** ICICLE's architecture allows for easy integration of additional cryptographic primitives or enhancements, ensuring that the framework can evolve with the latest advancements in cryptography and hardware acceleration. + +## Multi-Device Support + +- **Scalability:** ICICLE supports multi-device configurations, enabling the distribution of workloads across multiple GPUs or other hardware accelerators. This feature allows for scaling ZK proofs and other cryptographic operations across larger data centers or high-performance computing environments. + +--- + +### Conclusion + +The architecture of ICICLE V3 is built to be flexible, scalable, and extensible, making it a powerful tool for developers working with zero-knowledge proofs and other cryptographic operations. Whether you're working with open-source CPU backends or closed-source CUDA backends, ICICLE provides the tools and flexibility needed to achieve high performance and scalability in cryptographic computations. + +Explore the following sections to learn more about building your own backend, using ICICLE across multiple devices, and integrating it into your projects. diff --git a/docs/docs/icicle/benchmarks.md b/docs/docs/icicle/benchmarks.md new file mode 100644 index 000000000..4a9ee6a08 --- /dev/null +++ b/docs/docs/icicle/benchmarks.md @@ -0,0 +1,3 @@ +# Benchmarks + +TODO \ No newline at end of file diff --git a/docs/docs/icicle/build_from_source.md b/docs/docs/icicle/build_from_source.md new file mode 100644 index 000000000..8e0f3fcbf --- /dev/null +++ b/docs/docs/icicle/build_from_source.md @@ -0,0 +1,180 @@ + +# Build ICICLE from source + +This guide will help you get started with building, testing, and installing ICICLE, whether you're using C++, Rust, or Go. It also covers installation of the CUDA backend and important build options. + +## Building and Testing ICICLE frontend + +### C++: Build, Test, and Install (Frontend) + +ICICLE can be built and tested in C++ using CMake. The build process is straightforward, but there are several flags you can use to customize the build for your needs. + +#### Build Commands + +1. **Clone the ICICLE repository:** + ```bash + git clone https://github.com/ingonyama-zk/icicle.git + cd icicle + ``` + +2. **Configure the build:** + ```bash + mkdir -p build && rm -rf build/* + cmake -S icicle -B build -DFIELD=babybear + ``` + +:::info +To specify the field, use the flag -DFIELD=field, where field can be one of the following: babybear, stark252, m31. + +To specify a curve, use the flag -DCURVE=curve, where curve can be one of the following: bn254, bls12_377, bls12_381, bw6_761, grumpkin. +::: + +:::tip +If you have access to cuda backend repo, it can be built along ICICLE frontend by adding the following to the cmake command +- `-DCUDA_BACKEND=local` # if you have it locally +- `-DCUDA_BACKEND=` # to pull CUDA backend, given you have access +::: + +3. **Build the project:** + ```bash + cmake --build build -j + ``` + This is building the [libicicle_device](./libraries.md#icicle-device) and the [libicicle_field_babybear](./libraries.md#icicle-core) frontend lib that correspond to the field or curve. + +4. **Link:** +Link you application (or library) to ICICLE: +```cmake +target_link_libraries(yourApp PRIVATE icicle_field_babybear icicle_device) +``` + +5. **Installation (optional):** +To install the libs, specify the install prefix in the [cmake command](./build_from_source.md#build-commands) +`-DCMAKE_INSTALL_PREFIX=/install/dir/`. Default install path on linux is `/usr/local` if not specified. For other systems it may differ. The cmake command will print it to the log +``` +-- CMAKE_INSTALL_PREFIX=/install/dir/for/cmake/install +``` +Then after building, use cmake to install the libraries: +``` +cmake -S icicle -B build -DFIELD=babybear -DCMAKE_INSTALL_PREFIX=/path/to/install/dir/ +cmake --build build -j # build +cmake --install build # install icicle to /path/to/install/dir/ +``` + +6. **Run tests (optional):** +Add `-DBUILD_TESTS=ON` to the [cmake command](./build_from_source.md#build-commands) and build. +Execute all tests +```bash +cmake -S icicle -B build -DFIELD=babybear -DBUILD_TESTS=ON +cmake --build build -j +cd build/tests +ctest +``` +or choose the test-suite +```bash +./build/tests/test_field_api # or another test suite +# can specify tests using regex. For example for tests with ntt in the name: +./build/tests/test_field_api --gtest_filter="*ntt*" +``` +:::note +Most tests assume a cuda backend exists and will fail otherwise if cannot find a CUDA device. +::: + +#### Build Flags + +You can customize your ICICLE build with the following flags: + +- `-DCPU_BACKEND=ON/OFF`: Enable or disable built-in CPU backend. `default=ON`. +- `-DCMAKE_INSTALL_PREFIX=/install/dir`: Specify install directory. `default=/usr/local`. +- `-DBUILD_TESTS=ON/OFF`: Enable or disable tests. `default=OFF`. +- `-DBUILD_BENCHMARKS=ON/OFF`: Enable or disable benchmarks. `default=OFF`. + +#### Features + +By default, all [features](./libraries.md#supported-curves-and-operations) are enabled. +This is since installed backends may implement and register all APIs. Missing APIs in the frontend would cause linkage to fail due to missing symbols. Therefore by default we include them in the frontend part too. + +To disable features, add the following to the cmake command. +- ntt: `-DNTT=OFF` +- msm: `-DMSM=OFF` +- g2 msm: `-DG2=OFF` +- ecntt: `-DECNTT=OFF` +- extension field: `-DEXT_FIELD=OFF` + +:::tip +Disabling features is useful when developing with a backend that is slow to compile (e.g. CUDA backend); +::: + +### Rust: Build, Test, and Install + +To build and test ICICLE in Rust, follow these steps: + +1. **Navigate to the Rust bindings directory:** +```bash +cd wrappers/rust # or go to a specific field/curve 'cd wrappers/rust/icicle-fields/icicle-babybear' +``` + +2. **Build the Rust project:** +```bash +cargo build --release +``` +By default, all [supported features are enabled](#features). +Cargo features are used to disable features, rather than enable them, for the reason explained [here](#features): +- `no_g2` to disable G2 MSM +- `no_ecntt` to disable ECNTT + +They can be disabled as follows: +```bash +cargo build --release --features=no_ecntt,no_g2 +``` + +:::note +If you have access to cuda backend repo, it can be built along ICICLE frontend by using the following cargo features: +- `cuda_backend` : if the cuda backend resides in `icicle/backend/cuda` +- `pull_cuda_backend` : to pull main branch and build it +::: + + +3. **Run tests:** +```bash +cargo test # optional: --features=no_ecntt,no_g2,cuda_backend +``` +:::note +Most tests assume a CUDA backend is installed and fail otherwise. +::: + +4. **Install the library:** + +By default, the libraries are installed to the `target//deps/icicle` dir. If you want them installed elsewhere, define the env variable: +```bash +export ICICLE_INSTALL_DIR=/path/to/install/dir +``` + +#### Use as cargo dependency +In cargo.toml, specify the ICICLE libs to use: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here if need additional fields/curves +``` + +Can specify `branch = ` or `tag = ` or `rev = `. + +To disable features: +```bash +icicle-bls12-377 = { git = "https://github.com/ingonyama-zk/icicle.git", features = ["no_g2"] } +``` + +As explained above, the libs will be built and installed to `target//deps/icicle` so you can easily link to them. Alternatively you can set `ICICLE_INSTALL_DIR` env variable for a custom install directory. + +:::warning +Make sure to install icicle libs when installing a library/application that depends on icicle such that it is located at runtime. +::: + +### Go: Build, Test, and Install (TODO) + +## Install cuda backend + +[Install CUDA Backend (and License)](./install_cuda_backend.md#installation) \ No newline at end of file diff --git a/docs/docs/icicle/build_your_own_backend.md b/docs/docs/icicle/build_your_own_backend.md new file mode 100644 index 000000000..5cb2fc52a --- /dev/null +++ b/docs/docs/icicle/build_your_own_backend.md @@ -0,0 +1,3 @@ +# Build Your Own Backend + +TODO \ No newline at end of file diff --git a/docs/docs/icicle/core.md b/docs/docs/icicle/core.md deleted file mode 100644 index e887bae3b..000000000 --- a/docs/docs/icicle/core.md +++ /dev/null @@ -1,196 +0,0 @@ -# ICICLE Core - -ICICLE Core is a library written in C++/CUDA. All the ICICLE primitives are implemented within ICICLE Core. - -The Core is split into logical modules that can be compiled into static libraries using different [strategies](#compilation-strategies). You can then [link](#linking) these libraries with your C++ project or write your own [bindings](#writing-new-bindings-for-icicle) for other programming languages. If you want to use ICICLE with existing bindings please refer to the [Rust](/icicle/rust-bindings) or [Golang](/icicle/golang-bindings) bindings documentation. - -## Supported curves, fields and operations - -### Supported curves and operations - -| Operation\Curve | [bn254](https://neuromancer.sk/std/bn/bn254) | [bls12-377](https://neuromancer.sk/std/bls/BLS12-377) | [bls12-381](https://neuromancer.sk/std/bls/BLS12-381) | [bw6-761](https://eprint.iacr.org/2020/351) | grumpkin | -| --- | :---: | :---: | :---: | :---: | :---: | -| [MSM][MSM_DOCS] | ✅ | ✅ | ✅ | ✅ | ✅ | -| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | -| [NTT][NTT_DOCS] | ✅ | ✅ | ✅ | ✅ | ❌ | -| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | -| [VecOps][VECOPS_CODE] | ✅ | ✅ | ✅ | ✅ | ✅ | -| [Polynomials][POLY_DOCS] | ✅ | ✅ | ✅ | ✅ | ❌ | -| [Poseidon](primitives/poseidon) | ✅ | ✅ | ✅ | ✅ | ✅ | -| [Merkle Tree](primitives/poseidon#the-tree-builder) | ✅ | ✅ | ✅ | ✅ | ✅ | - -### Supported fields and operations - -| Operation\Field | [babybear](https://eprint.iacr.org/2023/824.pdf) | [Stark252](https://docs.starknet.io/documentation/architecture_and_concepts/Cryptography/p-value/) | -| --- | :---: | :---: | -| [VecOps][VECOPS_CODE] | ✅ | ✅ | -| [Polynomials][POLY_DOCS] | ✅ | ✅ | -| [NTT][NTT_DOCS] | ✅ | ✅ | -| Extension Field | ✅ | ❌ | - -### Supported hashes - -| Hash | Sizes | -| --- | :---: | -| Keccak | 256, 512 | - -## Compilation strategies - -Most of the codebase is curve/field agnostic, which means it can be compiled for different curves and fields. When you build ICICLE Core you choose a single curve or field. If you need multiple curves or fields, you compile ICICLE once per curve or field that is needed. It's that simple. Currently, the following choices are supported: - -- [Field mode][COMPILE_FIELD_MODE] - used for STARK fields like BabyBear / Mersenne / Goldilocks. Includes field arithmetic, NTT, Poseidon, Extension fields and other primitives. -- [Curve mode][COMPILE_CURVE_MODE] - used for SNARK curves like BN254 / BLS curves / Grumpkin / etc. Curve mode is built upon field mode, so it includes everything that field does It also includes curve operations / MSM / ECNTT / G2 and other curve-related primitives. - -:::info - -If you only want to use a curve's scalar or base field, you still need to use curve mode. You can disable MSM with [options](#compilation-options) - -::: - -### Compiling for a field - -You can compile ICICLE for a field using this command: - -```sh -cd icicle -mkdir -p build -cmake -DFIELD= -S . -B build -cmake --build build -j -``` - -This command will output `libingo_field_.a` into `build/lib`. - -### Compiling for a curve - -:::note - -Field related primitives will be compiled for the scalar field of the curve - -::: - -You can compile ICICLE for a SNARK curve using this command: - -```sh -cd icicle -mkdir -p build -cmake -DCURVE= -S . -B build -cmake --build build -j -``` - -Where `` can be one of `bn254`/`bls12_377`/`bls12_381`/`bw6_761`/`grumpkin`. - -This command will output both `libingo_curve_.a` and `libingo_field_.a` into `build/lib`. - -### Compilation options - -There exist multiple options that allow you to customize your build or enable additional functionality. - -#### EXT_FIELD - -Used only in [field mode][COMPILE_FIELD_MODE] to add an Extension field. Adds all supported field operations for the extension field. - -Default: `OFF` - -Usage: `-DEXT_FIELD=ON` - -#### G2 - -Used only in [curve mode][COMPILE_CURVE_MODE] to add G2 definitions. Also adds G2 MSM. - -Default: `OFF` - -Usage: `-DG2=ON` - -#### ECNTT - -Used only in [curve mode][COMPILE_CURVE_MODE] to add ECNTT function. - -Default: `OFF` - -Usage: `-DECNTT=ON` - -#### MSM - -Used only in [curve mode][COMPILE_CURVE_MODE] to add MSM function. As MSM takes a lot of time to build, you can disable it with this option to reduce compilation time. - -Default: `ON` - -Usage: `-DMSM=OFF` - -#### BUILD_HASH - -Can be used in any mode to build a hash library. Currently it only includes Keccak hash function, but more are coming. - -Default: `OFF` - -Usage: `-DBUILD_HASH=ON` - -#### BUILD_TESTS - -Can be used in any mode to include tests runner binary. - -Default: `OFF` - -USAGE: `-DBUILD_TESTS=ON` - -#### BUILD_BENCHMARKS - -Can be used in any mode to include benchmarks runner binary. - -Default: `OFF` - -USAGE: `-DBUILD_BENCHMARKS=ON` - -#### DEVMODE - -Can be used in any mode to include debug symbols in the build. - -Default: `OFF` - -USAGE: `-DEVMODE=ON` - -## Linking - -To link ICICLE with your project you first need to compile ICICLE with options of your choice. After that you can use CMake `target_link_libraries` to link with the generated static libraries and `target_include_directories` to include ICICLE headers (located in `icicle/include`). - -Refer to our [c++ examples](https://github.com/ingonyama-zk/icicle/tree/main/examples/c%2B%2B) for more info. Take a look at this [CMakeLists.txt](https://github.com/ingonyama-zk/icicle/blob/main/examples/c%2B%2B/msm/CMakeLists.txt#L22) - -## Writing new bindings for ICICLE - -Since ICICLE Core is written in CUDA / C++ its really simple to generate static libraries. These static libraries can be installed on any system and called by higher level languages such as Golang. - -Static libraries can be loaded into memory once and used by multiple programs, reducing memory usage and potentially improving performance. They also allow you to separate functionality into distinct modules so your static library may need to compile only specific features that you want to use. - -Let's review the [Golang bindings][GOLANG_BINDINGS] since its a pretty verbose example (compared to rust which hides it pretty well) of using static libraries. Golang has a library named `CGO` which can be used to link static libraries. Here's a basic example on how you can use cgo to link these libraries: - -```go -/* -#cgo LDFLAGS: -L/path/to/shared/libs -lbn254 -lbls12_381 -lbls12_377 -lbw6_671 -#include "icicle.h" // make sure you use the correct header file(s) -*/ -import "C" - -func main() { - // Now you can call the C functions from the ICICLE libraries. - // Note that C function calls are prefixed with 'C.' in Go code. - - out := (*C.BN254_projective_t)(unsafe.Pointer(p)) - in := (*C.BN254_affine_t)(unsafe.Pointer(affine)) - - C.projective_from_affine_bn254(out, in) -} -``` - -The comments on the first line tell `CGO` which libraries to import as well as which header files to include. You can then call methods which are part of the static library and defined in the header file, `C.projective_from_affine_bn254` is an example. - -If you wish to create your own bindings for a language of your choice we suggest you start by investigating how you can call static libraries. - - -[GOLANG_BINDINGS]: golang-bindings.md -[COMPILE_CURVE_MODE]: #compiling-for-a-curve -[COMPILE_FIELD_MODE]: #compiling-for-a-field -[NTT_DOCS]: primitives/ntt -[MSM_DOCS]: primitives/msm -[POLY_DOCS]: polynomials/overview -[VECOPS_CODE]: https://github.com/ingonyama-zk/icicle/blob/main/icicle/include/vec_ops/vec_ops.cuh - diff --git a/docs/docs/icicle/faq_and_troubleshooting.md b/docs/docs/icicle/faq_and_troubleshooting.md new file mode 100644 index 000000000..79d07f5e6 --- /dev/null +++ b/docs/docs/icicle/faq_and_troubleshooting.md @@ -0,0 +1,9 @@ +# FAQ and troubleshooting + +## Frequently asked questions + +TODO + +## Troubleshooting + +TODO \ No newline at end of file diff --git a/docs/docs/icicle/getting_started.md b/docs/docs/icicle/getting_started.md new file mode 100644 index 000000000..4476ef08b --- /dev/null +++ b/docs/docs/icicle/getting_started.md @@ -0,0 +1,170 @@ +# Getting started Guide + +## Overview + +This guide will walk you through the entire process of building, testing, and installing ICICLE using your preferred programming language—C++, Rust, or Go. Whether you're deploying on a CPU or leveraging CUDA for accelerated performance, this guide provides comprehensive instructions to get you started. It also outlines the typical workflow for a user, including key installation steps: + + +1. **Install ICICLE or build it from source**: This is explained in this guide. For building from source, refer to the [Build from Source page](./build_from_source.md). +2. **Follow the [Programmer’s Guide](./programmers_guide/general.md)**: Learn how to use ICICLE APIs. +3. **Start using ICICLE APIs on your CPU**: Your application will now use ICICLE on the CPU. +4. **Accelerate your application on a GPU**: [install the CUDA backend](./install_cuda_backend.md), load it, and select it in your application ([C++](./programmers_guide/cpp.md#loading-a-backend),[Rust](./programmers_guide/rust.md#loading-a-backend), [Go](./programmers_guide/go.md#loading-a-backend)). +5. **Run on the GPU**: Once the GPU backend is selected, all subsequent API calls will execute on the GPU. +6. **Optimize for multi-GPU environments**: Refer to the [Multi-GPU](./multi-device.md) Guide to fully utilize your system’s capabilities. +7. **Review memory management**: Revisit the [Memory Management section](./programmers_guide/general.md#device-abstraction) to allocate memory on the device efficiently and try to keep data on the GPU as long as possible. + + +The rest of this page details the content of a release, how to install it, and how to use it. ICICLE binaries are released for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, RHEL 8, and RHEL 9. + +:::note +Future releases will also include support for macOS and other systems. +::: + +## Content of a Release + +Each ICICLE release includes a tar file named `icicle30-.tar.gz`, where `icicle30` indicates version 3.0. This tar file contains ICICLE frontend build artifacts and headers for a specific distribution. The tar file structure includes: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the Icicle library from C++. +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core Icicle libraries are located in this directory. Applications linking to Icicle will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend (not included in this tar). + +- **CUDA backend** comes as separate tar `icicle30--cuda122.tar.gz` + - per distribution, for icicle-frontend V3.0 and CUDA 12.2. + +## installing and using icicle + +- [Full C++ example](https://github.com/ingonyama-zk/icicle/tree/yshekel/V3/examples/c++/install-and-use-icicle) +- [Full Rust example](https://github.com/ingonyama-zk/icicle/tree/yshekel/V3/examples/rust/install-and-use-icicle) +- [Full Go example](https://github.com/ingonyama-zk/icicle/tree/yshekel/V3/examples/golang/install-and-use-icicle) + +*(TODO update links to main branch when merged) + +1. **Extract and install the Tar Files**: + - [Download](https://github.com/ingonyama-zk/icicle/releases) the appropriate tar files for your distribution (Ubuntu 20.04, Ubuntu 22.04, or UBI 8,9 for RHEL compatible binaries). + - **Frontend libs and headers** should be installed in default search paths (such as `/usr/lib` and `usr/local/include`) for the compiler and linker to find. + - **Backend libs** should be installed in `/opt` + - Extract it to your desired location: + ```bash + # install the frontend part (Can skip for Rust) + tar xzvf icicle30-ubuntu22.tar.gz + cp -r ./icicle/lib/* /usr/lib/ + cp -r ./icicle/include/icicle/ /usr/local/include/ # copy C++ headers + # extract CUDA backend (OPTIONAL) + tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt + ``` + + :::note + Installing the frontend is optional for Rust. Rust does not use it. + ::: + + :::tip + You may install to any directory, but you need to ensure it can be found by the linker at compile and runtime. + You can install anywhere and use a symlink to ensure it can be easily found as if it were in the default directory. + ::: + +2. **Linking Your Application**: + + Applications need to link to the ICICLE device library and to every field and/or curve library. The backend libraries are dynamically loaded at runtime, so there is no need to link to them. + + **C++** + - When compiling your C++ application, link against the ICICLE libraries: + ```bash + g++ -o myapp myapp.cpp -licicle_device -licicle_field_bn254 -licicle_curve_bn254 + # if not installed in standard dirs, for example /custom/path/, need to specify it + g++ -o myapp myapp.cpp -I/custom/path/icicle/include -L/custom/path/icicle/lib -licicle_device -licicle_field_bn254 -licicle_curve_bn254 -Wl,-rpath,/custom/path/icicle/lib/ + ``` + + - Or via cmake + ```bash + # Add the executable + add_executable(example example.cpp) + # Link the libraries + target_link_libraries(example icicle_device icicle_field_bn254 icicle_curve_bn254) + + # OPTIONAL (if not installed in default location) + + # The following is setting compile and runtime paths for headers and libs assuming + # - headers in /custom/path/icicle/include + # - libs in/custom/path/icicle/lib + + # Include directories + target_include_directories(example PUBLIC /custom/path/icicle/include) + # Library directories + target_link_directories(example PUBLIC /custom/path/icicle/lib/) + # Set the RPATH so linker finds icicle libs at runtime + set_target_properties(example PROPERTIES + BUILD_RPATH /custom/path/icicle/lib/ + INSTALL_RPATH /custom/path/icicle/lib/) + ``` + + :::tip + If you face linkage issues, try `ldd myapp` to see the runtime dependencies. If ICICLE libs are not found, you need to add the install directory to the search path of the linker. In a development environment, you can do that using the environment variable export `LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/custom/path/icicle/lib` or similar (for non-Linux). For deployment, make sure it can be found and avoid using LD_LIBRARY_PATH. + + Alternatively, you can embed the search path in the app as an rpath by adding `-Wl,-rpath,/custom/path/icicle/lib/`. This is demonstrated above. + ::: + + **Rust** + - When building the ICICLE crates, ICICLE frontend libs are built from source, along with the Rust bindings. They are installed to `target//deps/icicle`, and Cargo will link them correctly. Note that you still need to install the CUDA backend if you have a CUDA GPU. + - Simply use `cargo build` or `cargo run` and it should link to icicle libs. + + **Go** - TODO + +:::warning +When deploying an application (whether in C++, Rust, or Go), you must make sure to either deploy the ICICLE libs (that you download or build from source) along with the application binaries (as tar, Docker image, package manager installer, or otherwise) or make sure to install ICICLE (and the backend) on the target machine. Otherwise, the target machine will have linkage issues. +::: + +## Backend Loading + +The ICICLE library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, ICICLE will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +:::warning +If building ICICLE frontend from source, make sure to load a backend that is compatible with the frontend version. CUDA backend libs are forward compatible with newer frontends (e.g., CUDA-backend-3.0 works with ICICLE-3.2). The opposite is not guaranteed. +::: + +If you install in a custom dir, make sure to set `ICICLE_BACKEND_INSTALL_DIR`: +```bash +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ myapp # for an executable myapp +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ cargo run # when using cargo +``` + +Then to load backend from ICICLE_BACKEND_INSTALL_DIR or `/opt/icicle/lib/backend` in your application: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend_from_env_or_default(); +``` +**Rust** +```rust +pub fn load_backend_from_env_or_default() -> Result<(), eIcicleError>; +``` +**Go** +```go +TODO +``` + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +**Rust** +```rust + pub fn load_backend(path: &str) -> Result<(), eIcicleError>; // OR + pub fn load_backend_non_recursive(path: &str) -> Result<(), eIcicleError>; +``` +- **`path`**: The directory where the backend libraries are located. + +**Go** +```go +TODO +``` diff --git a/docs/docs/icicle/golang-bindings.md b/docs/docs/icicle/golang-bindings.md index c2122b97f..ea2647c45 100644 --- a/docs/docs/icicle/golang-bindings.md +++ b/docs/docs/icicle/golang-bindings.md @@ -7,57 +7,60 @@ The Golang bindings are comprised of multiple packages. [`core`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/core) which defines all shared methods and structures, such as configuration structures, or memory slices. -[`cuda-runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/cuda_runtime) which defines abstractions for CUDA methods for allocating memory, initializing and managing streams, and `DeviceContext` which enables users to define and keep track of devices. +[`runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/runtime) which defines abstractions for ICICLE methods for allocating memory, initializing and managing streams, and `Device` which enables users to define and keep track of devices. -Each supported curve, field, and hash has its own package which you can find in the respective directories [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). If your project uses BN254 you only need to import that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). +Each supported curve and field has its own package which you can find in the respective directories [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). If your project uses BN254 you only need to import that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). ## Using ICICLE Golang bindings in your project To add ICICLE to your `go.mod` file. ```bash -go get github.com/ingonyama-zk/icicle +go get github.com/ingonyama-zk/icicle/v3 ``` If you want to specify a specific branch ```bash -go get github.com/ingonyama-zk/icicle@ +go get github.com/ingonyama-zk/icicle/v3@ ``` For a specific commit ```bash -go get github.com/ingonyama-zk/icicle@ +go get github.com/ingonyama-zk/icicle/v3@ ``` -To build the shared libraries you can run [this](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/build.sh) script: +### Building from source + +To build the shared libraries you can run [this](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/build.sh) script inside the downloaded go dependency: ```sh -./build.sh [-curve=] [-field=] [-hash=] [-cuda_version=] [-g2] [-ecntt] [-devmode] +./build.sh [-curve=] [-field=] [-cuda_version=] [-skip_msm] [-skip_ntt] [-skip_g2] [-skip_ecntt] [-skip_fieldext] curve - The name of the curve to build or "all" to build all supported curves field - The name of the field to build or "all" to build all supported fields -hash - The name of the hash to build or "all" to build all supported hashes --g2 - Optional - build with G2 enabled --ecntt - Optional - build with ECNTT enabled --devmode - Optional - build in devmode +-skip_msm - Optional - build with MSM disabled +-skip_ntt - Optional - build with NTT disabled +-skip_g2 - Optional - build with G2 disabled +-skip_ecntt - Optional - build with ECNTT disabled +-skip_fieldext - Optional - build without field extension -help - Optional - Displays usage information ``` :::note -If more than one curve or more than one field or more than one hash is supplied, the last one supplied will be built +If more than one curve or more than one field is supplied, the last one supplied will be built ::: -To build ICICLE libraries for all supported curves with G2 and ECNTT enabled. +To build ICICLE libraries for all supported curves without certain features, you can use their -skip_ flags. For example, for disabling G2 and ECNTT: ```bash -./build.sh -curve=all -g2 -ecntt +./build.sh -curve=all -skip_g2 -skip_ecntt ``` -If you wish to build for a specific curve, for example bn254, without G2 or ECNTT enabled. +By default, all features are enabled. To build for a specific field or curve, you can pass the `-field=` or `-curve=` flags: ``` bash ./build.sh -curve=bn254 @@ -67,15 +70,24 @@ Now you can import ICICLE into your project ```go import ( - "github.com/stretchr/testify/assert" - "testing" - - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) ... ``` +### Building with precompiled libs + +Download the frontend release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases), for example: icicle30-ubuntu22.tar.gz for ICICLE v3 on ubuntu 22.04 + +Extract the libs and move them to the downloaded go dependency in your GOMODCACHE + +```sh +# extract frontend part +tar xzvf icicle30-ubuntu22.tar.gz +cp -r ./icicle/lib/* $(go env GOMODCACHE)/github.com/ingonyama-zk/icicle/v3@/build/lib/ +``` + ## Running tests To run all tests, for all curves: @@ -84,23 +96,23 @@ To run all tests, for all curves: go test ./... -count=1 ``` -If you wish to run test for a specific curve: +If you wish to run test for a specific curve or field: ```bash -go test -count=1 +go test -count=1 ``` ## How do Golang bindings work? -The libraries produced from the CUDA code compilation are used to bind Golang to ICICLE's CUDA code. +The golang packages are binded to the libraries produced from compiling ICICLE using cgo. -1. These libraries (named `libingo_curve_.a` and `libingo_field_.a`) can be imported in your Go project to leverage the GPU accelerated functionalities provided by ICICLE. +1. These libraries (named `libicicle_curve_.a` and `libicicle_field_.a`) can be imported in your Go project to leverage the accelerated functionalities provided by ICICLE. 2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: ```go /* -#cgo LDFLAGS: -L/path/to/shared/libs -lingo_curve_bn254 -L$/path/to/shared/libs -lingo_field_bn254 -lstdc++ -lm +#cgo LDFLAGS: -L/path/to/shared/libs -licicle_device -lstdc++ -lm -Wl,-rpath=/path/to/shared/libs #include "icicle.h" // make sure you use the correct header file(s) */ import "C" @@ -118,19 +130,19 @@ Replace `/path/to/shared/libs` with the actual path where the shared libraries a ### Supported curves and operations | Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | -| --- | :---: | :---: | :---: | :---: | :---: | -| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | -| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | -| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | -| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | -| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | -| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | +| --------------- | :---: | :-------: | :-------: | :-----: | :------: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | ### Supported fields and operations | Operation\Field | babybear | -| --- | :---: | -| VecOps | ✅ | -| Polynomials | ✅ | -| NTT | ✅ | -| Extension Field | ✅ | +| --------------- | :------: | +| VecOps | ✅ | +| Polynomials | ✅ | +| NTT | ✅ | +| Extension Field | ✅ | diff --git a/docs/docs/icicle/golang-bindings/ecntt.md b/docs/docs/icicle/golang-bindings/ecntt.md index 3947995ed..4fd1acf8e 100644 --- a/docs/docs/icicle/golang-bindings/ecntt.md +++ b/docs/docs/icicle/golang-bindings/ecntt.md @@ -5,7 +5,7 @@ The `ECNtt[T any]()` function performs the Elliptic Curve Number Theoretic Transform (EC-NTT) on the input points slice, using the provided dir (direction), cfg (configuration), and stores the results in the results slice. ```go -func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) core.IcicleError +func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError ``` ### Parameters @@ -17,7 +17,7 @@ func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTC ### Return Value -- **`CudaError`**: A `core.IcicleError` value, which will be `core.IcicleErrorCode(0)` if the EC-NTT operation was successful, or an error if something went wrong. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the EC-NTT operation was successful, or an error if something went wrong. ## NTT Configuration (NTTConfig) @@ -25,29 +25,29 @@ The `NTTConfig` structure holds configuration parameters for the NTT operation, ```go type NTTConfig[T any] struct { - Ctx cr.DeviceContext - CosetGen T - BatchSize int32 - ColumnsBatch bool - Ordering Ordering - areInputsOnDevice bool - areOutputsOnDevice bool - IsAsync bool - NttAlgorithm NttAlgorithm + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler } ``` ### Fields -- **`Ctx`**: Device context containing details like device ID and stream ID. -- **`CosetGen`**: Coset generator used for coset (i)NTTs, defaulting to no coset being used. +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. - **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. -- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. - **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. - **`areInputsOnDevice`**: Indicates if input scalars are located on the device. - **`areOutputsOnDevice`**: Indicates if results are stored on the device. - **`IsAsync`**: Controls whether the NTT operation runs asynchronously. -- **`NttAlgorithm`**: Explicitly select the NTT algorithm. ECNTT supports running on `Radix2` algoruithm. +- **`Ext`**: Extended configuration for backend. ### Default Configuration @@ -63,30 +63,38 @@ func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ecntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func Main() { - // Obtain the default NTT configuration with a predefined coset generator. - cfg := GetDefaultNttConfig() - - // Define the size of the input scalars. - size := 1 << 18 - - // Generate Points for the ECNTT operation. - points := GenerateProjectivePoints(size) - - // Set the direction of the NTT (forward or inverse). - dir := core.KForward - - // Allocate memory for the results of the NTT operation. - results := make(core.HostSlice[Projective], size) - - // Perform the NTT operation. - err := ECNtt(points, dir, &cfg, results) - if err != cr.CudaSuccess { - panic("ECNTT operation failed") - } + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate Points for the ECNTT operation. + points := bn254.GenerateProjectivePoints(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.Projective], size) + + // Perform the NTT operation. + err := ecntt.ECNtt(points, dir, &cfg, results) + if err != runtime.Success { + panic("ECNTT operation failed") + } } ``` diff --git a/docs/docs/icicle/golang-bindings/keccak.md b/docs/docs/icicle/golang-bindings/keccak.md deleted file mode 100644 index 1ecc80de1..000000000 --- a/docs/docs/icicle/golang-bindings/keccak.md +++ /dev/null @@ -1,94 +0,0 @@ -# Keccak - -## Keccak Example - -```go -package main - -import ( - "encoding/hex" - - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/hash/keccak" -) - -func createHostSliceFromHexString(hexString string) core.HostSlice[uint8] { - byteArray, err := hex.DecodeString(hexString) - if err != nil { - panic("Not a hex string") - } - return core.HostSliceFromElements([]uint8(byteArray)) -} - -func main() { - input := createHostSliceFromHexString("1725b6") - outHost256 := make(core.HostSlice[uint8], 32) - - cfg := keccak.GetDefaultHashConfig() - e := keccak.Keccak256(input, int32(input.Len()), 1, outHost256, &cfg) - if e.CudaErrorCode != cr.CudaSuccess { - panic("Keccak256 hashing failed") - } - - outHost512 := make(core.HostSlice[uint8], 64) - e = keccak.Keccak512(input, int32(input.Len()), 1, outHost512, &cfg) - if e.CudaErrorCode != cr.CudaSuccess { - panic("Keccak512 hashing failed") - } - - numberOfBlocks := 3 - outHostBatch256 := make(core.HostSlice[uint8], 32*numberOfBlocks) - e = keccak.Keccak256(input, int32(input.Len()/numberOfBlocks), int32(numberOfBlocks), outHostBatch256, &cfg) - if e.CudaErrorCode != cr.CudaSuccess { - panic("Keccak256 batch hashing failed") - } -} -``` - -## Keccak Methods - -```go -func Keccak256(input core.HostOrDeviceSlice, inputBlockSize, numberOfBlocks int32, output core.HostOrDeviceSlice, config *HashConfig) core.IcicleError -func Keccak512(input core.HostOrDeviceSlice, inputBlockSize, numberOfBlocks int32, output core.HostOrDeviceSlice, config *HashConfig) core.IcicleError -``` - -### Parameters - -- **`input`**: A slice containing the input data for the Keccak256 hash function. It can reside in either host memory or device memory. -- **`inputBlockSize`**: An integer specifying the size of the input data for a single hash. -- **`numberOfBlocks`**: An integer specifying the number of results in the hash batch. -- **`output`**: A slice where the resulting hash will be stored. This slice can be in host or device memory. -- **`config`**: A pointer to a `HashConfig` object, which contains various configuration options for the Keccak256 operation. - -### Return Value - -- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the Keccak256/Keccak512 operation. - -## HashConfig - -The `HashConfig` structure holds configuration parameters for the Keccak256/Keccak512 operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. - -```go -type HashConfig struct { - Ctx cr.DeviceContext - areInputsOnDevice bool - areOutputsOnDevice bool - IsAsync bool -} -``` - -### Fields - -- **`Ctx`**: Device context containing details like device id and stream. -- **`areInputsOnDevice`**: Indicates if input data is located on the device. -- **`areOutputsOnDevice`**: Indicates if output hash is stored on the device. -- **`IsAsync`**: If true, runs the Keccak256/Keccak512 operation asynchronously. - -### Default Configuration - -Use `GetDefaultHashConfig` to obtain a default configuration, which can then be customized as needed. - -```go -func GetDefaultHashConfig() HashConfig -``` \ No newline at end of file diff --git a/docs/docs/icicle/golang-bindings/msm-pre-computation.md b/docs/docs/icicle/golang-bindings/msm-pre-computation.md index 888a01dd5..f13998d3e 100644 --- a/docs/docs/icicle/golang-bindings/msm-pre-computation.md +++ b/docs/docs/icicle/golang-bindings/msm-pre-computation.md @@ -4,9 +4,9 @@ To understand the theory behind MSM pre computation technique refer to Niall Emm ## Core package -### MSM PrecomputePoints +### MSM PrecomputeBases -`PrecomputePoints` and `G2PrecomputePoints` exists for all supported curves. +`PrecomputeBases` and `G2PrecomputeBases` exists for all supported curves. #### Description @@ -14,18 +14,17 @@ This function extends each provided base point $(P)$ with its multiples $(2^lP, The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. -#### `PrecomputePoints` +#### `PrecomputeBases` Precomputes points for MSM by extending each base point with its multiples. ```go -func PrecomputePoints(points core.HostOrDeviceSlice, msmSize int, cfg *core.MSMConfig, outputBases core.DeviceSlice) cr.CudaError +func PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError ``` ##### Parameters -- **`points`**: A slice of the original affine points to be extended with their multiples. -- **`msmSize`**: The size of a single msm in order to determine optimal parameters. +- **`bases`**: A slice of the original affine points to be extended with their multiples. - **`cfg`**: The MSM configuration parameters. - **`outputBases`**: The device slice allocated for storing the extended points. @@ -37,37 +36,43 @@ package main import ( "log" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { - cfg := bn254.GetDefaultMSMConfig() + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() points := bn254.GenerateAffinePoints(1024) - var precomputeFactor int32 = 8 + cfg.PrecomputeFactor = 8 var precomputeOut core.DeviceSlice - precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) - err := bn254.PrecomputePoints(points, 1024, &cfg, precomputeOut) - if err != cr.CudaSuccess { + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { log.Fatalf("PrecomputeBases failed: %v", err) } } ``` -#### `G2PrecomputePoints` +#### `G2PrecomputeBases` This method is the same as `PrecomputePoints` but for G2 points. Extends each G2 curve base point with its multiples for optimized MSM computations. ```go -func G2PrecomputePoints(points core.HostOrDeviceSlice, msmSize int, cfg *core.MSMConfig, outputBases core.DeviceSlice) cr.CudaError +func G2PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError ``` ##### Parameters -- **`points`**: A slice of the original affine points to be extended with their multiples. -- **`msmSize`**: The size of a single msm in order to determine optimal parameters. +- **`bases`**: A slice of the original affine points to be extended with their multiples. - **`cfg`**: The MSM configuration parameters. - **`outputBases`**: The device slice allocated for storing the extended points. @@ -79,20 +84,26 @@ package main import ( "log" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - g2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { - cfg := g2.G2GetDefaultMSMConfig() + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() points := g2.G2GenerateAffinePoints(1024) - var precomputeFactor int32 = 8 + cfg.PrecomputeFactor = 8 var precomputeOut core.DeviceSlice - precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) - err := g2.G2PrecomputePoints(points, 1024, 0, &cfg, precomputeOut) - if err != cr.CudaSuccess { + err := g2.G2PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { log.Fatalf("PrecomputeBases failed: %v", err) } } diff --git a/docs/docs/icicle/golang-bindings/msm.md b/docs/docs/icicle/golang-bindings/msm.md index 72710c551..7209eca47 100644 --- a/docs/docs/icicle/golang-bindings/msm.md +++ b/docs/docs/icicle/golang-bindings/msm.md @@ -6,13 +6,19 @@ package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" - bn254_msm "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + // Obtain the default MSM configuration. cfg := core.GetDefaultMSMConfig() @@ -24,43 +30,43 @@ func main() { points := bn254.GenerateAffinePoints(size) // Create a CUDA stream for asynchronous operations. - stream, _ := cr.CreateStream() + stream, _ := runtime.CreateStream() var p bn254.Projective // Allocate memory on the device for the result of the MSM operation. var out core.DeviceSlice - _, e := out.MallocAsync(p.Size(), p.Size(), stream) + _, e := out.MallocAsync(p.Size(), 1, stream) - if e != cr.CudaSuccess { + if e != runtime.Success { panic(e) } // Set the CUDA stream in the MSM configuration. - cfg.Ctx.Stream = &stream + cfg.StreamHandle = stream cfg.IsAsync = true // Perform the MSM operation. - e = bn254_msm.Msm(scalars, points, &cfg, out) + e = msm.Msm(scalars, points, &cfg, out) - if e != cr.CudaSuccess { + if e != runtime.Success { panic(e) } // Allocate host memory for the results and copy the results from the device. outHost := make(core.HostSlice[bn254.Projective], 1) - cr.SynchronizeStream(&stream) + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) outHost.CopyFromDevice(&out) // Free the device memory allocated for the results. out.Free() } - ``` ## MSM Method ```go -func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) cr.CudaError +func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) runtime.EIcicleError ``` ### Parameters @@ -72,7 +78,7 @@ func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *cor ### Return Value -- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the MSM operation. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. ## MSMConfig @@ -80,37 +86,37 @@ The `MSMConfig` structure holds configuration parameters for the MSM operation, ```go type MSMConfig struct { - Ctx cr.DeviceContext - PrecomputeFactor int32 - C int32 - Bitsize int32 - LargeBucketFactor int32 - batchSize int32 - areScalarsOnDevice bool - AreScalarsMontgomeryForm bool - arePointsOnDevice bool - ArePointsMontgomeryForm bool - areResultsOnDevice bool - IsBigTriangle bool - IsAsync bool + StreamHandle runtime.Stream + PrecomputeFactor int32 + C int32 + Bitsize int32 + BatchSize int32 + ArePointsSharedInBatch bool + areScalarsOnDevice bool + AreScalarsMontgomeryForm bool + areBasesOnDevice bool + AreBasesMontgomeryForm bool + areResultsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler } ``` ### Fields -- **`Ctx`**: Device context containing details like device id and stream. +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. - **`PrecomputeFactor`**: Controls the number of extra points to pre-compute. - **`C`**: Window bitsize, a key parameter in the "bucket method" for MSM. - **`Bitsize`**: Number of bits of the largest scalar. -- **`LargeBucketFactor`**: Sensitivity to frequently occurring buckets. -- **`batchSize`**: Number of results to compute in one batch. +- **`BatchSize`**: Number of results to compute in one batch. +- **`ArePointsSharedInBatch`**: Bases are shared for batch. Set to true if all MSMs use the same bases. Otherwise, the number of bases and number of scalars are expected to be equal. - **`areScalarsOnDevice`**: Indicates if scalars are located on the device. - **`AreScalarsMontgomeryForm`**: True if scalars are in Montgomery form. -- **`arePointsOnDevice`**: Indicates if points are located on the device. -- **`ArePointsMontgomeryForm`**: True if point coordinates are in Montgomery form. +- **`areBasesOnDevice`**: Indicates if bases are located on the device. +- **`AreBasesMontgomeryForm`**: True if point coordinates are in Montgomery form. - **`areResultsOnDevice`**: Indicates if results are stored on the device. -- **`IsBigTriangle`**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. - **`IsAsync`**: If true, runs MSM asynchronously. +- **`Ext`**: Extended configuration for backend. ### Default Configuration @@ -120,9 +126,9 @@ Use `GetDefaultMSMConfig` to obtain a default configuration, which can then be c func GetDefaultMSMConfig() MSMConfig ``` -## How do I toggle between the supported algorithms? +## Batched msm -When creating your MSM Config you may state which algorithm you wish to use. `cfg.Ctx.IsBigTriangle = true` will activate Large triangle reduction and `cfg.Ctx.IsBigTriangle = false` will activate iterative reduction. +For batch msm, simply allocate the results array with size corresponding to batch size and set the `ArePointsSharedInBatch` flag in config struct. ```go ... @@ -147,7 +153,7 @@ The number of results is interpreted from the size of `var out core.DeviceSlice` batchSize := 3 var p G2Projective var out core.DeviceSlice -out.Malloc(batchSize*p.Size(), p.Size()) +out.Malloc(p.Size(), batchSize) ... ``` @@ -164,7 +170,7 @@ Now you may import `g2` package of the specified curve. ```go import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" ) ``` @@ -174,25 +180,26 @@ This package include `G2Projective` and `G2Affine` points as well as a `G2Msm` m package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" - g2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { cfg := core.GetDefaultMSMConfig() - size := 1 << 12 - batchSize := 3 - totalSize := size * batchSize - scalars := bn254.GenerateScalars(totalSize) - points := g2.G2GenerateAffinePoints(totalSize) - - var p g2.G2Projective - var out core.DeviceSlice - out.Malloc(batchSize*p.Size(), p.Size()) - g2.G2Msm(scalars, points, &cfg, out) + points := bn254.GenerateAffinePoints(1024) + var precomputeFactor int32 = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(precomputeFactor)) + + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } } - ``` `G2Msm` works the same way as normal MSM, the difference is that it uses G2 Points. diff --git a/docs/docs/icicle/golang-bindings/multi-gpu.md b/docs/docs/icicle/golang-bindings/multi-gpu.md index 3d7cdf187..186c02018 100644 --- a/docs/docs/icicle/golang-bindings/multi-gpu.md +++ b/docs/docs/icicle/golang-bindings/multi-gpu.md @@ -19,48 +19,64 @@ import ( "fmt" "sync" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { - numDevices, _ := cr.GetDeviceCount() + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + + device := runtime.CreateDevice("CUDA", 0) + err := runtime.SetDevice(&device) + numDevices, _ := runtime.GetDeviceCount() fmt.Println("There are ", numDevices, " devices available") + + if err != runtime.Success { + panic(err) + } wg := sync.WaitGroup{} for i := 0; i < numDevices; i++ { + internalDevice := runtime.Device{DeviceType: device.DeviceType, Id: int32(i)} wg.Add(1) - // RunOnDevice makes sure each MSM runs on a single thread - cr.RunOnDevice(i, func(args ...any) { + runtime.RunOnDevice(&internalDevice, func(args ...any) { defer wg.Done() - cfg := bn254.GetDefaultMSMConfig() + currentDevice, err := runtime.GetActiveDevice() + if err != runtime.Success { + panic("Failed to get current device") + } + + fmt.Println("Running on ", currentDevice.GetDeviceType(), " ", currentDevice.Id, " device") + + cfg := msm.GetDefaultMSMConfig() cfg.IsAsync = true - for _, power := range []int{10, 18} { - size := 1 << power // 2^pwr - - // generate random scalars - scalars := bn254.GenerateScalars(size) - points := bn254.GenerateAffinePoints(size) - - // create a stream and allocate result pointer - stream, _ := cr.CreateStream() - var p bn254.Projective - var out core.DeviceSlice - out.MallocAsync(p.Size(), p.Size(), stream) - // assign stream to device context - cfg.Ctx.Stream = &stream - - // execute MSM - bn254.Msm(scalars, points, &cfg, out) - // read result from device - outHost := make(core.HostSlice[bn254.Projective], 1) - outHost.CopyFromDeviceAsync(&out, stream) - out.FreeAsync(stream) - - // sync the stream - cr.SynchronizeStream(&stream) + size := 1 << 10 + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + stream, _ := runtime.CreateStream() + var p bn254.Projective + var out core.DeviceSlice + _, err = out.MallocAsync(p.Size(), 1, stream) + if err != runtime.Success { + panic("Allocating bytes on device for Projective results failed") + } + cfg.StreamHandle = stream + + err = msm.Msm(scalars, points, &cfg, out) + if err != runtime.Success { + panic("Msm failed") } + outHost := make(core.HostSlice[bn254.Projective], 1) + outHost.CopyFromDeviceAsync(&out, stream) + out.FreeAsync(stream) + + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) + // Check with gnark-crypto }) } wg.Wait() @@ -71,7 +87,7 @@ This example demonstrates a basic pattern for distributing tasks across multiple ## Device Management API -To streamline device management we offer as part of `cuda_runtime` package methods for dealing with devices. +To streamline device management we offer as part of `runtime` package methods for dealing with devices. ### `RunOnDevice` @@ -87,7 +103,7 @@ While the goroutine is locked to the host thread, the Go runtime will not assign **Parameters:** -- **`deviceId int`**: The ID of the device on which to run the provided function. Device IDs start from 0. +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. - **`funcToRun func(args ...any)`**: The function to be executed on the specified device. - **`args ...any`**: Arguments to be passed to `funcToRun`. @@ -102,7 +118,8 @@ Any goroutines launched within `funcToRun` are not automatically bound to the sa **Example:** ```go -RunOnDevice(0, func(args ...any) { +device := runtime.CreateDevice("CUDA", 0) +RunOnDevice(&device, func(args ...any) { fmt.Println("This runs on GPU 0") // CUDA-related operations here will target GPU 0 }, nil) @@ -110,7 +127,7 @@ RunOnDevice(0, func(args ...any) { ### `SetDevice` -Sets the active device for the current host thread. All subsequent CUDA calls made from this thread will target the specified device. +Sets the active device for the current host thread. All subsequent calls made from this thread will target the specified device. :::warning This function should not be used directly in conjunction with goroutines. If you want to run multi-gpu scenarios with goroutines you should use [RunOnDevice](#runondevice) @@ -118,38 +135,27 @@ This function should not be used directly in conjunction with goroutines. If you **Parameters:** -- **`device int`**: The ID of the device to set as the current device. +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. **Returns:** -- **`CudaError`**: Error code indicating the success or failure of the operation. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. ### `GetDeviceCount` -Retrieves the number of CUDA-capable devices available on the host. +Retrieves the number of devices available on the host. **Returns:** -- **`(int, CudaError)`**: The number of devices and an error code indicating the success or failure of the operation. +- **`(int, EIcicleError)`**: The number of devices and an error code indicating the success or failure of the operation. -### `GetDevice` +### `GetActiveDevice` -Gets the ID of the currently active device for the calling host thread. +Gets the device of the currently active device for the calling host thread. **Returns:** -- **`(int, CudaError)`**: The ID of the current device and an error code indicating the success or failure of the operation. - -### `GetDeviceFromPointer` - -Retrieves the device associated with a given pointer. - -**Parameters:** - -- **`ptr unsafe.Pointer`**: Pointer to query. - -**Returns:** +- **`(*Device, EIcicleError)`**: The device pointer and an error code indicating the success or failure of the operation. -- **`int`**: The device ID associated with the memory pointed to by `ptr`. -This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. +This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA and other backends, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. diff --git a/docs/docs/icicle/golang-bindings/ntt.md b/docs/docs/icicle/golang-bindings/ntt.md index b51c79f9a..9a947603e 100644 --- a/docs/docs/icicle/golang-bindings/ntt.md +++ b/docs/docs/icicle/golang-bindings/ntt.md @@ -6,56 +6,66 @@ package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" - "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" + "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" ) func init() { - cfg := bn254.GetDefaultNttConfig() - initDomain(18, cfg) + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultNTTInitDomainConfig() + initDomain(18, cfg) } -func initDomain[T any](largestTestSize int, cfg core.NTTConfig[T]) core.IcicleError { - rouMont, _ := fft.Generator(uint64(1 << largestTestSize)) - rou := rouMont.Bits() - rouIcicle := bn254.ScalarField{} +func initDomain(largestTestSize int, cfg core.NTTInitDomainConfig) runtime.EIcicleError { + rouMont, _ := fft.Generator(uint64(1 << largestTestSize)) + rou := rouMont.Bits() + rouIcicle := bn254.ScalarField{} + limbs := core.ConvertUint64ArrToUint32Arr(rou[:]) - rouIcicle.FromLimbs(rou[:]) - e := bn254.InitDomain(rouIcicle, cfg.Ctx, false) - return e + rouIcicle.FromLimbs(limbs) + e := ntt.InitDomain(rouIcicle, cfg) + return e } func main() { - // Obtain the default NTT configuration with a predefined coset generator. - cfg := bn254.GetDefaultNttConfig() + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() - // Define the size of the input scalars. - size := 1 << 18 + // Define the size of the input scalars. + size := 1 << 18 - // Generate scalars for the NTT operation. - scalars := bn254.GenerateScalars(size) + // Generate scalars for the NTT operation. + scalars := bn254.GenerateScalars(size) - // Set the direction of the NTT (forward or inverse). - dir := core.KForward + // Set the direction of the NTT (forward or inverse). + dir := core.KForward - // Allocate memory for the results of the NTT operation. - results := make(core.HostSlice[bn254.ScalarField], size) + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.ScalarField], size) - // Perform the NTT operation. - err := bn254.Ntt(scalars, dir, &cfg, results) - if err.CudaErrorCode != cr.CudaSuccess { - panic("NTT operation failed") - } + // Perform the NTT operation. + err := ntt.Ntt(scalars, dir, &cfg, results) + if err != runtime.Success { + panic("NTT operation failed") + } + + ntt.ReleaseDomain() } ``` ## NTT Method ```go -func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) core.IcicleError +func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError ``` ### Parameters @@ -67,7 +77,7 @@ func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTCo ### Return Value -- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the NTT operation. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. ## NTT Configuration (NTTConfig) @@ -75,29 +85,29 @@ The `NTTConfig` structure holds configuration parameters for the NTT operation, ```go type NTTConfig[T any] struct { - Ctx cr.DeviceContext - CosetGen T - BatchSize int32 - ColumnsBatch bool - Ordering Ordering - areInputsOnDevice bool - areOutputsOnDevice bool - IsAsync bool - NttAlgorithm NttAlgorithm + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler } ``` ### Fields -- **`Ctx`**: Device context containing details like device ID and stream ID. -- **`CosetGen`**: Coset generator used for coset (i)NTTs, defaulting to no coset being used. +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. - **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. -- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. -- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`, `KMN`, `KNM`), affecting how data is arranged. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. - **`areInputsOnDevice`**: Indicates if input scalars are located on the device. - **`areOutputsOnDevice`**: Indicates if results are stored on the device. - **`IsAsync`**: Controls whether the NTT operation runs asynchronously. -- **`NttAlgorithm`**: Explicitly select the NTT algorithm. Default value: Auto (the implementation selects radix-2 or mixed-radix algorithm based on heuristics). +- **`Ext`**: Extended configuration for backend. ### Default Configuration @@ -112,7 +122,7 @@ func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] Before performing NTT operations, it's necessary to initialize the NTT domain; it only needs to be called once per GPU since the twiddles are cached. ```go -func InitDomain(primitiveRoot ScalarField, ctx cr.DeviceContext, fastTwiddles bool) core.IcicleError +func InitDomain(primitiveRoot bn254.ScalarField, cfg core.NTTInitDomainConfig) runtime.EIcicleError ``` This function initializes the domain with a given primitive root, optionally using fast twiddle factors to optimize the computation. @@ -122,30 +132,9 @@ This function initializes the domain with a given primitive root, optionally usi The `ReleaseDomain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. ```go -func ReleaseDomain(ctx cr.DeviceContext) core.IcicleError +func ReleaseDomain() runtime.EIcicleError ``` -### Parameters - -- **`ctx`**: a reference to the `DeviceContext` object, which represents the CUDA device context. - ### Return Value -The function returns a `core.IcicleError`, which represents the result of the operation. If the operation is successful, the function returns `core.IcicleErrorCode(0)`. - -### Example - -```go -import ( - "github.com/icicle-crypto/icicle-core/cr" - "github.com/icicle-crypto/icicle-core/core" -) - -func example() { - cfg := GetDefaultNttConfig() - err := ReleaseDomain(cfg.Ctx) - if err != nil { - // Handle the error - } -} -``` +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. diff --git a/docs/docs/icicle/golang-bindings/vec-ops.md b/docs/docs/icicle/golang-bindings/vec-ops.md index c39c8f2f0..e93d9a0a2 100644 --- a/docs/docs/icicle/golang-bindings/vec-ops.md +++ b/docs/docs/icicle/golang-bindings/vec-ops.md @@ -17,9 +17,10 @@ Icicle exposes a number of vector operations which a user can use: package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { @@ -30,8 +31,8 @@ func main() { cfg := core.DefaultVecOpsConfig() // Perform vector multiplication - err := bn254.VecOp(a, b, out, cfg, core.Add) - if err != cr.CudaSuccess { + err := vecOps.VecOp(a, b, out, cfg, core.Add) + if err != runtime.Success { panic("Vector addition failed") } } @@ -43,9 +44,10 @@ func main() { package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { @@ -56,8 +58,8 @@ func main() { cfg := core.DefaultVecOpsConfig() // Perform vector multiplication - err := bn254.VecOp(a, b, out, cfg, core.Sub) - if err != cr.CudaSuccess { + err := vecOps.VecOp(a, b, out, cfg, core.Sub) + if err != runtime.Success { panic("Vector subtraction failed") } } @@ -69,9 +71,10 @@ func main() { package main import ( - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" ) func main() { @@ -82,8 +85,8 @@ func main() { cfg := core.DefaultVecOpsConfig() // Perform vector multiplication - err := bn254.VecOp(a, b, out, cfg, core.Mul) - if err != cr.CudaSuccess { + err := vecOps.VecOp(a, b, out, cfg, core.Mul) + if err != runtime.Success { panic("Vector multiplication failed") } } @@ -92,7 +95,7 @@ func main() { ### VecOps Method ```go -func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret cr.CudaError) +func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret runtime.EIcicleError) ``` #### Parameters @@ -105,7 +108,7 @@ func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.V #### Return Value -- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the vector operation. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. ### VecOpsConfig @@ -113,21 +116,23 @@ The `VecOpsConfig` structure holds configuration parameters for the vector opera ```go type VecOpsConfig struct { - Ctx cr.DeviceContext - isAOnDevice bool - isBOnDevice bool - isResultOnDevice bool - IsAsync bool + StreamHandle runtime.Stream + isAOnDevice bool + isBOnDevice bool + isResultOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler } ``` #### Fields -- **Ctx**: Device context containing details like device ID and stream ID. -- **isAOnDevice**: Indicates if vector `a` is located on the device. -- **isBOnDevice**: Indicates if vector `b` is located on the device. -- **isResultOnDevice**: Specifies where the result vector should be stored (device or host memory). -- **IsAsync**: Controls whether the vector operation runs asynchronously. +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`isAOnDevice`**: Indicates if vector `a` is located on the device. +- **`isBOnDevice`**: Indicates if vector `b` is located on the device. +- **`isResultOnDevice`**: Specifies where the result vector should be stored (device or host memory). +- **`IsAsync`**: Controls whether the vector operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. #### Default Configuration @@ -146,7 +151,7 @@ The function takes a matrix represented as a 1D slice and transposes it, storing ### Function ```go -func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, ctx cr.DeviceContext, onDevice, isAsync bool) (ret core.IcicleError) +func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, config core.VecOpsConfig) runtime.EIcicleError ``` ## Parameters @@ -155,13 +160,11 @@ func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, ct - **`out`**: The output matrix is a `core.HostOrDeviceSlice`, which will be the transpose of the input matrix, stored as a 1D slice. - **`columnSize`**: The number of columns in the input matrix. - **`rowSize`**: The number of rows in the input matrix. -- **`ctx`**: The device context `cr.DeviceContext` to be used for the matrix transpose operation. -- **`onDevice`**: Indicates whether the input and output slices are stored on the device (GPU) or the host (CPU). -- **`isAsync`**: Indicates whether the matrix transpose operation should be executed asynchronously. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. ## Return Value -The function returns a `core.IcicleError` value, which represents the result of the matrix transpose operation. If the operation is successful, the returned value will be `0`. +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. ## Example Usage @@ -173,11 +176,11 @@ var output = make(core.HostSlice[ScalarField], 20) // ... // Get device context -ctx, _ := cr.GetDefaultDeviceContext() +cfg, _ := runtime.GetDefaultDeviceContext() // Transpose the matrix -err := TransposeMatrix(input, output, 5, 4, ctx, false, false) -if err.IcicleErrorCode != core.IcicleErrorCode(0) { +err := TransposeMatrix(input, output, 5, 4, cfg) +if err != runtime.Success { // Handle the error } diff --git a/docs/docs/icicle/install_cuda_backend.md b/docs/docs/icicle/install_cuda_backend.md new file mode 100644 index 000000000..32c76793d --- /dev/null +++ b/docs/docs/icicle/install_cuda_backend.md @@ -0,0 +1,30 @@ + +# CUDA Backend + +## Overview + +The CUDA backend in ICICLE V3 is a high-performance, closed-source component designed to accelerate cryptographic computations using NVIDIA GPUs. This backend includes specialized libraries optimized for various cryptographic fields and curves, providing significant speedups for operations such as MSM, NTT, and elliptic curve operations. + +## Installation + +The CUDA backend is a closed-source component that requires a license. [To install the CUDA backend, see here](./getting_started#installing-and-using-icicle). + +### Licensing + +:::note +Currently, the CUDA backend is free to use via Ingonyama’s icicle-cuda-backend-license server. By default, the CUDA backend will attempt to access this server. For more details, please contact support@ingonyama.com. +::: + +The CUDA backend requires a valid license to function. There are two types of CUDA backend licenses: + + 1. **Floating license**: In this mode, you host a license server, provided as a binary. This license supports a limited number of concurrent GPUs (N), which can be distributed across your machines as needed. N is decremented by 1 for each GPU using ICICLE per process. Once the process terminates (or crashes), the licenses are released. + 2. **Node locked license**: In this mode, the license is tied to a specific machine. The CUDA backend will accept the license only if it is used on the licensed machine. + +**To specify the license server address or file path::** + +``` +export ICICLE_LICENSE=port@ip # For license server +export ICICLE_LICENSE=/path/to/license # For node-locked license +``` + +For further assist , contact our support team for assistance support@ingonyama.com diff --git a/docs/docs/icicle/introduction.md b/docs/docs/icicle/introduction.md deleted file mode 100644 index c45985055..000000000 --- a/docs/docs/icicle/introduction.md +++ /dev/null @@ -1,247 +0,0 @@ -# Getting started with ICICLE - -This guide is oriented towards developers who want to start writing code with the ICICLE libraries. If you just want to run your existing ZK circuits on GPU refer to [this guide](./integrations.md#using-icicle-integrations) please. - -## ICICLE repository overview - -![ICICLE API overview](../../static/img/apilevels.png) - -The diagram above displays the general architecture of ICICLE and the API layers that exist. The CUDA API, which we also call ICICLE Core, is the lowest level and is comprised of CUDA kernels which implement all primitives such as MSM as well as C++ wrappers which expose these methods for different curves. - -ICICLE Core compiles into a static library. This library can be used with our official Golang and Rust wrappers or linked with your C++ project. You can also implement a wrapper for it in any other language. - -Based on this dependency architecture, the ICICLE repository has three main sections: - -- [ICICLE Core](#icicle-core) -- [ICICLE Rust bindings](#icicle-rust-and-golang-bindings) -- [ICICLE Golang bindings](#icicle-rust-and-golang-bindings) - -### ICICLE Core - -[ICICLE Core](/icicle/core) is a library that directly works with GPU by defining CUDA kernels and algorithms that invoke them. It contains code for [fast field arithmetic](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/field/field.cuh), cryptographic primitives used in ZK such as [NTT](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/ntt/), [MSM](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/msm/), [Poseidon Hash](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/poseidon/), [Polynomials](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/polynomials/) and others. - -ICICLE Core would typically be compiled into a static library and either used in a third party language such as Rust or Golang, or linked with your own C++ project. - -### ICICLE Rust and Golang bindings - -- [ICICLE Rust bindings](/icicle/rust-bindings) -- [ICICLE Golang bindings](/icicle/golang-bindings) - -These bindings allow you to easily use ICICLE in a Rust or Golang project. Setting up Golang bindings requires a bit of extra steps compared to the Rust bindings which utilize the `cargo build` tool. - -## Running ICICLE - -This guide assumes that you have a Linux or Windows machine with an Nvidia GPU installed. If you don't have access to an Nvidia GPU you can access one for free on [Google Colab](https://colab.google/). - -:::info note - -ICICLE can only run on Linux or Windows. **MacOS is not supported**. - -::: - -### Prerequisites - -- NVCC (version 12.0 or newer) -- cmake 3.18 and above -- GCC - version 9 or newer is recommended. -- Any Nvidia GPU -- Linux or Windows operating system. - -#### Optional Prerequisites - -- Docker, latest version. -- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html) - -If you don't wish to install these prerequisites you can follow this tutorial using a [ZK-Container](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile) (docker container). To learn more about using ZK-Containers [read this](../ZKContainers.md). - -### Setting up ICICLE and running tests - -The objective of this guide is to make sure you can run the ICICLE Core, Rust and Golang tests. Achieving this will ensure you know how to setup ICICLE and run an ICICLE program. For simplicity, we will be using the ICICLE docker container as our environment, however, you may install the prerequisites on your machine and [skip](#icicle-core-1) the docker section. - -#### Setting up environment with Docker - -Lets begin by cloning the ICICLE repository: - -```sh -git clone https://github.com/ingonyama-zk/icicle -``` - -We will proceed to build the docker image [found here](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile): - -```sh -docker build -t icicle-demo . -docker run -it --runtime=nvidia --gpus all --name icicle_container icicle-demo -``` - -- `-it` runs the container in interactive mode with a terminal. -- `--gpus all` Allocate all available GPUs to the container. You can also specify which GPUs to use if you don't want to allocate all. -- `--runtime=nvidia` Use the NVIDIA runtime, necessary for GPU support. - -To read more about these settings reference this [article](https://developer.nvidia.com/nvidia-container-runtime). - -If you accidentally close your terminal and want to reconnect just call: - -```sh -docker exec -it icicle_container bash -``` - -Lets make sure that we have the correct CUDA version before proceeding - -```sh -nvcc --version -``` - -You should see something like this - -```sh -nvcc: NVIDIA (R) Cuda compiler driver -Copyright (c) 2005-2023 NVIDIA Corporation -Built on Tue_Aug_15_22:02:13_PDT_2023 -Cuda compilation tools, release 12.2, V12.2.140 -Build cuda_12.2.r12.2/compiler.33191640_0 -``` - -Make sure the release version is at least 12.0. - -#### ICICLE Core - -ICICLE Core is found under [`/icicle`](https://github.com/ingonyama-zk/icicle/tree/main/icicle). To build and run the tests first: - -```sh -cd icicle -``` - -For this example, we are going to compile ICICLE for a `bn254` curve. However other compilation strategies are supported. - -```sh -mkdir -p build -cmake -S . -B build -DCURVE=bn254 -DBUILD_TESTS=ON -cmake --build build -j -``` - -`-DBUILD_TESTS` option compiles the tests, without this flag `ctest` won't work. -`-DCURVE` option tells the compiler which curve to build. You can find a list of supported curves [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/cmake/CurvesCommon.cmake#L2). - -The output in `build` folder should include the static libraries for the compiled curve. - -To run the test - -```sh -cd build/tests -ctest -``` - -#### ICICLE Rust - -The rust bindings work by first compiling the CUDA static libraries as seen [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). The compilation of CUDA and the Rust library is all handled by the rust build toolchain. - -Similar to ICICLE Core here we also have to compile per curve. - -Lets compile curve `bn254` - -```sh -cd wrappers/rust/icicle-curves/icicle-bn254 -``` - -Now lets build our library - -```sh -cargo build --release -``` - -This may take a couple of minutes since we are compiling both the CUDA and Rust code. - -To run the tests - -```sh -cargo test -``` - -We also include some benchmarks - -```sh -cargo bench -``` - -#### ICICLE Golang - -The Golang bindings require compiling ICICLE Core first. We supply a [build script](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/build.sh) to help build what you need. - -Script usage: - -```sh -./build.sh [-curve=] [-field=] [-hash=] [-cuda_version=] [-g2] [-ecntt] [-devmode] - -curve - The name of the curve to build or "all" to build all supported curves -field - The name of the field to build or "all" to build all supported fields -hash - The name of the hash to build or "all" to build all supported hashes --g2 - Optional - build with G2 enabled --ecntt - Optional - build with ECNTT enabled --devmode - Optional - build in devmode -``` - -:::note - -If more than one curve or more than one field or more than one hash is supplied, the last one supplied will be built - -::: - -Once the library has been built, you can use and test the Golang bindings. - -To test a specific curve, field or hash, change to it's directory and then run: - -```sh -go test ./tests -count=1 -failfast -timeout 60m -p 2 -v -``` - -You will be able to see each test that runs, how long it takes and whether it passed or failed - -### Running ICICLE examples - -ICICLE examples can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/examples) these examples cover some simple use cases using C++, rust and golang. - -Lets run one of our C++ examples, in this case the [MSM example](https://github.com/ingonyama-zk/icicle/blob/main/examples/c%2B%2B/msm/example.cu). - -```sh -cd examples/c++/msm -./compile.sh -./run.sh -``` - -:::tip - -Read through the compile.sh and CMakeLists.txt to understand how to link your own C++ project with ICICLE - -::: - -#### Running with Docker - -In each example directory, ZK-container files are located in a subdirectory `.devcontainer`. - -```sh -msm/ -├── .devcontainer - ├── devcontainer.json - └── Dockerfile -``` - -Now lets build our docker file and run the test inside it. Make sure you have installed the [optional prerequisites](#optional-prerequisites). - -```sh -docker build -t icicle-example-msm -f .devcontainer/Dockerfile . -``` - -Lets start and enter the container - -```sh -docker run -it --rm --gpus all -v .:/icicle-example icicle-example-msm -``` - -Inside the container you can run the same commands: - -```sh -./compile.sh -./run.sh -``` - -You can now experiment with our other examples, perhaps try to run a rust or golang example next. diff --git a/docs/docs/icicle/libraries.md b/docs/docs/icicle/libraries.md new file mode 100644 index 000000000..00c561e16 --- /dev/null +++ b/docs/docs/icicle/libraries.md @@ -0,0 +1,59 @@ +# ICICLE libraries + +ICICLE is composed of two main logical parts: +1. [**ICICLE device library**](#icicle-device) +2. [**ICICLE template core library**](#icicle-core) + +## ICICLE device + +The ICICLE device library serves as an abstraction layer for interacting with various hardware devices. It provides a comprehensive interface for tasks such as setting the active device, querying device-specific information like free and total memory, determining the number of available devices, and managing memory allocation. Additionally, it offers functionality for copying data to and from devices, managing task queues (streams) for efficient device utilization, and abstracting the complexities of device management away from the user. + +See programmers guide for more details. [C++](./programmers_guide/cpp#device-management), [Rust](./programmers_guide/rust#device-management), [Go TODO](./programmers_guide/go) + +## ICICLE Core + +ICICLE Core is a template library written in C++ that implements fundamental cryptographic operations, including field and curve arithmetic, as well as higher-level APIs such as MSM and NTT. + +The Core can be [instantiated](./build_from_source) for different fields, curves, and other cryptographic components, allowing you to tailor it to your specific needs. You can link your application to one or more ICICLE libraries, depending on the requirements of your project. For example, you might only need the babybear library or a combination of babybear and a Merkle tree builder. + + +### Rust +Each library has a corresponding crate. See [programmers guide](./programmers_guide/general.md) for more details. + +### Supported curves, fields and operations + +#### Supported curves and operations + +| Operation\Curve | [bn254](https://neuromancer.sk/std/bn/bn254) | [bls12-377](https://neuromancer.sk/std/bls/BLS12-377) | [bls12-381](https://neuromancer.sk/std/bls/BLS12-381) | [bw6-761](https://eprint.iacr.org/2020/351) | grumpkin | +| --------------------------------------------------- | :------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: | :-----------------------------------------: | :------: | +| [MSM](./primitives/msm) | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 MSM | ✅ | ✅ | ✅ | ✅ | ❌ | +| [NTT](./primitives/ntt) | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Poseidon](primitives/poseidon) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Merkle Tree](primitives/poseidon#the-tree-builder) | ✅ | ✅ | ✅ | ✅ | ✅ | + +#### Supported fields and operations + +| Operation\Field | [babybear](https://eprint.iacr.org/2023/824.pdf) | [Stark252](https://docs.starknet.io/documentation/architecture_and_concepts/Cryptography/p-value/) | +| ----------------------------------------- | :----------------------------------------------: | :------------------------------------------------------------------------------------------------: | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | +| [NTT](primitives/ntt) | ✅ | ✅ | +| Extension Field | ✅ | ❌ | + +#### Supported hashes + +| Hash | Sizes | +| ------ | :------: | +| Keccak | 256, 512 | + +## Backend +Each backend must implement the device API interface. +Each backend may implement +- One or more ICICLE library. For example implement only bn254 curve. +- One or more APIs in this library. For example MSM only. + +See [CUDA backend](./install_cuda_backend.md) and [Build Your Own Backend](./build_your_own_backend.md) for more info about implementing a backend. diff --git a/docs/docs/icicle/migrate_from_v2.md b/docs/docs/icicle/migrate_from_v2.md new file mode 100644 index 000000000..792f3b435 --- /dev/null +++ b/docs/docs/icicle/migrate_from_v2.md @@ -0,0 +1,93 @@ + +# Migration from Icicle V2 to V3 + +Icicle V3 introduces a unified interface for high-performance computing across various devices, extending the functionality that was previously limited to GPUs in Icicle V2. This guide will assist you in transitioning from Icicle V2 to V3 by highlighting the key changes and providing examples for both C++ and Rust. + +## Key Conceptual Changes + +- **Device Independence**: n V2, Icicle was primarily designed for GPU computation, directly utilizing CUDA APIs. In V3, Icicle has evolved to support a broader range of computational devices, including CPUs, GPUs, and other accelerators. As a result, CUDA APIs have been replaced with device-agnostic Icicle APIs. + +- **Unified API**: The APIs are now standardized across all devices, ensuring consistent usage and reducing the complexity of managing different hardware backends. + +:::warning +When migrating from V2 to V3, it is important to note that, by default, your code now executes on the CPU. This contrasts with V2, which was exclusively a CUDA library. For details on installing and using CUDA GPUs, refer to the [CUDA backend guide](./install_cuda_backend.md). +::: + +## Migration Guide for C++ + +### Replacing CUDA APIs with Icicle APIs + +In Icicle V3, CUDA-specific APIs have been replaced with Icicle APIs that are designed to be backend-agnostic. This allows your code to run on different devices without requiring modifications. + +- **Device Management**: Use Icicle's device management APIs instead of CUDA-specific functions. For example, instead of `cudaSetDevice()`, you would use `icicle_set_device()`. + +- **Memory Management**: Replace CUDA memory management functions such as `cudaMalloc()` and `cudaFree()` with Icicle's `icicle_malloc()` and `icicle_free()`. + +- **Stream Management**: Replace `cudaStream_t` with `icicleStreamHandle` and use Icicle's stream management functions. + +For a detailed overview and examples, please refer to the [Icicle C++ Programmer's Guide](./programmers_guide/cpp.md) for full API details. + +### Example Migration + +**V2 (CUDA-specific):** +```cpp +cudaStream_t stream; +cudaStreamCreate(&stream); +void* device_ptr; +cudaMalloc(&device_ptr, 1024); +// Perform operations using CUDA APIs +cudaStreamDestroy(stream); +cudaFree(device_ptr); +``` + +**V3 (Device-agnostic):** +```cpp +icicleStreamHandle stream; +icicle_create_stream(&stream); +void* device_ptr; +icicle_malloc(&device_ptr, 1024); +// Perform operations using Icicle APIs +icicle_destroy_stream(stream); +icicle_free(device_ptr); +``` + +## Migration Guide for Rust + +### Replacing `icicle_cuda_runtime` with `icicle_runtime` + +In Icicle V3, the `icicle_cuda_runtime` crate is replaced with the `icicle_runtime` crate. This change reflects the broader support for different devices beyond just CUDA-enabled GPUs. + +- **Device Management**: Use `icicle_runtime`'s device management functions instead of those in `icicle_cuda_runtime`. The `Device` struct remains central, but it's now part of a more generalized runtime. + +- **Memory Abstraction**: The `DeviceOrHostSlice` trait remains for memory abstraction, allowing seamless data handling between host and device. + +- **Stream Management**: Replace `CudaStream` with `IcicleStream`, which now supports broader device types. + +### Example Migration + +**V2 (`icicle_cuda_runtime`):** +```rust +use icicle_cuda_runtime::{CudaStream, DeviceVec, HostSlice}; + +let mut stream = CudaStream::create().unwrap(); +let mut device_memory = DeviceVec::cuda_malloc(1024).unwrap(); +// Perform operations using CudaStream and related APIs +stream.synchronize().unwrap(); +``` + +**V3 (`icicle_runtime`):** +```rust +use icicle_runtime::{IcicleStream, DeviceVec, HostSlice}; + +let mut stream = IcicleStream::create().unwrap(); +let mut device_memory = DeviceVec::device_malloc(1024).unwrap(); +// Perform operations using IcicleStream and related APIs +stream.synchronize().unwrap(); +``` + +### Other Considerations + +- **API Names**: While most API names remain consistent, they are now part of a more generalized runtime that can support multiple devices. Ensure that you update the crate imports and function calls accordingly. +- **Backend Loading**: Ensure that you are loading the appropriate backend using the `load_backend_from_env_or_default()` function, which is essential for setting up the runtime environment. + +For further details and examples, refer to the [Programmer's Guide](./programmers_guide/general.md). diff --git a/docs/docs/icicle/multi-device.md b/docs/docs/icicle/multi-device.md new file mode 100644 index 000000000..07f690a8d --- /dev/null +++ b/docs/docs/icicle/multi-device.md @@ -0,0 +1,79 @@ + +# Multi-Device with ICICLE + +:::info +If you are looking for the Multi-Device API documentation refer [here](./rust-bindings/multi-gpu.md) for Rust and [here](./golang-bindings/multi-gpu.md) for Golang. +::: + +One common challenge with Zero-Knowledge (ZK) computation is managing large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, which can push even the most advanced compute devices to their limits. To effectively scale and process such large circuits, leveraging multiple devices becomes a necessity. + +Multi-Device programming involves developing software to operate across multiple GPUs, CPUs, or other specialized hardware devices. Let's first explore different approaches to Multi-Device programming, then we will cover how ICICLE allows you to easily develop your ZK computations to run across many devices. + +## Approaches to Multi-Device Programming + +There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi-device systems, but they can generally be split into two categories: + +### Device Server Approach + +This approach typically involves a single or multiple CPUs managing threads that read from and write to multiple devices, such as GPUs, CPUs, or accelerators. You can think of it as a scaled-up HOST-Device model. + +![Multi-Device Server Approach](image.png) + +This approach doesn't necessarily allow for tackling larger computation sizes, but it does enable the simultaneous computation of tasks that wouldn't fit on a single device. + +For example, if you needed to compute two MSMs of size 2^26 on a GPU with 16GB of VRAM, you would normally have to perform these tasks asynchronously. However, by doubling the number of GPUs or other devices in your system, you can now run these computations in parallel. + +### Inter-Device Approach + +This more sophisticated approach involves leveraging technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/), NVLink, or other interconnect technologies to combine multiple devices and split a computation across different hardware. + +This approach requires redesigning the algorithm at the software level to be compatible with splitting tasks among devices. In some cases, to minimize latency, special inter-device connections can be installed on a server to allow direct communication between multiple devices. + +## Writing ICICLE Code for Multi-Device Setups + +Currently, ICICLE adopts a Device Server approach, where we assume you have a machine with multiple devices (GPUs, CPUs, etc.) and wish to run computations on each device. + +Each thread needs to set a device. Following api calls (including memory management and compute apis) will execute on that device, for this thread. + +### C++ +```cpp +// Set the device to CPU +Device device = "CPU"; +icicle_set_device(device); +``` +### Rust +```rust +// Set the device to the first CUDA GPU (GPU-0) +let device = icicle_runtime::Device::new("CUDA", 0 /* =device_id*/); +icicle_runtime::set_device(&device).unwrap(); +``` + +### Go (TODO) + +## Best Practices + +- **Avoid Hardcoding Device IDs:** If you want your software to take full advantage of all available devices on a machine, use methods such as `icicle_get_device_count()` to support an arbitrary number of devices. +```cpp +/** + * @brief Checks if the specified device is available. + * + * @param dev The device to check for availability. + * @return eIcicleError Status of the device availability check. + * - `SUCCESS` if the device is available. + * - `INVALID_DEVICE` if the device is not available. + */ +extern "C" eIcicleError icicle_is_device_available(const Device& dev); + + /** + * @brief Get number of available devices active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_get_device_count(int& device_count /*OUT*/); +``` + +- **Launch One CPU Thread per Device:** To avoid [common errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and ensure code readability, we recommend creating a dedicated thread for each device. Within each CPU thread, you can launch as many tasks as you like for a device, as long as they all run on the same device ID. This will make your code more manageable, easier to read, and performant. +--- + +This guide provides an overview of multi-device support in ICICLE, explaining the approaches and best practices for efficiently scaling ZK computations across multiple devices. For further information or support, please refer to our [documentation](./) or join the discussion on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/docs/icicle/multi-gpu.md b/docs/docs/icicle/multi-gpu.md deleted file mode 100644 index 4a31051ce..000000000 --- a/docs/docs/icicle/multi-gpu.md +++ /dev/null @@ -1,61 +0,0 @@ -# Multi GPU with ICICLE - -:::info - -If you are looking for the Multi GPU API documentation refer [here](./rust-bindings/multi-gpu.md) for Rust and [here](./golang-bindings/multi-gpu.md) for Golang. - -::: - -One common challenge with Zero-Knowledge computation is managing the large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, pushing the capabilities of even advanced GPUs to their limits. To effectively scale and process such large circuits, leveraging multiple GPUs in tandem becomes a necessity. - -Multi-GPU programming involves developing software to operate across multiple GPU devices. Lets first explore different approaches to Multi-GPU programming then we will cover how ICICLE allows you to easily develop youR ZK computations to run across many GPUs. - -## Approaches to Multi GPU programming - -There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi GPU, however, it can be split into two categories. - -### GPU Server approach - -This approach usually involves a single or multiple CPUs opening threads to read / write from multiple GPUs. You can think about it as a scaled up HOST - Device model. - -![alt text](image.png) - -This approach won't let us tackle larger computation sizes but it will allow us to compute multiple computations which we wouldn't be able to load onto a single GPU. - -For example let's say that you had to compute two MSMs of size 2^26 on a 16GB VRAM GPU you would normally have to perform them asynchronously. However, if you double the number of GPUs in your system you can now run them in parallel. - -### Inter GPU approach - -This approach involves a more sophisticated approach to multi GPU computation. Using technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/) and NVLink it's possible to combine multiple GPUs and split a computation among different devices. - -This approach requires redesigning the algorithm at the software level to be compatible with splitting amongst devices. In some cases, to lower latency to a minimum, special inter GPU connections would be installed on a server to allow direct communication between multiple GPUs. - -## Writing ICICLE Code for Multi GPUs - -The approach we have taken for the moment is a GPU Server approach; we assume you have a machine with multiple GPUs and you wish to run some computation on each GPU. - -To dive deeper and learn about the API check out the docs for our different ICICLE API - -- [Rust Multi GPU APIs](./rust-bindings/multi-gpu.md) -- [Golang Multi GPU APIs](./golang-bindings/multi-gpu.md) -- C++ Multi GPU APIs - -## Best practices - -- Never hardcode device IDs, if you want your software to take advantage of all GPUs on a machine use methods such as `get_device_count` to support arbitrary number of GPUs. - -- Launch one CPU thread per GPU. To avoid [nasty errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and hard to read code we suggest that for every GPU you create a dedicated thread. Within a CPU thread you should be able to launch as many tasks as you wish for a GPU as long as they all run on the same GPU id. This will make your code way more manageable, easy to read and performant. - -## ZKContainer support for multi GPUs - -Multi GPU support should work with ZK-Containers by simply defining which devices the docker container should interact with: - -```sh -docker run -it --gpus '"device=0,2"' zk-container-image -``` - -If you wish to expose all GPUs - -```sh -docker run --gpus all zk-container-image -``` diff --git a/docs/docs/icicle/overview.md b/docs/docs/icicle/overview.md index 2a7f9e847..49fb67ecc 100644 --- a/docs/docs/icicle/overview.md +++ b/docs/docs/icicle/overview.md @@ -1,58 +1,77 @@ -# What is ICICLE? +--- +slug: /icicle/overview +title: ICICLE Overview +--- + +# ICICLE Overview + +## What is ICICLE? [![GitHub Release](https://img.shields.io/github/v/release/ingonyama-zk/icicle)](https://github.com/ingonyama-zk/icicle/releases) -[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library designed to accelerate zero-knowledge proofs (ZKPs) using multiple compute backends, including GPUs, CPUs, and potentially other platforms. ICICLE's key strength lies in its ability to implement blazing-fast cryptographic primitives, enabling developers to significantly reduce proving times with minimal effort. + +## Key Features -ICICLE allows developers with minimal GPU experience to effortlessly accelerate their ZK application; from our experiments, even the most naive implementation may yield 10X improvement in proving times. +- **Acceleration of “zk” Math:** ICICLE provides optimized implementations for cryptographic primitives crucial to zero-knowledge proofs, such as elliptic curve operations, MSM, NTT, Poseidon hash, and more. +- **Set of Libraries:** ICICLE includes a comprehensive set of libraries supporting various fields, curves, and other cryptographic needs. +- **Cross-Language Support:** Available bindings for C++, Rust, Go, and potentially Python make ICICLE accessible across different development environments. +- **Backend Agnosticism:** Develop on CPU and deploy on various backends, including GPUs, specialized hardware, and other emerging platforms, depending on your project's needs. +- **Extensibility:** Designed for easy integration and extension, allowing you to build and deploy custom backends and cryptographic primitives. -ICICLE has been used by many leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark) and others to accelerate their ZK proving pipeline. +## Evolution from V2 to V3 -## Dont have access to a GPU? +Originally, ICICLE was focused solely on GPU acceleration. With the release of V3, ICICLE now supports multiple backends, making it more versatile and adaptable to different hardware environments. Whether you're leveraging the power of GPUs or exploring other compute platforms, ICICLE V3 is designed to fit your needs. -We understand that not all developers have access to a GPU and we don't want this to limit anyone from developing with ICICLE. -Here are some ways we can help you gain access to GPUs: +## Who Uses ICICLE? -:::note +ICICLE has been successfully integrated and used by leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark), and others to enhance their ZK proving pipelines. -If none of the following options suit your needs, contact us on [telegram](https://t.me/RealElan) for assistance. We're committed to ensuring that a lack of a GPU doesn't become a bottleneck for you. If you need help with setup or any other issues, we're here to help you. +## Don't Have Access to a GPU? -::: +We understand that not all developers have access to GPUs, but this shouldn't limit your ability to develop with ICICLE. Here are some ways to gain access to GPUs. ### Grants -At Ingonyama we are interested in accelerating the progress of ZK and cryptography. If you are an engineer, developer or an academic researcher we invite you to checkout [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We will give you access to GPUs and even pay you to do your dream research! +At Ingonyama, we are committed to accelerating progress in ZK and cryptography. If you're an engineer, developer, or academic researcher, we invite you to check out [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We can provide access to GPUs and even fund your research. ### Google Colab -This is a great way to get started with ICICLE instantly. Google Colab offers free GPU access to a NVIDIA T4 instance with 16 GB of memory which should be enough for experimenting and even prototyping with ICICLE. +Google Colab is a great platform to start experimenting with ICICLE instantly. It offers free access to NVIDIA T4 GPUs, which are more than sufficient for experimenting and prototyping with ICICLE. -For an extensive guide on how to setup Google Colab with ICICLE refer to [this article](./colab-instructions.md). +For a detailed guide on setting up Google Colab with ICICLE, refer to [this article](./colab-instructions.md). ### Vast.ai -[Vast.ai](https://vast.ai/) is a global GPU marketplace where you can rent many different types of GPUs by the hour for [competitive pricing](https://vast.ai/pricing). They provide on-demand and interruptible rentals depending on your need or use case; you can learn more about their rental types [here](https://vast.ai/faq#rental-types). +[Vast.ai](https://vast.ai/) offers a global GPU marketplace where you can rent different types of GPUs by the hour at competitive prices. Whether you need on-demand or interruptible rentals, Vast.ai provides flexibility for various use cases. Learn more about their rental options [here](https://vast.ai/faq#rental-types). + +## What Can You Do with ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) can be used similarly to any other cryptography library. Through various integrations, ICICLE has proven effective in multiple use cases: + +### Circuit Developers -## What can you do with ICICLE? +If you're a circuit developer facing bottlenecks, integrating ICICLE into your prover may solve performance issues. ICICLE is integrated into popular ZK provers like [Gnark](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2), enabling immediate GPU acceleration without additional code changes. -[ICICLE](https://github.com/ingonyama-zk/icicle) can be used in the same way you would use any other cryptography library. While developing and integrating ICICLE into many proof systems, we found some use case categories: +### Integrating into Existing ZK Provers -### Circuit developers +ICICLE allows for selective acceleration of specific parts of your ZK prover, helping to address specific bottlenecks without requiring a complete rewrite of your prover. -If you are a circuit developer and are experiencing bottlenecks while running your circuits, an ICICLE integrated prover may be the solution. +### Developing Your Own ZK Provers -ICICLE has been integrated into a number of popular ZK provers including [Gnark prover](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2). This means that you can enjoy GPU acceleration for your existing circuits immediately without writing a single line of code by simply switching on the GPU prover flag! +For those building ZK provers from the ground up, ICICLE is an ideal tool for creating optimized and scalable provers. The ability to scale across multiple machines within a data center is a key advantage when using ICICLE with GPUs. -### Integrating into existing ZK provers +### Developing Proof of Concepts -From our collaborations we have learned that its possible to accelerate a specific part of your prover to solve for a specific bottleneck. +ICICLE is also well-suited for prototyping and developing small-scale projects. With bindings for Golang and Rust, you can easily create a library implementing specific cryptographic primitives, such as a KZG commitment library, using ICICLE. -ICICLE can be used to accelerate specific parts of your prover without completely rewriting your ZK prover. +--- -### Developing your own ZK provers +## Get Started with ICICLE -If your goal is to build a ZK prover from the ground up, ICICLE is an ideal tool for creating a highly optimized and scalable ZK prover. A key benefit of using GPUs with ICICLE is the ability to scale your ZK prover efficiently across multiple machines within a data center. +Explore the full capabilities of ICICLE by diving into the [Architecture](./arch_overview.md), [Getting Started Guide](./getting_started.md) and the [programmers guide](./programmers_guide/general.md) to learn how to integrate, deploy, and extend ICICLE across different backends. -### Developing proof of concepts +If you have any questions or need support, feel free to reach out on [Discord] or [GitHub](https://github.com/ingonyama-zk). We're here to help you accelerate your ZK development with ICICLE. -ICICLE is also ideal for developing small prototypes. ICICLE has Golang and Rust bindings so you can easily develop a library implementing a specific primitive using ICICLE. An example would be develop a KZG commitment library using ICICLE. +[ICICLE-OVERVIEW]: ./icicle/overview.md +[Discord]: https://discord.gg/6vYrE7waPj \ No newline at end of file diff --git a/docs/docs/icicle/polynomials/overview.md b/docs/docs/icicle/polynomials/overview.md index 06058ffe4..881eeef88 100644 --- a/docs/docs/icicle/polynomials/overview.md +++ b/docs/docs/icicle/polynomials/overview.md @@ -55,20 +55,8 @@ The Polynomial class encapsulates a polynomial, providing a variety of operation This section outlines how to use the Polynomial API in C++. Bindings for Rust and Go are detailed under the Bindings sections. -### Backend Initialization - -Initialization with an appropriate factory is required to configure the computational context and backend. - -```cpp -#include "polynomials/polynomials.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" - -// Initialize with a CUDA backend -Polynomial::initialize(std::make_shared()); -``` - :::note -Initialization of a factory must be done per linked curve or field. +Make sure to set an ICICLE device prior to using the polynomial API. ::: ### Construction @@ -336,7 +324,7 @@ The Polynomial API includes comprehensive support for multi-GPU environments, a Like other components of the icicle framework, the Polynomial API allows explicit setting of the current CUDA device: ```cpp -cudaSetDevice(int deviceID); +icicle_set_device(devA); ``` This function sets the active CUDA device. All subsequent operations that allocate or deal with polynomial data will be performed on this device. @@ -347,10 +335,10 @@ Polynomials are always allocated on the current CUDA device at the time of their ```cpp // Set the device before creating polynomials -cudaSetDevice(0); +icicle_set_device(devA); Polynomial p1 = Polynomial::from_coefficients(coeffs, size); -cudaSetDevice(1); +icicle_set_device(devB); Polynomial p2 = Polynomial::from_coefficients(coeffs, size); ``` @@ -360,7 +348,7 @@ When performing operations that result in the creation of new polynomials (such ```cpp // Ensure both operands are on the same device -cudaSetDevice(0); +icicle_set_device(devA); auto p3 = p1 + p2; // Throws an exception if p1 and p2 are not on the same device ``` diff --git a/docs/docs/icicle/primitives/Icicle_Release_README.md b/docs/docs/icicle/primitives/Icicle_Release_README.md new file mode 100644 index 000000000..4d3a76e89 --- /dev/null +++ b/docs/docs/icicle/primitives/Icicle_Release_README.md @@ -0,0 +1,91 @@ + +# Icicle Release README + +## Overview + +Icicle is a powerful C++ library designed to provide flexible and efficient computation through its modular backend architecture. This README explains how to build and release Icicle for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, and CentOS 7. It also describes the content of a release and how to use the generated tar files. + +## Content of a Release + +Each Icicle release includes a tar file containing the build artifacts for a specific distribution. The tar file includes the following structure: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the Icicle library from C++. + +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core Icicle libraries are located in this directory. Applications linking to Icicle will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend. While the CUDA backend is included, it will only be used on machines with a GPU. On machines without a GPU, the CUDA backend is not utilized. + +### Considerations + +Currently, the CUDA backend is included in every installation tar file, even on machines without a GPU. This ensures consistency across installations but results in additional files being installed that may not be used. + +**TODO**: Consider splitting the release into two separate tar files—one with the CUDA backend and one without—depending on the target machine’s hardware capabilities. + +## Build Docker Image + +To build the Docker images for each distribution and CUDA version, use the following commands: + +```bash +# Ubuntu 22.04, CUDA 12.2 +docker build -t icicle-release-ubuntu22-cuda122 -f Dockerfile.ubuntu22 . + +# Ubuntu 20.04, CUDA 12.2 +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 . + +# CentOS 7, CUDA 12.2 +docker build -t icicle-release-centos7-cuda122 -f Dockerfile.centos7 . +``` + +### Docker Environment Explanation + +The Docker images you build represent the target environment for the release. Each Docker image is tailored to a specific distribution and CUDA version. You first build the Docker image, which sets up the environment, and then use this Docker image to build the release tar file. This ensures that the build process is consistent and reproducible across different environments. + +## Build Libraries Inside the Docker + +To build the Icicle libraries inside a Docker container and output the tar file to the `release_output` directory: + +```bash +mkdir -p release_output +docker run --rm --gpus all -v ./icicle:/icicle -v ./release_output:/output -v ./scripts:/scripts icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh +``` + +This command executes the `build_release_and_tar.sh` script inside the Docker container, which provides the build environment. It maps the source code and output directory to the container, ensuring the generated tar file is available on the host system. + +You can replace `icicle-release-ubuntu22-cuda122` with another Docker image tag to build in the corresponding environment (e.g., Ubuntu 20.04 or CentOS 7). + +## Installing and Using the Release + +1. **Extract the Tar File**: + - Download the appropriate tar file for your distribution (Ubuntu 20.04, Ubuntu 22.04, or CentOS 7). + - Extract it to your desired location: + ```bash + tar -xzvf icicle--cuda122.tar.gz -C /path/to/install/location + ``` + +2. **Linking Your Application**: + - When compiling your C++ application, link against the Icicle libraries found in `./icicle/lib/`: + ```bash + g++ -o myapp myapp.cpp -L/path/to/icicle/lib -licicle_device -licicle_field_or_curve + ``` + - Note: You only need to link to the Icicle device and field or curve libraries. The backend libraries are dynamically loaded at runtime. + +## Backend Loading + +The Icicle library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, Icicle will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` + +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +--- + diff --git a/docs/docs/icicle/primitives/keccak.md b/docs/docs/icicle/primitives/keccak.md index 37ea9904d..0d2a0dc90 100644 --- a/docs/docs/icicle/primitives/keccak.md +++ b/docs/docs/icicle/primitives/keccak.md @@ -1,5 +1,7 @@ # Keccak +TODO update for V3 + [Keccak](https://keccak.team/files/Keccak-implementation-3.2.pdf) is a cryptographic hash function designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. It was selected as the winner of the NIST hash function competition, becoming the basis for the [SHA-3 standard](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf). Keccak operates on a message input of any length and produces a fixed-size hash output. The hash function is built upon the sponge construction, which involves absorbing the input data followed by squeezing out the hash value. diff --git a/docs/docs/icicle/primitives/merkle.md b/docs/docs/icicle/primitives/merkle.md new file mode 100644 index 000000000..ab6b1badd --- /dev/null +++ b/docs/docs/icicle/primitives/merkle.md @@ -0,0 +1,3 @@ +# Merkle tree + +TODO update for V3 \ No newline at end of file diff --git a/docs/docs/icicle/primitives/msm.md b/docs/docs/icicle/primitives/msm.md index 7cb4a0abf..32c6a5de7 100644 --- a/docs/docs/icicle/primitives/msm.md +++ b/docs/docs/icicle/primitives/msm.md @@ -1,60 +1,134 @@ -# MSM - Multi scalar multiplication - -MSM stands for Multi scalar multiplication, its defined as: - - - M - S - M - ( - a - , - G - ) - = - - ∑ - - j - = - 0 - - - n - − - 1 - - - - a - j - - - G - j - - - -Where - -$G_j \in G$ - points from an Elliptic Curve group. - -$a_0, \ldots, a_n$ - Scalars - -$MSM(a, G) \in G$ - a single EC (elliptic curve) point - -In words, MSM is the sum of scalar and EC point multiplications. We can see from this definition that the core operations occurring are Modular Multiplication and Elliptic curve point addition. Its obvious that multiplication can be computed in parallel and then the products summed, making MSM inherently parallelizable. -Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. +# MSM - Multi Scalar Multiplication + +## Overview + +Multi-Scalar Multiplication (MSM) is a fundamental operation in elliptic curve cryptography and zero-knowledge proofs. It is defined as: + +$$ +MSM(a, G) = \sum_{j=0}^{n-1} a_j \cdot G_j +$$ + +Where: +- $G_j \in G$ are points from an elliptic curve group. +- $a_0, \ldots, a_n$ are scalars. +- $MSM(a, G) \in G$ is the result, a single elliptic curve point. + +MSM is inherently parallelizable, making it a critical operation for optimizing performance in cryptographic protocols like zk-SNARKs. Accelerating MSM can significantly reduce the time required for proof generation. + +Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. You can learn more about how MSMs work from this [video](https://www.youtube.com/watch?v=Bl5mQA7UL2I) and from our resource list on [Ingopedia](https://www.ingonyama.com/ingopedia/msm). -## Supported Bindings +## C++ API + +### `MSMConfig` Struct + +The `MSMConfig` struct configures the MSM operation. It allows customization of parameters like the number of precomputed points, the window bitsize (`c`), and memory management. Here's the configuration structure: + +```cpp +struct MSMConfig { + icicleStreamHandle stream; + int precompute_factor; + int c; + int bitsize; + int batch_size; + bool are_points_shared_in_batch; + bool are_scalars_on_device; + bool are_scalars_montgomery_form; + bool are_points_on_device; + bool are_points_montgomery_form; + bool are_results_on_device; + bool is_async; + ConfigExtension* ext; +}; +``` + +#### Default Configuration + +You can obtain a default `MSMConfig` using: + +```cpp + static MSMConfig default_msm_config() + { + MSMConfig config = { + nullptr, // stream + 1, // precompute_factor + 0, // c + 0, // bitsize + 1, // batch_size + true, // are_points_shared_in_batch + false, // are_scalars_on_device + false, // are_scalars_montgomery_form + false, // are_points_on_device + false, // are_points_montgomery_form + false, // are_results_on_device + false, // is_async + nullptr, // ext + }; + return config; + } +``` + +### `msm` Function + +The `msm` function computes the MSM operation: + +```cpp +template +eIcicleError msm(const S* scalars, const A* bases, int msm_size, const MSMConfig& config, P* results); +``` + +:::note +The API is template and can work with all ICICLE curves (if corresponding lib is linked), including G2 groups. +::: + +### Batched MSM + +The MSM supports batch mode - running multiple MSMs in parallel. It's always better to use the batch mode instead of running single msms in serial as long as there is enough memory available. We support running a batch of MSMs that share the same points as well as a batch of MSMs that use different points. + +Config fields `are_points_shared_in_batch` and `batch_size` are used to configure msm for batch mode. + +### G2 MSM + +for G2 MSM, use the [same msm api](#msm-function) with the G2 types. + +:::note +Supported curves have types for both G1 and G2. +::: + +### Precompution + +#### What It Does: + +- The function computes a set of additional points derived from the original base points. These precomputed points are stored and later reused during the MSM computation. +- Purpose: By precomputing and storing these points, the MSM operation can reduce the number of operations needed at runtime, which can significantly speed up the calculation. + +#### When to Use: + +- Memory vs. Speed Trade-off: Precomputation increases the memory footprint because additional points are stored, but it reduces the computational effort during MSM, making the process faster. +- Best for Repeated Use: It’s especially beneficial when the same set of base points is used multiple times in different MSM operations. + +```cpp +template +eIcicleError msm_precompute_bases(const A* input_bases, int bases_size, const MSMConfig& config, A* output_bases); +``` + +:::note +User is allocating the `output_bases` (on host or device memory) and later use it as bases when calling msm. +::: + +## Rust and Go bindings + +The Rust and Go bindings provide equivalent functionality for their respective environments. Refer to their documentation for details on usage. - [Golang](../golang-bindings/msm.md) -- [Rust](../rust-bindings//msm.md) +- [Rust](../rust-bindings/msm.md) + +## CUDA backend MSM +This section describes the CUDA msm implementation and how to customize it (optional). -## Algorithm description +### Algorithm description We follow the bucket method algorithm. The GPU implementation consists of four phases: @@ -63,67 +137,20 @@ We follow the bucket method algorithm. The GPU implementation consists of four p 3. Buckets Reduction phase - bucket results are multiplied by their corresponding bucket number and each bucket module is reduced to a small number of final results. By default, this is done by an iterative algorithm which is highly parallel. Setting `is_big_triangle` to `true` will switch this phase to the running sum algorithm described in the above YouTube talk which is much less parallel. 4. Final accumulation phase - The final results from the last phase are accumulated using the double-and-add algorithm. -## Batched MSM - -The MSM supports batch mode - running multiple MSMs in parallel. It's always better to use the batch mode instead of running single msms in serial as long as there is enough memory available. We support running a batch of MSMs that share the same points as well as a batch of MSMs that use different points. - -## MSM configuration +## Configuring CUDA msm +Use `ConfigExtension` object to pass backend specific configuration. +CUDA specific msm configuration: ```cpp - /** - * @struct MSMConfig - * Struct that encodes MSM parameters to be passed into the [MSM](@ref MSM) function. The intended use of this struct - * is to create it using [default_msm_config](@ref default_msm_config) function and then you'll hopefully only need to - * change a small number of default values for each of your MSMs. - */ - struct MSMConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream id. */ - int points_size; /**< Number of points in the MSM. If a batch of MSMs needs to be computed, this should be - * a number of different points. So, if each MSM re-uses the same set of points, this - * variable is set equal to the MSM size. And if every MSM uses a distinct set of - * points, it should be set to the product of MSM size and [batch_size](@ref - * batch_size). Default value: 0 (meaning it's equal to the MSM size). */ - int precompute_factor; /**< The number of extra points to pre-compute for each point. See the - * [precompute_msm_points](@ref precompute_msm_points) function, `precompute_factor` passed - * there needs to be equal to the one used here. Larger values decrease the - * number of computations to make, on-line memory footprint, but increase the static - * memory footprint. Default value: 1 (i.e. don't pre-compute). */ - int c; /**< \f$ c \f$ value, or "window bitsize" which is the main parameter of the "bucket - * method" that we use to solve the MSM problem. As a rule of thumb, larger value - * means more on-line memory footprint but also more parallelism and less computational - * complexity (up to a certain point). Currently pre-computation is independent of - * \f$ c \f$, however in the future value of \f$ c \f$ here and the one passed into the - * [precompute_msm_points](@ref precompute_msm_points) function will need to be identical. - * Default value: 0 (the optimal value of \f$ c \f$ is chosen automatically). */ - int bitsize; /**< Number of bits of the largest scalar. Typically equals the bitsize of scalar field, - * but if a different (better) upper bound is known, it should be reflected in this - * variable. Default value: 0 (set to the bitsize of scalar field). */ - int large_bucket_factor; /**< Variable that controls how sensitive the algorithm is to the buckets that occur - * very frequently. Useful for efficient treatment of non-uniform distributions of - * scalars and "top windows" with few bits. Can be set to 0 to disable separate - * treatment of large buckets altogether. Default value: 10. */ - int batch_size; /**< The number of MSMs to compute. Default value: 1. */ - bool are_scalars_on_device; /**< True if scalars are on device and false if they're on host. Default value: - * false. */ - bool are_scalars_montgomery_form; /**< True if scalars are in Montgomery form and false otherwise. Default value: - * true. */ - bool are_points_on_device; /**< True if points are on device and false if they're on host. Default value: false. */ - bool are_points_montgomery_form; /**< True if coordinates of points are in Montgomery form and false otherwise. - * Default value: true. */ - bool are_results_on_device; /**< True if the results should be on device and false if they should be on host. If set - * to false, `is_async` won't take effect because a synchronization is needed to - * transfer results to the host. Default value: false. */ - bool is_big_triangle; /**< Whether to do "bucket accumulation" serially. Decreases computational complexity - * but also greatly decreases parallelism, so only suitable for large batches of MSMs. - * Default value: false. */ - bool is_async; /**< Whether to run the MSM asynchronously. If set to true, the MSM function will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the MSM - * function will block the current CPU thread. */ - }; +ConfigExtension ext; +ext.set("large_bucket_factor", 15); +// use the config-extension in the msm config for the backend to see. +msm_config.ext = &ext; +// call msm +msm(..., config,...); // msm backend is reading the config-extension ``` -## Choosing optimal parameters +### Choosing optimal parameters `is_big_triangle` should be `false` in almost all cases. It might provide better results only for very small MSMs (smaller than 2^8^) with a large batch (larger than 100) but this should be tested per scenario. Large buckets exist in two cases: @@ -154,42 +181,23 @@ This gives a good approximation within 10% of the actual required memory for mos Here is a useful table showing optimal parameters for different MSMs. They are optimal for BLS12-377 curve when running on NVIDIA GeForce RTX 3090 Ti. This is the configuration used: -```cpp - msm::MSMConfig config = { - ctx, // DeviceContext - N, // points_size - precomp_factor, // precompute_factor - user_c, // c - 0, // bitsize - 10, // large_bucket_factor - batch_size, // batch_size - false, // are_scalars_on_device - false, // are_scalars_montgomery_form - true, // are_points_on_device - false, // are_points_montgomery_form - true, // are_results_on_device - false, // is_big_triangle - true // is_async - }; -``` - Here are the parameters and the results for the different cases: -| MSM size | Batch size | Precompute factor | c | Memory estimation (GB) | Actual memory (GB) | Single MSM time (ms) | -| --- | --- | --- | --- | --- | --- | --- | -| 10 | 1 | 1 | 9 | 0.00227 | 0.00277 | 9.2 | -| 10 | 1 | 23 | 11 | 0.00259 | 0.00272 | 1.76 | -| 10 | 1000 | 1 | 7 | 0.94 | 1.09 | 0.051 | -| 10 | 1000 | 23 | 11 | 2.59 | 2.74 | 0.025 | -| 15 | 1 | 1 | 11 | 0.011 | 0.019 | 9.9 | -| 15 | 1 | 16 | 16 | 0.061 | 0.065 | 2.4 | -| 15 | 100 | 1 | 11 | 1.91 | 1.92 | 0.84 | -| 15 | 100 | 19 | 14 | 6.32 | 6.61 | 0.56 | -| 18 | 1 | 1 | 14 | 0.128 | 0.128 | 14.4 | -| 18 | 1 | 15 | 17 | 0.40 | 0.42 | 5.9 | -| 22 | 1 | 1 | 17 | 1.64 | 1.65 | 68 | -| 22 | 1 | 13 | 21 | 5.67 | 5.94 | 54 | -| 24 | 1 | 1 | 18 | 6.58 | 6.61 | 232 | -| 24 | 1 | 7 | 21 | 12.4 | 13.4 | 199 | +| MSM size | Batch size | Precompute factor | c | Memory estimation (GB) | Actual memory (GB) | Single MSM time (ms) | +| -------- | ---------- | ----------------- | --- | ---------------------- | ------------------ | -------------------- | +| 10 | 1 | 1 | 9 | 0.00227 | 0.00277 | 9.2 | +| 10 | 1 | 23 | 11 | 0.00259 | 0.00272 | 1.76 | +| 10 | 1000 | 1 | 7 | 0.94 | 1.09 | 0.051 | +| 10 | 1000 | 23 | 11 | 2.59 | 2.74 | 0.025 | +| 15 | 1 | 1 | 11 | 0.011 | 0.019 | 9.9 | +| 15 | 1 | 16 | 16 | 0.061 | 0.065 | 2.4 | +| 15 | 100 | 1 | 11 | 1.91 | 1.92 | 0.84 | +| 15 | 100 | 19 | 14 | 6.32 | 6.61 | 0.56 | +| 18 | 1 | 1 | 14 | 0.128 | 0.128 | 14.4 | +| 18 | 1 | 15 | 17 | 0.40 | 0.42 | 5.9 | +| 22 | 1 | 1 | 17 | 1.64 | 1.65 | 68 | +| 22 | 1 | 13 | 21 | 5.67 | 5.94 | 54 | +| 24 | 1 | 1 | 18 | 6.58 | 6.61 | 232 | +| 24 | 1 | 7 | 21 | 12.4 | 13.4 | 199 | The optimal values can vary per GPU and per curve. It is best to try a few combinations until you get the best results for your specific case. diff --git a/docs/docs/icicle/primitives/ntt.md b/docs/docs/icicle/primitives/ntt.md index 9816b3dce..7d45ef9b1 100644 --- a/docs/docs/icicle/primitives/ntt.md +++ b/docs/docs/icicle/primitives/ntt.md @@ -1,5 +1,7 @@ # NTT - Number Theoretic Transform +## Overview + The Number Theoretic Transform (NTT) is a variant of the Fourier Transform used over finite fields, particularly those of integers modulo a prime number. NTT operates in a discrete domain and is used primarily in applications requiring modular arithmetic, such as cryptography and polynomial multiplication. NTT is defined similarly to the Discrete Fourier Transform (DFT), but instead of using complex roots of unity, it uses roots of unity within a finite field. The definition hinges on the properties of the finite field, specifically the existence of a primitive root of unity of order $N$ (where $N$ is typically a power of 2), and the modulo operation is performed with respect to a specific prime number that supports these roots. @@ -21,18 +23,7 @@ NTT is particularly useful because it enables efficient polynomial multiplicatio There exists also INTT which is the inverse operation of NTT. INTT can take as input an output sequence of integers from an NTT and reconstruct the original sequence. -## Using NTT - -### Supported Bindings - -- [Golang](../golang-bindings/ntt.md) -- [Rust](../rust-bindings/ntt.md) - -### Examples - -- [Rust API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/rust/ntt/src/main.rs#L1) - -- [C++ API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/c%2B%2B/ntt/example.cu#L1) +## C++ API ### Ordering @@ -52,25 +43,168 @@ The `Ordering` enum defines how inputs and outputs are arranged for the NTT oper Choosing an algorithm is heavily dependent on your use case. For example Cooley-Tukey will often use `kRN` and Gentleman-Sande often uses `kNR`. -### Modes +```cpp +enum class Ordering { + kNN, /**< Inputs and outputs are in natural-order. */ + kNR, /**< Inputs are in natural-order and outputs are in bit-reversed-order. */ + kRN, /**< Inputs are in bit-reversed-order and outputs are in natural-order. */ + kRR, /**< Inputs and outputs are in bit-reversed-order. */ + kNM, /**< Inputs are in natural-order and outputs are in digit-reversed-order. */ + kMN /**< Inputs are in digit-reversed-order and outputs are in natural-order. */ +}; +``` + +### `NTTConfig` Struct + +The `NTTConfig` struct configures the NTT operation. It allows customization of parameters like the batch size, column batch computation, order of inputs and outputs etc. + +```cpp + template + struct NTTConfig { + icicleStreamHandle stream; + S coset_gen; + int batch_size; + bool columns_batch; + Ordering ordering; + bool are_inputs_on_device; + bool are_outputs_on_device; + bool is_async; + ConfigExtension* ext = nullptr; + }; +``` + +#### Default configuration + +You can obtain a default `NTTConfig` using: +```cpp +template +static NTTConfig default_ntt_config() +{ + NTTConfig config = { + nullptr, // stream + S::one(), // coset_gen + 1, // batch_size + false, // columns_batch + Ordering::kNN, // ordering + false, // are_inputs_on_device + false, // are_outputs_on_device + false, // is_async + }; + return config; +} +``` + +### NTT domain +Before computing an NTT, it is mandatory to initialize the roots of unity domain for computing the NTT. + +:::note +NTT domain is constructed for a given size $2^N$ and can be used for any NTT of size smaller or equal to $2^N$. For example a domain of size 32 can be used to compute NTTs of size 2,4,8,16,32. +::: + +```cpp +template +eIcicleError ntt_init_domain(const S& primitive_root, const NTTInitDomainConfig& config); +``` + +:::note +Domain is constructed per device. When using multiple devices (e.g. GPUs), need to call it per device prior to calling ntt. +::: + +To retrieve a root of unity from the domain: +```cpp +template S get_root_of_unity(uint64_t max_size); +``` + +Finally, release the domain to free up device memory when not required: +```cpp +template S get_root_of_unity(uint64_t max_size); +``` + +where + +```cpp +struct NTTInitDomainConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_async; /**< True if operation is asynchronous. Default value is false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ +}; + +static NTTInitDomainConfig default_ntt_init_domain_config() +{ + NTTInitDomainConfig config = { + nullptr, // stream + false // is_async + }; + return config; +} +``` + +### `ntt` Function + +the `ntt` function computes the NTT operation: + +```cpp +template +eIcicleError ntt(const E* input, int size, NTTDir dir, const NTTConfig& config, E* output); + +// Where NTTDir specific whether it is a forward or inverse transform +enum class NTTDir { + kForward, /**< Perform forward NTT. */ + kInverse /**< Perform inverse NTT (iNTT). */ +}; +``` +### EC-NTT +[The ntt api](#ntt-function) works for ECNTT too, given correct types, for supported curves. + +### Batch NTT + +Batch NTT allows you to compute many NTTs with a single API call. Batch NTT can significantly reduce read/write times as well as computation overhead by executing multiple NTT operations in parallel. Batch mode may also offer better utilization of computational resources (memory and compute). + +To compute a batch, set the `batch_size` and `columns_batch` fields of the config struct. + +### Rust and Go bindings -NTT also supports two different modes `Batch NTT` and `Single NTT` +- [Golang](../golang-bindings/ntt.md) +- [Rust](../rust-bindings/ntt.md) -Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. +### Example -#### Single NTT +The following example demonstartes how to use ntt and how pass custom configurations to the CUDA backend. This is discussed below. -Single NTT will launch a single NTT computation. +```cpp +#include "icicle/backend/ntt_config.h" -Choose this mode when your application requires processing individual NTT operations in isolation. +// allocate and init input/output +int batch_size = /*...*/; +int log_ntt_size = /*...*/; +int ntt_size = 1 << log_ntt_size; +auto input = std::make_unique(batch_size * ntt_size); +auto output = std::make_unique(batch_size * ntt_size); +initialize_input(ntt_size, batch_size, input.get()); -#### Batch NTT Mode +// Initialize NTT domain with fast twiddles (CUDA backend) +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); -Batch NTT allows you to run many NTTs with a single API call. Batch NTT mode can significantly reduce read/write times as well as computation overhead by executing multiple NTT operations in parallel. Batch mode may also offer better utilization of computational resources (memory and compute). +// ntt configuration +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +config.batch_size = batch_size; -## Supported algorithms +// Compute NTT with explicit selection of Mixed-Radix algorithm. +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` -Our NTT implementation supports two algorithms `radix-2` and `mixed-radix`. +### CUDA backend NTT +This section describes the CUDA ntt implementation and how to use it. + +Our CUDA NTT implementation supports two algorithms `radix-2` and `mixed-radix`. ### Radix 2 @@ -157,3 +291,32 @@ Performance really depends on logn size, batch size, ordering, inverse, coset, c For this reason we implemented our [heuristic auto-selection](https://github.com/ingonyama-zk/icicle/blob/main/icicle/src/ntt/ntt.cu#L573) which should choose the most efficient algorithm in most cases. We still recommend you benchmark for your specific use case if you think a different configuration would yield better results. + +To Explicitly choose the algorithm: + +```cpp +#include "icicle/backend/ntt_config.h" + +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` + + +### Fast twiddles + +When using the Mixed-radix algorithm, it is recommended to initialize the domain in "fast-twiddles" mode. This is essentially allocating the domain using extra memory but enables faster ntt. +To do so simply, pass this flag to the CUDA backend. + +```cpp +#include "icicle/backend/ntt_config.h" + +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); +``` \ No newline at end of file diff --git a/docs/docs/icicle/primitives/overview.md b/docs/docs/icicle/primitives/overview.md index 67956f3e2..4fe315144 100644 --- a/docs/docs/icicle/primitives/overview.md +++ b/docs/docs/icicle/primitives/overview.md @@ -1,12 +1,11 @@ -# ICICLE Primitives - -This section of the documentation is dedicated to the ICICLE primitives, we will cover the usage and internal details of our primitives such as hashing algorithms, MSM and NTT. +# ICICLE Compute APIs +This section of the documentation is dedicated to the main APIs provided by ICICLE. We will cover the usage and internal details of our core primitives, such as Multi-Scalar Multiplication (MSM), Number Theoretic Transform (NTT), and various hashing algorithms. Each primitive has its own dedicated page with examples and explanations for C++, Rust, and Go. ## Supported primitives - - [MSM](./msm.md) - [NTT](./ntt.md) -- [Keccak Hash](./keccak.md) -- [Poseidon Hash](./poseidon.md) +- [Vector Operations](./vec_ops.md) +- [Polynomials](../polynomials/overview.md) +- Hashing and commitment schemes coming soon diff --git a/docs/docs/icicle/primitives/poseidon.md b/docs/docs/icicle/primitives/poseidon.md index 5626701b9..df2af0930 100644 --- a/docs/docs/icicle/primitives/poseidon.md +++ b/docs/docs/icicle/primitives/poseidon.md @@ -1,5 +1,7 @@ # Poseidon +TODO update for V3 + [Poseidon](https://eprint.iacr.org/2019/458.pdf) is a popular hash in the ZK ecosystem primarily because its optimized to work over large prime fields, a common setting for ZK proofs, thereby minimizing the number of multiplicative operations required. Poseidon has also been specifically designed to be efficient when implemented within ZK circuits, Poseidon uses far less constraints compared to other hash functions like Keccak or SHA-256 in the context of ZK circuits. diff --git a/docs/docs/icicle/primitives/poseidon2.md b/docs/docs/icicle/primitives/poseidon2.md index cbf20bb5e..fcb2a7e06 100644 --- a/docs/docs/icicle/primitives/poseidon2.md +++ b/docs/docs/icicle/primitives/poseidon2.md @@ -1,5 +1,7 @@ # Poseidon2 +TODO update for V3 + [Poseidon2](https://eprint.iacr.org/2023/323) is a recently released optimized version of Poseidon1. The two versions differ in two crucial points. First, Poseidon is a sponge hash function, while Poseidon2 can be either a sponge or a compression function depending on the use case. Secondly, Poseidon2 is instantiated by new and more efficient linear layers with respect to Poseidon. These changes decrease the number of multiplications in the linear layer by up to 90% and the number of constraints in Plonk circuits by up to 70%. This makes Poseidon2 currently the fastest arithmetization-oriented hash function without lookups. diff --git a/docs/docs/icicle/primitives/vec_ops.md b/docs/docs/icicle/primitives/vec_ops.md new file mode 100644 index 000000000..e9e10c1a9 --- /dev/null +++ b/docs/docs/icicle/primitives/vec_ops.md @@ -0,0 +1,187 @@ + +# Vector Operations API + +## Overview + +The Vector Operations API in Icicle provides a set of functions for performing element-wise and scalar-vector operations on vectors, matrix operations, and miscellaneous operations like bit-reversal and slicing. These operations can be performed on the host or device, with support for asynchronous execution. + +### VecOpsConfig + +The `VecOpsConfig` struct is a configuration object used to specify parameters for vector operations. + +#### Fields + +- **`stream: icicleStreamHandle`**: Specifies the CUDA stream for asynchronous execution. If `nullptr`, the default stream is used. +- **`is_a_on_device: bool`**: Indicates whether the first input vector (`a`) is already on the device. If `false`, the vector will be copied from the host to the device. +- **`is_b_on_device: bool`**: Indicates whether the second input vector (`b`) is already on the device. If `false`, the vector will be copied from the host to the device. This field is optional. +- **`is_result_on_device: bool`**: Indicates whether the result should be stored on the device. If `false`, the result will be transferred back to the host. +- **`is_async: bool`**: Specifies whether the vector operation should be performed asynchronously. When `true`, the operation will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity. +- **`ext: ConfigExtension*`**: Backend-specific extensions. + +#### Default Configuration + +```cpp +static VecOpsConfig default_vec_ops_config() { + VecOpsConfig config = { + nullptr, // stream + false, // is_a_on_device + false, // is_b_on_device + false, // is_result_on_device + false, // is_async + }; + return config; +} +``` + +### Element-wise Operations + +These functions perform element-wise operations on two input vectors `a` and `b`, producing an output vector. + +#### `vector_add` + +Adds two vectors element-wise. + +```cpp +template +eIcicleError vector_add(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_sub` + +Subtracts vector `b` from vector `a` element-wise. + +```cpp +template +eIcicleError vector_sub(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_mul` + +Multiplies two vectors element-wise. + +```cpp +template +eIcicleError vector_mul(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_div` + +Divides vector `a` by vector `b` element-wise. + +```cpp +template +eIcicleError vector_div(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_accumulate` + +Adds vector b to a, inplace. + +```cpp +template +eIcicleError vector_accumulate(T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config); +``` + +#### `convert_montogomery` + +Convert a vector of field elements to/from montgomery form. +```cpp +template +eIcicleError convert_montgomery(const T* input, uint64_t size, bool is_into, const VecOpsConfig& config, T* output); +``` + +### Scalar-Vector Operations + +These functions apply a scalar operation to each element of a vector. + +#### `scalar_add_vec / scalar_sub_vec` + +Adds a scalar to each element of a vector. + +```cpp +template +eIcicleError scalar_add_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_sub_vec` + +Subtract each element of a vector from a scalar `scalar-vec`. + +```cpp +template +eIcicleError scalar_sub_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_mul_vec` + +Multiplies each element of a vector by a scalar. + +```cpp +template +eIcicleError scalar_mul_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +### Matrix Operations + +These functions perform operations on matrices. + +#### `matrix_transpose` + +Transposes a matrix. + +```cpp +template +eIcicleError matrix_transpose(const T* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, T* mat_out); +``` + +### Miscellaneous Operations + +#### `bit_reverse` + +Reorders the vector elements based on a bit-reversal pattern. + +```cpp +template +eIcicleError bit_reverse(const T* vec_in, uint64_t size, const VecOpsConfig& config, T* vec_out); +``` + +#### `slice` + +Extracts a slice from a vector. + +```cpp +template +eIcicleError slice(const T* vec_in, uint64_t offset, uint64_t stride, uint64_t size, const VecOpsConfig& config, T* vec_out); +``` + +#### `highest_non_zero_idx` + +Finds the highest non-zero index in a vector. + +```cpp +template +eIcicleError highest_non_zero_idx(const T* vec_in, uint64_t size, const VecOpsConfig& config, int64_t* out_idx); +``` + +#### `polynomial_eval` + +Evaluates a polynomial at given domain points. + +```cpp +template +eIcicleError polynomial_eval(const T* coeffs, uint64_t coeffs_size, const T* domain, uint64_t domain_size, const VecOpsConfig& config, T* evals /*OUT*/); +``` + +#### `polynomial_division` + +Divides two polynomials. + +```cpp +template +eIcicleError polynomial_division(const T* numerator, int64_t numerator_deg, const T* denumerator, int64_t denumerator_deg, const VecOpsConfig& config, T* q_out /*OUT*/, uint64_t q_size, T* r_out /*OUT*/, uint64_t r_size); +``` + +### Rust and Go bindings + +- [Golang](../golang-bindings/vec-ops.md) +- [Rust](../rust-bindings/vec-ops.md) diff --git a/docs/docs/icicle/programmers_guide/cpp.md b/docs/docs/icicle/programmers_guide/cpp.md new file mode 100644 index 000000000..bd534da1e --- /dev/null +++ b/docs/docs/icicle/programmers_guide/cpp.md @@ -0,0 +1,300 @@ +# Icicle C++ Usage Guide + +## Overview + +This guide covers the usage of Icicle's C++ API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all icicle runtime APIs in [runtime.h](https://github.com/ingonyama-zk/icicle/blob/yshekel/V3/icicle/include/icicle/runtime.h) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```cpp +#include "icicle/runtime.h" +eIcicleError result = icicle_load_backend_from_env_or_default(); +// or load from custom install dir +eIcicleError result = icicle_load_backend("/path/to/backend/installdir", true); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```cpp +icicle::Device device = {"CUDA", 0}; // or other +eIcicleError result = icicle_set_device(device); +// or query current (thread) device +eIcicleError result = icicle_get_active_device(device); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```cpp +int device_count; +eIcicleError result = icicle_get_device_count(device_count); + +bool is_host_memory; +eIcicleError result = icicle_is_host_memory(ptr); + +bool is_device_memory; +eIcicleError result = icicle_is_active_device_memory(ptr); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```cpp +void* ptr; +eIcicleError result = icicle_malloc(&ptr, 1024); // Allocate 1024 bytes +eIcicleError result = icicle_free(ptr); // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```cpp +icicleStreamHandle stream; +eIcicleError err = icicle_create_stream(&stream); + +void* ptr; +err = icicle_malloc_async(&ptr, 1024, stream); +err = icicle_free_async(ptr, stream); +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```cpp +size_t total_memory, available_memory; +eIcicleError err = icicle_get_available_memory(total_memory, available_memory); +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```cpp +eIcicleError err = icicle_memset(ptr, 0, 1024); // Set 1024 bytes to 0 +eIcicleError err = icicle_memset_async(ptr, 0, 1024, stream); +``` + +## Data Transfer + +### Copying Data + +Data can be copied between host and device, or between devices. The location of the memory is inferred from the pointers: + +```cpp +eIcicleError result = icicle_copy(dst, src, size); +eIcicleError result = icicle_copy_async(dst, src, size, stream); +``` + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```cpp +eIcicleError result = icicle_copy_to_host(host_dst, device_src, size); +eIcicleError result = icicle_copy_to_host_async(host_dst, device_src, size, stream); + +eIcicleError result = icicle_copy_to_device(device_dst, host_src, size); +eIcicleError result = icicle_copy_to_device_async(device_dst, host_src, size, stream); +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```cpp +icicleStreamHandle stream; +eIcicleError result = icicle_create_stream(&stream); +eIcicleError result = icicle_destroy_stream(stream); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```cpp +eIcicleError result = icicle_stream_synchronize(stream); +eIcicleError result = icicle_device_synchronize(); +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```cpp +icicle::Device dev; +eIcicleError result = icicle_is_device_available(dev); +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```cpp +DeviceProperties properties; +eIcicleError result = icicle_get_device_properties(properties); + +/******************/ +// where DeviceProperties is +struct DeviceProperties { + bool using_host_memory; // Indicates if the device uses host memory + int num_memory_regions; // Number of memory regions available on the device + bool supports_pinned_memory; // Indicates if the device supports pinned memory + // Add more properties as needed +}; +``` + + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +int main() +{ + // Load installed backends + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the api call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + // Setup inputs + int msm_size = 1024; + auto scalars = std::make_unique(msm_size); + auto points = std::make_unique(msm_size); + projective_t result; + + // Generate random inputs + scalar_t::rand_host_many(scalars.get(), msm_size); + projective_t::rand_host_many(points.get(), msm_size); + + // (optional) copy scalars to device memory explicitly + scalar_t* scalars_d = nullptr; + auto err = icicle_malloc((void**)&scalars_d, sizeof(scalar_t) * msm_size); + // Note: need to test err and make sure no errors occurred + err = icicle_copy(scalars_d, scalars.get(), sizeof(scalar_t) * msm_size); + + // MSM configuration + MSMConfig config = default_msm_config(); + // tell icicle that the scalars are on device. Note that EC points and result are on host memory in this example. + config.are_scalars_on_device = true; + + // Execute the MSM kernel (on the current device) + eIcicleError result_code = msm(scalars_d, points.get(), msm_size, config, &result); + // OR call bn254_msm(scalars_d, points.get(), msm_size, config, &result); + + // Free the device memory + icicle_free(scalars_d); + + // Check for errors + if (result_code == eIcicleError::SUCCESS) { + std::cout << "MSM result: " << projective_t::to_affine(result) << std::endl; + } else { + std::cerr << "MSM computation failed with error: " << get_error_string(result_code) << std::endl; + } + + return 0; +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/polynomials/polynomials.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +// define bn254Poly to be a polynomial over the scalar field of bn254 +using bn254Poly = Polynomial; + +static bn254Poly randomize_polynomial(uint32_t size) +{ + auto coeff = std::make_unique(size); + for (int i = 0; i < size; i++) + coeff[i] = scalar_t::rand_host(); + return bn254Poly::from_rou_evaluations(coeff.get(), size); +} + +int main() +{ + // Load backend and set device + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the API call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + int poly_size = 1024; + + // build domain for ntt is required for some polynomial ops that rely on ntt + ntt_init_domain(scalar_t::omega(12), default_ntt_init_domain_config()); + + // randomize polynomials f(x),g(x) over the scalar field of bn254 + bn254Poly f = randomize_polynomial(poly_size); + bn254Poly g = randomize_polynomial(poly_size); + + // Perform polynomial multiplication + auto result = f * g; // Executes on the current device + + ICICLE_LOG_INFO << "Done"; + + return 0; +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `eIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```cpp +if (result != eIcicleError::SUCCESS) { + // Handle error +} +``` + +This guide provides an overview of the essential APIs available in Icicle for C++. The provided examples should help you get started with integrating Icicle into your high-performance computing projects. diff --git a/docs/docs/icicle/programmers_guide/general.md b/docs/docs/icicle/programmers_guide/general.md new file mode 100644 index 000000000..3d2062c56 --- /dev/null +++ b/docs/docs/icicle/programmers_guide/general.md @@ -0,0 +1,107 @@ + +# Icicle Programmer's Guide + +## Compute APIs + +Icicle offers a variety of compute APIs, including Number Theoretic Transforms (NTT), Multi Scalar Multiplication (MSM), vector operations, Elliptic Curve NTT (ECNTT), polynomials, and more. These APIs follow a consistent structure, making it straightforward to apply the same usage patterns across different operations. + +[Check out all details about compute APIs here](../primitives/overview.md). + +### Common Structure of Compute APIs + +Each compute API in Icicle typically involves the following components: + +- **Inputs and Outputs**: The data to be processed and the resulting output are passed to the API functions. These can reside either on the host (CPU) or on a device (GPU). + +- **Parameters**: Parameters such as the size of data to be processed are provided to control the computation. + +- **Configuration Struct**: A configuration struct is used to specify additional options for the computation. This struct has default values but can be customized as needed. + +The configuration struct allows users to modify settings such as: + +- Specifying whether inputs and outputs are on the host or device. +- Adjusting the data layout for specific optimizations. +- Passing custom options to the backend implementation through an extension mechanism, such as setting the number of CPU cores to use. + +### Example (C++) + +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +// optionally modify the config struct here + +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +Where `VecOpsConfig` is defined in `icicle/vec_ops.h`: + +```cpp +struct VecOpsConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_a_on_device; /**< True if `a` is on the device, false if it is not. Default value: false. */ + bool is_b_on_device; /**< True if `b` is on the device, false if it is not. Default value: false. OPTIONAL. */ + bool is_result_on_device; /**< If true, the output is preserved on the device, otherwise on the host. Default value: false. */ + bool is_async; /**< Whether to run the vector operations asynchronously. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extension. */ +}; +``` + +This pattern is consistent across most Icicle APIs, in C++/Rust/Go, providing flexibility while maintaining a familiar structure. For NTT, MSM, and other operations, include the corresponding header and call the template APIs. + +### Config struct extension + +In special cases, where an application wants to specify backend specific options, this is achieved with a config-extension struct. +For example the CPU backend has an option regarding how many threads to use for a vector addition looks as follows: +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +ConfigExtension ext; +config.ext = &ext; +ext.set("n_threads", 8); // tell the CPU backend to use 8 threads +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +:::note +This is not device-agnostic behavior, meaning such code is aware of the backend. +Having said that, it is not an error to pass options to a backend that is not aware of them. +::: + +## Device Abstraction + +Icicle provides a device abstraction layer that allows you to interact with different compute devices such as CPUs and GPUs seamlessly. The device abstraction ensures that your code can work across multiple hardware platforms without modification. + +### Device Management + +- **Loading Backends**: Backends are loaded dynamically based on the environment configuration or a specified path. +- **Setting Active Device**: The active device for a thread can be set, allowing for targeted computation on a specific device. + +## Streams + +Streams in Icicle allow for asynchronous execution and memory operations, enabling parallelism and non-blocking execution. Streams are associated with specific devices, and you can create, destroy, and synchronize streams to manage your workflow. + +:::note +For compute APIs, streams go into the `config.stream` field along with the `is_async=true` config flag. +::: + +### Memory Management + +Icicle provides functions for allocating, freeing, and managing memory across devices. Memory operations can be performed synchronously or asynchronously, depending on the use case. + +### Data Transfer + +Data transfer between the host and devices, or between different devices, is handled through a set of APIs that ensure efficient and error-checked operations. Asynchronous operations are supported to maximize performance. + +### Synchronization + +Synchronization ensures that all previous operations on a stream or device are completed. This is crucial when coordinating between multiple operations that depend on one another. + +## Additional Information + +- **Error Handling**: Icicle uses a specific error enumeration (`eIcicleError`) to handle and return error states across its APIs. +- **Device Properties**: You can query various properties of devices to tailor operations according to the hardware capabilities. diff --git a/docs/docs/icicle/programmers_guide/go.md b/docs/docs/icicle/programmers_guide/go.md new file mode 100644 index 000000000..1cd6bec11 --- /dev/null +++ b/docs/docs/icicle/programmers_guide/go.md @@ -0,0 +1,296 @@ +# ICICLE Golang Usage Guide + +## Overview + +This guide covers the usage of Icicle's Golang API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all ICICLE runtime APIs in [runtime.go](https://github.com/ingonyama-zk/icicle/blob/yshekel/V3/wrappers/golang/runtime/runtime.go) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```go +import "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + +result := runtime.LoadBackendFromEnvOrDefault() +// or load from custom install dir +result := runtime.LoadBackend("/path/to/backend/installdir", true) +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```go +device = runtime.CreateDevice("CUDA", 0) // or other +result := runtime.SetDevice(device) +// or query current (thread) device +activeDevice := runtime.GetActiveDevice() +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```go +numDevices := runtime.GetDeviceCount() + +var ptr unsafe.Pointer +isHostMemory = runtime.IsHostMemory(ptr) +isDeviceMemory = runtime.IsActiveDeviceMemory(ptr) +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```go +ptr, err := runtime.Malloc(1024) // Allocate 1024 bytes +err := runtime.Free(ptr) // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```go +stream, err := runtime.CreateStream() + +ptr, err := runtime.MallocAsync(1024, stream) +err = runtime.FreeAsync(ptr, stream) +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```go +size_t total_memory, available_memory; +availableMemory, err := runtime.GetAvailableMemory() +freeMemory := availableMemory.Free +totalMemory := availableMemory.Total +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```go +err := runtime.Memset(ptr, 0, 1024) // Set 1024 bytes to 0 +err := runtime.MemsetAsync(ptr, 0, 1024, stream) +``` + +## Data Transfer + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```go +result := runtime.CopyToHost(host_dst, device_src, size) +result := runtime.CopyToHostAsync(host_dst, device_src, size, stream) +result := runtime.CopyToDevice(device_dst, host_src, size) +result := runtime.CopyToDeviceAsync(device_dst, host_src, size, stream) +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```go +stream, err := runtime.CreateStream() +err = runtime.DestroyStream(stream) +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```go +err := runtime.StreamSynchronize(stream) +err := runtime.DeviceSynchronize() +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```go +dev := runtime.CreateDevice("CPU", 0) +isCPUAvail := runtime.IsDeviceAvailable(dev) +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```go +properties, err := runtime.GetDeviceProperties(properties); + +/******************/ +// where DeviceProperties is +type DeviceProperties struct { + UsingHostMemory bool // Indicates if the device uses host memory + NumMemoryRegions int32 // Number of memory regions available on the device + SupportsPinnedMemory bool // Indicates if the device supports pinned memory +} +``` + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) + +func main() { + + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // Setup inputs + const size = 1 << 18 + + // Generate random inputs + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // (optional) copy scalars to device memory explicitly + var scalarsDevice core.DeviceSlice + scalars.CopyToDevice(&scalarsDevice, true) + + // MSM configuration + cfgBn254 := core.GetDefaultMSMConfig() + + // allocate memory for the result + result := make(core.HostSlice[bn254.Projective], 1) + + // execute bn254 MSM on device + err := bn254Msm.Msm(scalarsDevice, points, &cfgBn254, result) + + // Check for errors + if err != runtime.Success { + errorString := fmt.Sprint( + "bn254 Msm failed: ", err) + panic(errorString) + } + + // free explicitly allocated device memory + scalarsDevice.Free() +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/polynomial" +) + +func initBabybearDomain() runtime.EIcicleError { + cfgInitDomain := core.GetDefaultNTTInitDomainConfig() + rouIcicle := babybear.ScalarField{} + rouIcicle.FromUint32(1461624142) + return ntt.InitDomain(rouIcicle, cfgInitDomain) +} + +func init() { + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // build domain for ntt is required for some polynomial ops that rely on ntt + err := initBabybearDomain() + if err != runtime.Success { + errorString := fmt.Sprint( + "Babybear Domain initialization failed: ", err) + panic(errorString) + } +} + +func main() { + + // Setup inputs + const polySize = 1 << 10 + + // randomize two polynomials over babybear field + var fBabybear polynomial.DensePolynomial + defer fBabybear.Delete() + var gBabybear polynomial.DensePolynomial + defer gBabybear.Delete() + fBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize)) + gBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize / 2)) + + // Perform polynomial multiplication + rBabybear := fBabybear.Multiply(&gBabybear) // Executes on the current device + defer rBabybear.Delete() + rDegree := rBabybear.Degree() + + fmt.Println("f Degree: ", fBabybear.Degree()) + fmt.Println("g Degree: ", gBabybear.Degree()) + fmt.Println("r Degree: ", rDegree) +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `EIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```go +if result != runtime.SUCCESS { + // Handle error +} +``` + +This guide provides an overview of the essential APIs available in Icicle for C++. The provided examples should help you get started with integrating Icicle into your high-performance computing projects. diff --git a/docs/docs/icicle/programmers_guide/rust.md b/docs/docs/icicle/programmers_guide/rust.md new file mode 100644 index 000000000..c3f89f514 --- /dev/null +++ b/docs/docs/icicle/programmers_guide/rust.md @@ -0,0 +1,247 @@ + +# Icicle Rust Usage Guide + +## Overview + +This guide covers the usage of Icicle’s Rust API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Build the Rust Application and Execute + +To successfully build and execute the Rust application using ICICLE, you need to define the ICICLE dependencies in your Cargo.toml file: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here as needed +``` + +Once the dependencies are defined, you can build and run your application using the following command: +```bash +cargo run --release +``` + +This will compile your Rust application with optimizations and execute it. + +:::note +The icicle-runtime crate is used to load backends, select a device, and interact with the device in an abstract way when managing memory, streams, and other resources, as explained in this guide. +::: + +## Device Management + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```rust +use icicle_runtime::runtime; + +runtime::load_backend_from_env_or_default().unwrap(); +// or load from custom install dir +runtime::load_backend("/path/to/backend/installdir").unwrap(); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```rust +use icicle_runtime::Device; + +let device = Device::new("CUDA", 0); // or other +icicle_runtime::set_device(&device).unwrap(); + +let active_device = icicle_runtime::get_active_device().unwrap(); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```rust +let device_count = icicle_runtime::get_device_count().unwrap(); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated on the active device using the `DeviceVec` API. This memory allocation is flexible, as it supports allocation on any device, including the CPU if the CPU backend is used. + +```rust +use icicle_runtime::memory::DeviceVec; + +// Allocate 1024 elements on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc(1024).unwrap(); +``` + +The memory is released when the `DeviceVec` object is dropped. + +### Asynchronous Memory Operations + +Asynchronous memory operations can be performed using streams. This allows for non-blocking execution, with memory allocation and deallocation occurring asynchronously. +```rust +use icicle_runtime::stream::IcicleStream; +use icicle_runtime::memory::DeviceVec; + +let mut stream = IcicleStream::create().unwrap(); // mutability is for the destroy() method + +// Allocate 1024 elements asynchronously on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc_async(1024, &stream).unwrap(); + +// dispatch additional copy, compute etc. ops to the stream + +// Synchronize the stream to ensure all operations are complete +stream.synchronize().unwrap(); +stream.destroy().unwrap(); // +``` + +:::note +Streams need be explicitly destroyed before being dropped. +::: + +### Querying Available Memory + +You can retrieve the total and available memory on the active device using the `get_available_memory` function. + +```rust +use icicle_runtime::memory::get_available_memory; + +// Retrieve total and available memory on the active device +let (total_memory, available_memory) = get_available_memory().unwrap(); + +println!("Total memory: {}", total_memory); +println!("Available memory: {}", available_memory); +``` + +This function returns a tuple containing the total memory and the currently available memory on the device. It is essential for managing and optimizing resource usage in your applications. + +## Data Transfer + +### Copying Data + +Data can be copied between the host and device, or between devices. The location of the memory is handled by the `HostOrDeviceSlice` and `DeviceSlice` traits: + +```rust +use icicle_runtime::memory::{DeviceVec, HostSlice}; + +// Copy data from host to device +let input = vec![1, 2, 3, 4]; +let mut d_mem = DeviceVec::::device_malloc(input.len()).unwrap(); +d_mem.copy_from_host(HostSlice::from_slice(&input)).unwrap(); +// OR +d_mem.copy_from_host_async(HostSlice::from_slice(&input, &stream)).unwrap(); + +// Copy data back from device to host +let mut output = vec![0; input.len()]; +d_mem.copy_to_host(HostSlice::from_mut_slice(&mut output)).unwrap(); +// OR +d_mem.copy_to_host_async(HostSlice::from_mut_slice(&mut output, &stream)).unwrap(); +``` +## Stream Management + +### Creating and Destroying Streams + +Streams in Icicle are used to manage asynchronous operations, ensuring that computations can run in parallel without blocking the CPU thread: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Create a stream +let mut stream = IcicleStream::create().unwrap(); + +// Destroy the stream +stream.destroy().unwrap(); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Synchronization ensures that all previous operations on a stream or device are completed before moving on to the next task. This is crucial when coordinating between multiple dependent operations: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Synchronize the stream +stream.synchronize().unwrap(); + +// Synchronize the device +icicle_runtime::device_synchronize().unwrap(); +``` +These functions ensure that your operations are properly ordered and completed before the program proceeds, which is critical in parallel computing environments. + +## Device Properties + +### Checking Device Availability + +Check if a specific device is available and retrieve a list of registered devices: +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + println!("CUDA device is available."); +} else { + println!("CUDA device is not available."); +} + +let registered_devices = icicle_runtime::get_registered_devices().unwrap(); +println!("Registered devices: {:?}", registered_devices); +``` + +### Querying Device Properties + +Retrieve properties of the active device to understand its capabilities and configurations: + +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device); + let device_props = icicle_runtime::get_device_properties().unwrap(); + println!("Device using host memory: {}", device_props.using_host_memory); +} +``` + +These functions allow you to query device capabilities and ensure that your application is running on the appropriate hardware. + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API in Rust. + +```rust +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device + let _ = icicle_runtime::runtime::load_backend_from_env_or_default(); + let cuda_device = Device::new("CUDA", 0); + if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device).unwrap(); + } + + let size = 1024; + + // Randomize inputs + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} +``` diff --git a/docs/docs/icicle/rust-bindings.md b/docs/docs/icicle/rust-bindings.md index f3e6142f6..bbfb16d9c 100644 --- a/docs/docs/icicle/rust-bindings.md +++ b/docs/docs/icicle/rust-bindings.md @@ -4,9 +4,9 @@ Rust bindings allow you to use ICICLE as a rust library. `icicle-core` defines all interfaces, macros and common methods. -`icicle-cuda-runtime` defines DeviceContext which can be used to manage a specific GPU as well as wrapping common CUDA methods. +`icicle-runtime` contains runtime APIs for memory management, stream management and more. -`icicle-curves` implements all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the CUDA libraries for that curve as part of the rust-toolchain build. +`icicle-curves` / `icicle-fields` implement all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the ICICLE libraries for that curve as part of the rust-toolchain build. ## Using ICICLE Rust bindings in your project @@ -14,12 +14,12 @@ Simply add the following to your `Cargo.toml`. ```toml # GPU Icicle integration -icicle-cuda-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git" } icicle-bn254 = { git = "https://github.com/ingonyama-zk/icicle.git" } ``` -`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-cuda-runtime` contain ICICLE utilities and CUDA wrappers. +`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-runtime` contain ICICLE utilities and CUDA wrappers. If you wish to point to a specific ICICLE branch add `branch = ""` or `tag = ""` to the ICICLE dependency. For a specific commit add `rev = ""`. @@ -27,61 +27,38 @@ When you build your project ICICLE will be built as part of the build command. ## How do the rust bindings work? -The rust bindings are just rust wrappers for ICICLE Core static libraries which can be compiled. We integrate the compilation of the static libraries into rusts toolchain to make usage seamless and easy. This is achieved by [extending rusts build command](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). +The rust bindings are rust crates that wrap the ICICLE Core libraries (C++). Each crate can wrap one or more ICICLE core libraries. They are built too when building the crate. -```rust -use cmake::Config; -use std::env::var; - -fn main() { - println!("cargo:rerun-if-env-changed=CXXFLAGS"); - println!("cargo:rerun-if-changed=../../../../icicle"); - - let cargo_dir = var("CARGO_MANIFEST_DIR").unwrap(); - let profile = var("PROFILE").unwrap(); - - let out_dir = Config::new("../../../../icicle") - .define("BUILD_TESTS", "OFF") //TODO: feature - .define("CURVE", "bn254") - .define("CMAKE_BUILD_TYPE", "Release") - .build_target("icicle") - .build(); - - println!("cargo:rustc-link-search={}/build", out_dir.display()); - - println!("cargo:rustc-link-lib=ingo_bn254"); - println!("cargo:rustc-link-lib=stdc++"); - // println!("cargo:rustc-link-search=native=/usr/local/cuda/lib64"); - println!("cargo:rustc-link-lib=cudart"); -} -``` +:::note +Since ICICLE V3, core libraries are shared-libraries. This means that they must be installed in a directory that can be found by the linker. In addition, installing an application that depends on ICICLE must make sure to install ICICLE or have it installed on the target machine. +::: ## Supported curves, fields and operations ### Supported curves and operations | Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | -| --- | :---: | :---: | :---: | :---: | :---: | -| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | -| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | -| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | -| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | -| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | -| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | -| Poseidon | ✅ | ✅ | ✅ | ✅ | ✅ | -| Merkle Tree | ✅ | ✅ | ✅ | ✅ | ✅ | +| --------------- | :---: | :-------: | :-------: | :-----: | :------: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | +| Poseidon | ✅ | ✅ | ✅ | ✅ | ✅ | +| Merkle Tree | ✅ | ✅ | ✅ | ✅ | ✅ | ### Supported fields and operations | Operation\Field | babybear | stark252 | -| --- | :---: | :---: | -| VecOps | ✅ | ✅ | -| Polynomials | ✅ | ✅ | -| NTT | ✅ | ✅ | -| Extension Field | ✅ | ❌ | +| --------------- | :------: | :------: | +| VecOps | ✅ | ✅ | +| Polynomials | ✅ | ✅ | +| NTT | ✅ | ✅ | +| Extension Field | ✅ | ❌ | ### Supported hashes -| Hash | Sizes | -| --- | :---: | +| Hash | Sizes | +| ------ | :------: | | Keccak | 256, 512 | diff --git a/docs/docs/icicle/rust-bindings/ecntt.md b/docs/docs/icicle/rust-bindings/ecntt.md index c2f790aef..426324a93 100644 --- a/docs/docs/icicle/rust-bindings/ecntt.md +++ b/docs/docs/icicle/rust-bindings/ecntt.md @@ -10,13 +10,7 @@ pub fn ecntt( dir: NTTDir, cfg: &NTTConfig, output: &mut (impl HostOrDeviceSlice> + ?Sized), -) -> IcicleResult<()> -where - C::ScalarField: FieldImpl, - ::Config: ECNTT, -{ - // ... function implementation ... -} +) -> Result<(), eIcicleError> ``` ## Parameters @@ -28,4 +22,4 @@ where ## Return Value -- **`IcicleResult<()>`**: This function returns an `IcicleResult` which is a wrapper type that indicates success or failure of the NTT computation. On success, it contains `Ok(())`. +- **`Result<(), eIcicleError>`**: This function returns an `eIcicleError` which is a wrapper type that indicates success or failure of the NTT computation. On success, it contains `Ok(())`. diff --git a/docs/docs/icicle/rust-bindings/keccak.md b/docs/docs/icicle/rust-bindings/keccak.md index 9c8b231c7..637ad3e63 100644 --- a/docs/docs/icicle/rust-bindings/keccak.md +++ b/docs/docs/icicle/rust-bindings/keccak.md @@ -1,5 +1,7 @@ # Keccak +TODO update for V3 + ## Keccak Example ```rust diff --git a/docs/docs/icicle/rust-bindings/msm-pre-computation.md b/docs/docs/icicle/rust-bindings/msm-pre-computation.md deleted file mode 100644 index 687704fb2..000000000 --- a/docs/docs/icicle/rust-bindings/msm-pre-computation.md +++ /dev/null @@ -1,45 +0,0 @@ -# MSM Pre computation - -To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). - -## `precompute_points` - -Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. - -```rust -pub fn precompute_points>( - points: &(impl HostOrDeviceSlice> + ?Sized), - msm_size: i32, - cfg: &MSMConfig, - output_bases: &mut DeviceSlice>, -) -> IcicleResult<()> -``` - -### Parameters - -- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. -- **`msm_size`**: The size of a single msm in order to determine optimal parameters. -- **`cfg`**: The MSM configuration parameters. -- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. - -#### Returns - -`Ok(())` if the operation is successful, or an `IcicleResult` error otherwise. - -#### Description - -This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. - -The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. - -#### Example Usage - -```rust -let cfg = MSMConfig::default(); -let precompute_factor = 4; // Number of points to precompute -let mut extended_bases = HostOrDeviceSlice::cuda_malloc(expected_size).expect("Failed to allocate memory for extended bases"); - -// Precompute the bases using the specified factor -precompute_points(&points, msm_size, &cfg, &mut extended_bases) - .expect("Failed to precompute bases"); -``` diff --git a/docs/docs/icicle/rust-bindings/msm.md b/docs/docs/icicle/rust-bindings/msm.md index 8cfccf053..e4f86444f 100644 --- a/docs/docs/icicle/rust-bindings/msm.md +++ b/docs/docs/icicle/rust-bindings/msm.md @@ -1,54 +1,14 @@ # MSM -## Example - -```rust -use icicle_bn254::curve::{CurveCfg, G1Projective, ScalarCfg}; -use icicle_core::{curve::Curve, msm, traits::GenerateRandom}; -use icicle_cuda_runtime::{memory::HostOrDeviceSlice, stream::CudaStream}; - -fn main() { - let size: usize = 1 << 10; // Define the number of points and scalars - - // Generate random points and scalars - println!("Generating random G1 points and scalars for BN254..."); - let points = CurveCfg::generate_random_affine_points(size); - let scalars = ScalarCfg::generate_random(size); - - // Wrap points and scalars in HostOrDeviceSlice for MSM - let points_host = HostOrDeviceSlice::Host(points); - let scalars_host = HostOrDeviceSlice::Host(scalars); - - // Allocate memory on the CUDA device for MSM results - let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).expect("Failed to allocate CUDA memory for MSM results"); - - // Create a CUDA stream for asynchronous execution - let stream = CudaStream::create().expect("Failed to create CUDA stream"); - let mut cfg = msm::MSMConfig::default(); - cfg.ctx.stream = &stream; - cfg.is_async = true; // Enable asynchronous execution - - // Execute MSM on the device - println!("Executing MSM on device..."); - msm::msm(&scalars_host, &points_host, &cfg, &mut msm_results).expect("Failed to execute MSM"); - - // Synchronize CUDA stream to ensure MSM execution is complete - stream.synchronize().expect("Failed to synchronize CUDA stream"); - - // Optionally, move results to host for further processing or printing - println!("MSM execution complete."); -} -``` - ## MSM API Overview ```rust -pub fn msm( - scalars: &HostOrDeviceSlice, - points: &HostOrDeviceSlice>, +pub fn msm>( + scalars: &(impl HostOrDeviceSlice + ?Sized), + bases: &(impl HostOrDeviceSlice> + ?Sized), cfg: &MSMConfig, - results: &mut HostOrDeviceSlice>, -) -> IcicleResult<()> + results: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> Result<(), eIcicleError>; ``` ### Parameters @@ -61,36 +21,33 @@ pub fn msm( ### MSM Config ```rust -pub struct MSMConfig<'a> { - pub ctx: DeviceContext<'a>, - points_size: i32, +pub struct MSMConfig { + pub stream_handle: IcicleStreamHandle, pub precompute_factor: i32, pub c: i32, - pub bitsize: i32, - pub large_bucket_factor: i32, + pub bitsize: i32, batch_size: i32, + are_points_shared_in_batch: bool, are_scalars_on_device: bool, pub are_scalars_montgomery_form: bool, are_points_on_device: bool, pub are_points_montgomery_form: bool, - are_results_on_device: bool, - pub is_big_triangle: bool, + are_results_on_device: bool, pub is_async: bool, + pub ext: ConfigExtension, } ``` -- **`ctx: DeviceContext`**: Specifies the device context, device id and the CUDA stream for asynchronous execution. -- **`point_size: i32`**: +- **`stream_handle: IcicleStreamHandle`**: Specifies a stream for asynchronous execution. - **`precompute_factor: i32`**: Determines the number of extra points to pre-compute for each point, affecting memory footprint and performance. - **`c: i32`**: The "window bitsize," a parameter controlling the computational complexity and memory footprint of the MSM operation. - **`bitsize: i32`**: The number of bits of the largest scalar, typically equal to the bit size of the scalar field. -- **`large_bucket_factor: i32`**: Adjusts the algorithm's sensitivity to frequently occurring buckets, useful for non-uniform scalar distributions. - **`batch_size: i32`**: The number of MSMs to compute in a single batch, for leveraging parallelism. - **`are_scalars_montgomery_form`**: Set to `true` if scalars are in montgomery form. - **`are_points_montgomery_form`**: Set to `true` if points are in montgomery form. - **`are_scalars_on_device: bool`**, **`are_points_on_device: bool`**, **`are_results_on_device: bool`**: Indicate whether the corresponding buffers are on the device memory. -- **`is_big_triangle`**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. - **`is_async: bool`**: Whether to perform the MSM operation asynchronously. +- **`ext: ConfigExtension`**: extended configuration for backend. ### Usage @@ -98,73 +55,63 @@ The `msm` function is designed to compute the sum of multiple scalar-point multi When performing MSM operations, it's crucial to match the size of the `scalars` and `points` arrays correctly and ensure that the `results` buffer is appropriately sized to hold the output. The `MSMConfig` should be set up to reflect the specifics of the operation, including whether the operation should be asynchronous and any device-specific settings. -## How do I toggle between the supported algorithms? - -When creating your MSM Config you may state which algorithm you wish to use. `is_big_triangle=true` will activate Large triangle reduction and `is_big_triangle=false` will activate iterative reduction. +## Example ```rust -... +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device ... -let mut cfg_bls12377 = msm::get_default_msm_config::(); + // Randomize inputs + let size = 1024; + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); -// is_big_triangle will determine which algorithm to use -cfg_bls12377.is_big_triangle = true; + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} -msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); -... ``` -You may reference the rust code [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L54). +## Batched msm -## How do I toggle between MSM modes? +For batch msm, simply allocate the results array with size corresponding to batch size and set the `are_points_shared_in_batch` flag in config struct. -Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `msm::msm` function. If you are expecting an array of `msm_results`, ICICLE will automatically split `scalars` and `points` into equal parts and run them as multiple MSMs in parallel. +## Precomputationg -```rust -... - -let mut msm_result: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); -msm::msm(&scalars, &points, &cfg, &mut msm_result).unwrap(); +Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. -... +```rust +/// Returns `Ok(())` if no errors occurred or a `eIcicleError` otherwise. +pub fn precompute_bases>( + points: &(impl HostOrDeviceSlice> + ?Sized), + config: &MSMConfig, + output_bases: &mut DeviceSlice>, +) -> Result<(), eIcicleError>; ``` -In the example above we allocate a single expected result which the MSM method will interpret as `batch_size=1` and run a single MSM. - -In the next example, we are expecting 10 results which sets `batch_size=10` and runs 10 MSMs in batch mode. - -```rust -... +### Parameters -let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(10).unwrap(); -msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); +- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. +- **`msm_size`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. -... -``` +#### Returns -Here is a [reference](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L108) to the code which automatically sets the batch size. For more MSM examples have a look [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/examples/rust/msm/src/main.rs#L1). +`Ok(())` if the operation is successful, or an `eIcicleError` error otherwise. ## Parameters for optimal performance Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) - -## Support for G2 group - -MSM also supports G2 group. - -Using MSM in G2 requires a G2 config, and of course your Points should also be G2 Points. - -```rust -... - -let scalars = HostOrDeviceSlice::Host(upper_scalars[..size].to_vec()); -let g2_points = HostOrDeviceSlice::Host(g2_upper_points[..size].to_vec()); -let mut g2_msm_results: HostOrDeviceSlice<'_, G2Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); -let mut g2_cfg = msm::get_default_msm_config::(); - -msm::msm(&scalars, &g2_points, &g2_cfg, &mut g2_msm_results).unwrap(); - -... -``` - -Here you can [find an example](https://github.com/ingonyama-zk/icicle/blob/5a96f9937d0a7176d88c766bd3ef2062b0c26c37/examples/rust/msm/src/main.rs#L114) of MSM on G2 Points. diff --git a/docs/docs/icicle/rust-bindings/multi-gpu.md b/docs/docs/icicle/rust-bindings/multi-gpu.md index 8e36a5396..29cee1aba 100644 --- a/docs/docs/icicle/rust-bindings/multi-gpu.md +++ b/docs/docs/icicle/rust-bindings/multi-gpu.md @@ -1,5 +1,7 @@ # Multi GPU APIs +TODO update for V3 + To learn more about the theory of Multi GPU programming refer to [this part](../multi-gpu.md) of documentation. Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) diff --git a/docs/docs/icicle/rust-bindings/ntt.md b/docs/docs/icicle/rust-bindings/ntt.md index 9d0b8f62f..06758972e 100644 --- a/docs/docs/icicle/rust-bindings/ntt.md +++ b/docs/docs/icicle/rust-bindings/ntt.md @@ -1,61 +1,21 @@ # NTT -## Example - -```rust -use icicle_bn254::curve::{ScalarCfg, ScalarField}; -use icicle_core::{ntt::{self, NTT}, traits::GenerateRandom}; -use icicle_cuda_runtime::{device_context::DeviceContext, memory::HostOrDeviceSlice, stream::CudaStream}; - -fn main() { - let size = 1 << 12; // Define the size of your input, e.g., 2^10 - - let icicle_omega = ::get_root_of_unity( - size.try_into() - .unwrap(), - ) - - // Generate random inputs - println!("Generating random inputs..."); - let scalars = HostOrDeviceSlice::Host(ScalarCfg::generate_random(size)); - - // Allocate memory on CUDA device for NTT results - let mut ntt_results: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::cuda_malloc(size).expect("Failed to allocate CUDA memory"); - - // Create a CUDA stream - let stream = CudaStream::create().expect("Failed to create CUDA stream"); - let ctx = DeviceContext::default(); // Assuming default device context - ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); - - // Configure NTT - let mut cfg = ntt::NTTConfig::default(); - cfg.ctx.stream = &stream; - cfg.is_async = true; // Set to true for asynchronous execution - - // Execute NTT on device - println!("Executing NTT on device..."); - ntt::ntt(&scalars, ntt::NTTDir::kForward, &cfg, &mut ntt_results).expect("Failed to execute NTT"); - - // Synchronize CUDA stream to ensure completion - stream.synchronize().expect("Failed to synchronize CUDA stream"); - - // Optionally, move results to host for further processing or verification - println!("NTT execution complete."); -} -``` - ## NTT API overview ```rust -pub fn ntt( - input: &HostOrDeviceSlice, +pub fn ntt( + input: &(impl HostOrDeviceSlice + ?Sized), dir: NTTDir, cfg: &NTTConfig, - output: &mut HostOrDeviceSlice, -) -> IcicleResult<()> -``` + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; -`ntt:ntt` expects: +pub fn ntt_inplace( + inout: &mut (impl HostOrDeviceSlice + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, +) -> Result<(), eIcicleError> +``` - **`input`** - buffer to read the inputs of the NTT from. - **`dir`** - whether to compute forward or inverse NTT. @@ -67,16 +27,16 @@ The `input` and `output` buffers can be on device or on host. Being on host mean ### NTT Config ```rust -pub struct NTTConfig<'a, S> { - pub ctx: DeviceContext<'a>, +pub struct NTTConfig { + pub stream_handle: IcicleStreamHandle, pub coset_gen: S, pub batch_size: i32, pub columns_batch: bool, pub ordering: Ordering, - are_inputs_on_device: bool, - are_outputs_on_device: bool, + pub are_inputs_on_device: bool, + pub are_outputs_on_device: bool, pub is_async: bool, - pub ntt_algorithm: NttAlgorithm, + pub ext: ConfigExtension, } ``` @@ -84,7 +44,7 @@ The `NTTConfig` struct is a configuration object used to specify parameters for #### Fields -- **`ctx: DeviceContext<'a>`**: Specifies the device context, including the device ID and the stream ID. +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution - **`coset_gen: S`**: Defines the coset generator used for coset (i)NTTs. By default, this is set to `S::one()`, indicating that no coset is being used. @@ -99,102 +59,48 @@ The `NTTConfig` struct is a configuration object used to specify parameters for - **`are_outputs_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. If the inputs and outputs are the same pointer NTT will be computed in place. - **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. When set to `true`, the NTT function will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity and correctness. +- **`ext: ConfigExtension`**: extended configuration for backend. -- **`ntt_algorithm: NttAlgorithm`**: Can be one of `Auto`, `Radix2`, `MixedRadix`. -`Auto` will select `Radix 2` or `Mixed Radix` algorithm based on heuristics. -`Radix2` and `MixedRadix` will force the use of an algorithm regardless of the input size or other considerations. You should use one of these options when you know for sure that you want to - -#### Usage - -Example initialization with default settings: - -```rust -let default_config = NTTConfig::default(); -``` - -Customizing the configuration: - -```rust -let custom_config = NTTConfig { - ctx: custom_device_context, - coset_gen: my_coset_generator, - batch_size: 10, - columns_batch: false, - ordering: Ordering::kRN, - are_inputs_on_device: true, - are_outputs_on_device: true, - is_async: false, - ntt_algorithm: NttAlgorithm::MixedRadix, -}; -``` - -### Modes - -NTT supports two different modes `Batch NTT` and `Single NTT` - -You may toggle between single and batch NTT by simply configure `batch_size` to be larger then 1 in your `NTTConfig`. +#### Example ```rust -let mut cfg = ntt::get_default_ntt_config::(); -cfg.batch_size = 10 // your ntt using this config will run in batch mode. -``` - -`batch_size=1` would keep our NTT in single NTT mode. - -Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. - -### Initializing the NTT Domain - -Before performing NTT operations, its necessary to initialize the NTT domain, It only needs to be called once per GPU since the twiddles are cached. - -```rust -ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); -``` - -### `initialize_domain` - -```rust -pub fn initialize_domain(primitive_root: F, ctx: &DeviceContext, fast_twiddles: bool) -> IcicleResult<()> -where - F: FieldImpl, - ::Config: NTT; +// Setting Bn254 points and scalars +println!("Generating random inputs on host for bn254..."); +let scalars = Bn254ScalarCfg::generate_random(size); +let mut ntt_results = DeviceVec::::device_malloc(size).unwrap(); + +// constructing NTT domain +initialize_domain( + ntt::get_root_of_unity::( + size.try_into() + .unwrap(), + ), + &ntt::NTTInitDomainConfig::default(), +) +.unwrap(); + +// Using default config +let cfg = ntt::NTTConfig::::default(); + +// Computing NTT +ntt::ntt( + HostSlice::from_slice(&scalars), + ntt::NTTDir::kForward, + &cfg, + &mut ntt_results[..], +) +.unwrap(); ``` -#### Parameters +### NTT Domain -- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. - -- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. - -#### Returns - -- **`IcicleResult<()>`**: Will return an error if the operation fails. - -#### Parameters - -- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. - -- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. - -#### Returns - -- **`IcicleResult<()>`**: Will return an error if the operation fails. - -### Releasing the domain - -The `release_domain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. +Before performing NTT operations, it is mandatory to construct the domain as [explained here](../primitives/ntt.md#ntt-domain). +In rust, we have the following functions to construct, destruct the domain and retrieve a root of unity from it: ```rust -pub fn release_domain(ctx: &DeviceContext) -> IcicleResult<()> -where - F: FieldImpl, - ::Config: NTT +pub trait NTTDomain { + pub fn initialize_domain(primitive_root: F, config: &NTTInitDomainConfig) -> Result<(), eIcicleError>; + pub fn release_domain() -> Result<(), eIcicleError>; + pub fn get_root_of_unity(max_size: u64) -> F; +} ``` - -#### Parameters - -- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. - -#### Returns - -The function returns an `IcicleResult<()>`, which represents the result of the operation. If the operation is successful, the function returns `Ok(())`, otherwise it returns an error. diff --git a/docs/docs/icicle/rust-bindings/polynomials.md b/docs/docs/icicle/rust-bindings/polynomials.md index c168aff48..edb99c917 100644 --- a/docs/docs/icicle/rust-bindings/polynomials.md +++ b/docs/docs/icicle/rust-bindings/polynomials.md @@ -110,8 +110,7 @@ These traits are implemented for references to DensePolynomial (i.e., &DensePoly In addition to the traits, the following methods are implemented: ```rust -impl DensePolynomial { - pub fn init_cuda_backend() -> bool {...} +impl DensePolynomial { // Returns a mutable slice of the polynomial coefficients on the device pub fn coeffs_mut_slice(&mut self) -> &mut DeviceSlice {...} } @@ -131,7 +130,7 @@ Functions within the DensePolynomial API that deal with polynomial coefficients ```rust // Assume `coeffs` could either be in host memory or CUDA device memory -let coeffs: DeviceSlice = DeviceVec::::cuda_malloc(coeffs_len).unwrap(); +let coeffs: DeviceSlice = DeviceVec::::device_malloc(coeffs_len).unwrap(); let p_from_coeffs = PolynomialBabyBear::from_coeffs(&coeffs, coeffs.len()); // Similarly for evaluations from roots of unity @@ -152,8 +151,6 @@ First, choose the appropriate field implementation for your polynomial operation ```rust use icicle_babybear::polynomials::DensePolynomial as PolynomialBabyBear; -// Initialize the CUDA backend for polynomial operations -PolynomialBabyBear::init_cuda_backend(); let f = PolynomialBabyBear::from_coeffs(...); // now use f by calling the implemented traits @@ -234,7 +231,7 @@ f.eval_on_domain(HostSlice::from_slice(&domain), HostSlice::from_mut_slice(&mut // Evaluate on roots-of-unity-domain let domain_log_size = 4; -let mut device_evals = DeviceVec::::cuda_malloc(1 << domain_log_size).unwrap(); +let mut device_evals = DeviceVec::::device_malloc(1 << domain_log_size).unwrap(); f.eval_on_rou_domain(domain_log_size, &mut device_evals[..]); ``` @@ -246,7 +243,7 @@ Read or copy polynomial coefficients for further processing: let x_squared_coeff = f.get_coeff(2); // Coefficient of x^2 // Copy coefficients to a device-specific memory space -let mut device_mem = DeviceVec::::cuda_malloc(coeffs.len()).unwrap(); +let mut device_mem = DeviceVec::::device_malloc(coeffs.len()).unwrap(); f.copy_coeffs(0, &mut device_mem[..]); ``` diff --git a/docs/docs/icicle/rust-bindings/vec-ops.md b/docs/docs/icicle/rust-bindings/vec-ops.md index e8cafdfda..61aa71570 100644 --- a/docs/docs/icicle/rust-bindings/vec-ops.md +++ b/docs/docs/icicle/rust-bindings/vec-ops.md @@ -1,56 +1,6 @@ # Vector Operations API -Our vector operations API which is part of `icicle-cuda-runtime` package, includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory. - -## Examples - -### Addition of Scalars - -```rust -use icicle_bn254::curve::{ScalarCfg, ScalarField}; -use icicle_core::vec_ops::{add_scalars}; - -let test_size = 1 << 18; - -let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); -let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); -let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); - -let cfg = VecOpsConfig::default(); -add_scalars(&a, &b, &mut result, &cfg).unwrap(); -``` - -### Subtraction of Scalars - -```rust -use icicle_bn254::curve::{ScalarCfg, ScalarField}; -use icicle_core::vec_ops::{sub_scalars}; - -let test_size = 1 << 18; - -let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); -let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); -let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); - -let cfg = VecOpsConfig::default(); -sub_scalars(&a, &b, &mut result, &cfg).unwrap(); -``` - -### Multiplication of Scalars - -```rust -use icicle_bn254::curve::{ScalarCfg, ScalarField}; -use icicle_core::vec_ops::{mul_scalars}; - -let test_size = 1 << 18; - -let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); -let ones: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::one(); test_size]); -let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); - -let cfg = VecOpsConfig::default(); -mul_scalars(&a, &ones, &mut result, &cfg).unwrap(); -``` +Our vector operations API includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory. ## Vector Operations Configuration @@ -61,22 +11,24 @@ The `VecOpsConfig` struct encapsulates the settings for vector operations, inclu Defines configuration parameters for vector operations. ```rust -pub struct VecOpsConfig<'a> { - pub ctx: DeviceContext<'a>, - is_a_on_device: bool, - is_b_on_device: bool, - is_result_on_device: bool, +pub struct VecOpsConfig { + pub stream_handle: IcicleStreamHandle, + pub is_a_on_device: bool, + pub is_b_on_device: bool, + pub is_result_on_device: bool, pub is_async: bool, + pub ext: ConfigExtension, } ``` #### Fields -- **`ctx: DeviceContext<'a>`**: Specifies the device context for the operation, including the device ID and memory pool. -- **`is_a_on_device`**: Indicates if the first operand vector resides in device memory. -- **`is_b_on_device`**: Indicates if the second operand vector resides in device memory. -- **`is_result_on_device`**: Specifies if the result vector should be stored in device memory. -- **`is_async`**: Enables asynchronous operation. If `true`, operations are non-blocking; otherwise, they block the current thread. +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution +- **`is_a_on_device: bool`**: Indicates whether the input data a has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_b_on_device: bool`**: Indicates whether the input b data has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_result_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. +- **`ext: ConfigExtension`**: extended configuration for backend. ### Default Configuration @@ -86,122 +38,67 @@ pub struct VecOpsConfig<'a> { let cfg = VecOpsConfig::default(); ``` -These are the default settings. - -```rust -impl<'a> Default for VecOpsConfig<'a> { - fn default() -> Self { - Self::default_for_device(DEFAULT_DEVICE_ID) - } -} - -impl<'a> VecOpsConfig<'a> { - pub fn default_for_device(device_id: usize) -> Self { - VecOpsConfig { - ctx: DeviceContext::default_for_device(device_id), - is_a_on_device: false, - is_b_on_device: false, - is_result_on_device: false, - is_async: false, - } - } -} -``` - ## Vector Operations Vector operations are implemented through the `VecOps` trait, providing methods for addition, subtraction, and multiplication of vectors. -### `VecOps` Trait - -```rust -pub trait VecOps { - fn add( - a: &HostOrDeviceSlice, - b: &HostOrDeviceSlice, - result: &mut HostOrDeviceSlice, - cfg: &VecOpsConfig, - ) -> IcicleResult<()>; - - fn sub( - a: &HostOrDeviceSlice, - b: &HostOrDeviceSlice, - result: &mut HostOrDeviceSlice, - cfg: &VecOpsConfig, - ) -> IcicleResult<()>; - - fn mul( - a: &HostOrDeviceSlice, - b: &HostOrDeviceSlice, - result: &mut HostOrDeviceSlice, - cfg: &VecOpsConfig, - ) -> IcicleResult<()>; -} -``` - -#### Methods +### Methods All operations are element-wise operations, and the results placed into the `result` param. These operations are not in place. - **`add`**: Computes the element-wise sum of two vectors. +- **`accumulate`**: Sum input b to a inplace. - **`sub`**: Computes the element-wise difference between two vectors. - **`mul`**: Performs element-wise multiplication of two vectors. +- **`transpose`**: Performs matrix transpose. +- **`bit_reverse/bit_reverse_inplace`**: Reverse order of elements based on bit-reverse. -## MatrixTranspose API Documentation - -This section describes the functionality of the `TransposeMatrix` function used for matrix transposition. -The function takes a matrix represented as a 1D slice and transposes it, storing the result in another 1D slice. - -### Function ```rust -pub fn transpose_matrix( - input: &HostOrDeviceSlice, - row_size: u32, - column_size: u32, - output: &mut HostOrDeviceSlice, - ctx: &DeviceContext, - on_device: bool, - is_async: bool, -) -> IcicleResult<()> -where - F: FieldImpl, - ::Config: VecOps -``` +pub fn add_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn accumulate_scalars( + a: &mut (impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn sub_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn mul_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; -### Parameters - -- **`input`**: A slice representing the input matrix. The slice can be stored on either the host or the device. -- **`row_size`**: The number of rows in the input matrix. -- **`column_size`**: The number of columns in the input matrix. -- **`output`**: A mutable slice to store the transposed matrix. The slice can be stored on either the host or the device. -- **`ctx`**: A reference to the `DeviceContext`, which provides information about the device where the operation will be performed. -- **`on_device`**: A boolean flag indicating whether the inputs and outputs are on the device. -- **`is_async`**: A boolean flag indicating whether the operation should be performed asynchronously. - -### Return Value - -`Ok(())` if the operation is successful, or an `IcicleResult` error otherwise. - -### Example - -```rust -use icicle::HostOrDeviceSlice; -use icicle::DeviceContext; -use icicle::FieldImpl; -use icicle::VecOps; - -let input: HostOrDeviceSlice = // ...; -let mut output: HostOrDeviceSlice = // ...; -let ctx: DeviceContext = // ...; - -transpose_matrix(&input, 5, 4, &mut output, &ctx, true, false) - .expect("Failed to transpose matrix"); -``` - -The function takes a matrix represented as a 1D slice, transposes it, and stores the result in another 1D slice. The input and output slices can be stored on either the host or the device, and the operation can be performed synchronously or asynchronously. - -The function is generic and can work with any type `F` that implements the `FieldImpl` trait. The `::Config` type must also implement the `VecOps` trait, which provides the `transpose` method used to perform the actual transposition. - -The function returns an `IcicleResult<()>`, indicating whether the operation was successful or not. +pub fn transpose_matrix( + input: &(impl HostOrDeviceSlice + ?Sized), + nof_rows: u32, + nof_cols: u32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn bit_reverse( + input: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; + +pub fn bit_reverse_inplace( + input: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; +``` \ No newline at end of file diff --git a/docs/docs/introduction.md b/docs/docs/introduction.md index 14cb3ae72..b173cf5f9 100644 --- a/docs/docs/introduction.md +++ b/docs/docs/introduction.md @@ -6,42 +6,37 @@ title: '' # Welcome to Ingonyama's Developer Documentation -Ingonyama is a next-generation semiconductor company, focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute intensive cryptography and making it accessible for developers to build on top of. +Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute-intensive cryptography and making it accessible for developers to build upon. -Currently our flagship products are: +Our flagship product is **ICICLE** -- **ICICLE**: - [ICICLE](https://github.com/ingonyama-zk/icicle) is a fully featured GPU accelerated cryptography library for building ZK provers. ICICLE allows you to accelerate your existing ZK protocols in a matter of hours or implement your protocol from scratch on GPU. +#### **ICICLE v3** +[ICICLE v3](https://github.com/ingonyama-zk/icicle) is a versatile cryptography library designed to support multiple compute backends, including CUDA, CPU, and potentially others like Metal, WebGPU, Vulkan, and ZPU. Originally focused on GPU acceleration, ICICLE has evolved to offer backend-agnostic cryptographic acceleration, allowing you to build ZK provers or other cryptographic applications with ease, leveraging the best available hardware for your needs. ---- - -## Our current take on hardware acceleration - -We believe GPUs are as important for ZK as for AI. +- **Multiple Backend Support:** Develop on CPU and deploy on various backends including CUDA and potentially Metal, WebGPU, Vulkan, ZPU, or even remote machines. +- **Cross-Language Compatibility:** Use ICICLE across multiple programming languages such as C++, Rust, Go, and possibly Python. +- **Optimized for ZKPs:** Accelerate cryptographic operations like elliptic curve operations, MSM, NTT, Poseidon hash, and more. -- GPUs are a perfect match for ZK compute - around 97% of ZK protocol runtime is parallel by nature. -- GPUs are simple for developers to use and scale compared to other hardware platforms. -- GPUs are extremely competitive in terms of power / performance and price (3x cheaper compared to FPGAs). -- GPUs are popular and readily available. +**Learn more about ICICLE and its multi-backend support [here][ICICLE-OVERVIEW].** -For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). +--- -Despite our current focus on GPUs we are still hard at work developing a ZPU (ZK Processing Unit), with the goal of offering a programmable hardware platform for ZK. To read more about ZPUs we suggest you read this [article](https://medium.com/@ingonyama/zpu-the-zero-knowledge-processing-unit-f886a48e00e0). +## Our Approach to Hardware Acceleration -## ICICLE +We believe that GPUs are as critical for ZK as they are for AI. -[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. -ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. +- **Parallelism:** Approximately 97% of ZK protocol runtime is naturally parallel, making GPUs an ideal match. +- **Developer-Friendly:** GPUs offer simplicity in scaling and usage compared to other hardware platforms. +- **Cost-Effective:** GPUs provide a competitive balance of power, performance, and cost, often being 3x cheaper than FPGAs. -ICICLE is designed to be easy to use, developers don't have to touch a single line of CUDA code. Our Rust and Golang bindings allow your team to transition from CPU to GPU with minimal changes. +For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). -Learn more about ICICLE and GPUs [here][ICICLE-OVERVIEW]. ## Get in Touch If you have any questions, ideas, or are thinking of building something in this space, join the discussion on [Discord]. You can explore our code on [github](https://github.com/ingonyama-zk) or read some of [our research papers](https://github.com/ingonyama-zk/papers). -Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and sign up for our [mailing list](https://wkf.ms/3LKCbdj) to get our latest announcements. +Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and join us IRL at our [next event](https://www.ingonyama.com/events) [ICICLE-OVERVIEW]: ./icicle/overview.md [Discord]: https://discord.gg/6vYrE7waPj diff --git a/docs/sidebars.js b/docs/sidebars.js index 48cb95704..c23d9b836 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -14,171 +14,213 @@ module.exports = { }, collapsed: false, items: [ - { - type: "doc", - label: "Getting started", - id: "icicle/introduction" - }, - { - type: "doc", - label: "ICICLE Core", - id: "icicle/core", - }, { type: "category", - label: "Primitives", + label: "Getting started", link: { type: `doc`, - id: 'icicle/primitives/overview', + id: "icicle/getting_started", }, - collapsed: true, + collapsed: false, items: [ { type: "doc", - label: "MSM", - id: "icicle/primitives/msm", - }, - { - type: "doc", - label: "NTT", - id: "icicle/primitives/ntt", + label: "Build ICICLE from source", + id: "icicle/build_from_source", }, + ], + }, + { + type: "category", + label: "Architecture overview", + link: { + type: `doc`, + id: "icicle/arch_overview" + }, + collapsed: false, + items: [ { type: "doc", - label: "Keccak Hash", - id: "icicle/primitives/keccak", + label: "CUDA Backend", + id: "icicle/install_cuda_backend" }, { type: "doc", - label: "Poseidon Hash", - id: "icicle/primitives/poseidon", + label: "Multi Device Support", + id: "icicle/multi-device", }, { type: "doc", - label: "Poseidon2 Hash", - id: "icicle/primitives/poseidon2", + label: "Build Your Own Backend", + id: "icicle/build_your_own_backend" }, - ], - }, - { - type: "doc", - label: "Polynomials", - id: "icicle/polynomials/overview", + ] }, { type: "doc", - label: "Multi GPU Support", - id: "icicle/multi-gpu", + label: "ICICLE libraries", + id: "icicle/libraries", }, { type: "category", - label: "Golang bindings", + label: "Programmers guide", link: { type: `doc`, - id: "icicle/golang-bindings", + id: "icicle/programmers_guide/general", }, - collapsed: true, + collapsed: false, items: [ - { - type: "category", - label: "MSM", - link: { - type: `doc`, - id: "icicle/golang-bindings/msm", - }, - collapsed: true, - items: [ - { - type: "doc", - label: "MSM pre computation", - id: "icicle/golang-bindings/msm-pre-computation", - } - ] - }, { type: "doc", - label: "NTT", - id: "icicle/golang-bindings/ntt", + label: "C++", + id: "icicle/programmers_guide/cpp", }, { type: "doc", - label: "EC-NTT", - id: "icicle/golang-bindings/ecntt", + label: "Rust", + id: "icicle/programmers_guide/rust", }, { type: "doc", - label: "Vector operations", - id: "icicle/golang-bindings/vec-ops", - }, - { - type: "doc", - label: "Keccak Hash", - id: "icicle/golang-bindings/keccak", - }, - { - type: "doc", - label: "Multi GPU Support", - id: "icicle/golang-bindings/multi-gpu", - }, - ] + label: "Go", + id: "icicle/programmers_guide/go", + } + ], }, { type: "category", - label: "Rust bindings", + label: "Compute API", link: { type: `doc`, - id: "icicle/rust-bindings", + id: 'icicle/primitives/overview', }, collapsed: true, items: [ - { - type: "category", - label: "MSM", - link: { - type: `doc`, - id: "icicle/rust-bindings/msm", - }, - collapsed: true, - items: [ - { - type: "doc", - label: "MSM pre computation", - id: "icicle/rust-bindings/msm-pre-computation", - } - ] - }, { type: "doc", - label: "NTT", - id: "icicle/rust-bindings/ntt", + label: "MSM", + id: "icicle/primitives/msm", }, { type: "doc", - label: "EC-NTT", - id: "icicle/rust-bindings/ecntt", + label: "NTT / ECNTT", + id: "icicle/primitives/ntt", }, { type: "doc", label: "Vector operations", - id: "icicle/rust-bindings/vec-ops", + id: "icicle/primitives/vec_ops", }, { type: "doc", - label: "Keccak Hash", - id: "icicle/rust-bindings/keccak", + label: "Polynomials", + id: "icicle/polynomials/overview", }, { - type: "doc", - label: "Multi GPU Support", - id: "icicle/rust-bindings/multi-gpu", + type: "category", + label: "Golang bindings", + link: { + type: `doc`, + id: "icicle/golang-bindings", + }, + collapsed: true, + items: [ + { + type: "category", + label: "MSM", + link: { + type: `doc`, + id: "icicle/golang-bindings/msm", + }, + collapsed: true, + items: [ + { + type: "doc", + label: "MSM pre computation", + id: "icicle/golang-bindings/msm-pre-computation", + } + ] + }, + { + type: "doc", + label: "NTT", + id: "icicle/golang-bindings/ntt", + }, + { + type: "doc", + label: "EC-NTT", + id: "icicle/golang-bindings/ecntt", + }, + { + type: "doc", + label: "Vector operations", + id: "icicle/golang-bindings/vec-ops", + }, + { + type: "doc", + label: "Multi GPU Support", + id: "icicle/golang-bindings/multi-gpu", + }, + ] }, { - type: "doc", - label: "Polynomials", - id: "icicle/rust-bindings/polynomials", + type: "category", + label: "Rust bindings", + link: { + type: `doc`, + id: "icicle/rust-bindings", + }, + collapsed: true, + items: [ + { + type: "doc", + label: "MSM", + id: "icicle/rust-bindings/msm", + }, + { + type: "doc", + label: "NTT", + id: "icicle/rust-bindings/ntt", + }, + { + type: "doc", + label: "ECNTT", + id: "icicle/rust-bindings/ecntt", + }, + { + type: "doc", + label: "Vector operations", + id: "icicle/rust-bindings/vec-ops", + }, + { + type: "doc", + label: "Polynomials", + id: "icicle/rust-bindings/polynomials", + }, + { + type: "doc", + label: "Multi GPU Support (TODO)", + id: "icicle/rust-bindings/multi-gpu", + }, + ], }, ], }, + { + type: "doc", + label: "Migrate from ICICLE V2", + id: "icicle/migrate_from_v2", + }, + { + type: "doc", + label: "Benchmarks", + id: "icicle/benchmarks", + }, + { + type: "doc", + label: "FAQ and Troubleshooting", + id: "icicle/faq_and_troubleshooting", + }, { type: "doc", label: "Google Colab Instructions", @@ -191,11 +233,6 @@ module.exports = { }, ] }, - { - type: "doc", - label: "ZK Containers", - id: "ZKContainers", - }, { type: "doc", label: "Ingonyama Grant program", diff --git a/examples/ZKContainer.md b/examples/ZKContainer.md deleted file mode 100644 index 1eacf3c29..000000000 --- a/examples/ZKContainer.md +++ /dev/null @@ -1,23 +0,0 @@ -# ZKContainer - -We recommend using [ZKContainer](https://www.ingonyama.com/blog/product-announcement-zk-containers), where we have already preinstalled all the required dependencies, to run Icicle examples. -To use our containers you will need [Docker](https://www.docker.com/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html). - -In each example directory, ZKContainer files are located in a subdirectory `.devcontainer`. - -- File `Dockerfile` specifies how to build an image of a ZKContainer. -- File `devcontainer.json` enables running ZKContainer from Visual Studio Code. - -## Running ZKContainer from shell - -```sh -docker build -t icicle-example-poseidon -f .devcontainer/Dockerfile . -``` - -To run the example interactively, start the container - -```sh -docker run -it --rm --gpus all -v .:/icicle-example icicle-example-poseidon -``` - -Inside the container, run the commands for building the library for whichever [build system](../README.md#build-systems) you choose to use. diff --git a/examples/c++/best-practice-ntt/CMakeLists.txt b/examples/c++/best-practice-ntt/CMakeLists.txt index d6e5b7d7d..d523b645c 100644 --- a/examples/c++/best-practice-ntt/CMakeLists.txt +++ b/examples/c++/best-practice-ntt/CMakeLists.txt @@ -1,23 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") +project(example) + +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) diff --git a/examples/c++/best-practice-ntt/README.md b/examples/c++/best-practice-ntt/README.md index 0839105aa..0ba43ee29 100644 --- a/examples/c++/best-practice-ntt/README.md +++ b/examples/c++/best-practice-ntt/README.md @@ -17,17 +17,19 @@ Typically, you concurrently ## Best-Practices -1. Use three separate CUDA streams for Download, Upload, and Compute operations -2. Use pinned (page-locked) memory on host to speed data bus transfers. Calling `cudaHostAlloc` allocates pinned memory. -3. Use in-place NTT to save on device memory. +1. Use three separate streams for Download to device, Upload from device, and Compute operations +2. Future: Use pinned (page-locked) memory on host to speed data bus transfers. +3. Compute in-place NTT. ## Running the example -To change the default curve BN254, edit `compile.sh` and `CMakeLists.txt` +To change the default curve BN254, edit `run.sh` and `CMakeLists.txt` ```sh -./compile.sh -./run.sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir ``` To compare with ICICLE baseline (i.e. non-concurrent) NTT, you can run [this example](../ntt/README.md). diff --git a/examples/c++/best-practice-ntt/compile.sh b/examples/c++/best-practice-ntt/compile.sh deleted file mode 100755 index 2506ff967..000000000 --- a/examples/c++/best-practice-ntt/compile.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DG2=OFF -DMSM=OFF -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example - diff --git a/examples/c++/best-practice-ntt/example.cpp b/examples/c++/best-practice-ntt/example.cpp new file mode 100644 index 000000000..791491cf7 --- /dev/null +++ b/examples/c++/best-practice-ntt/example.cpp @@ -0,0 +1,126 @@ +#include +#include +#include +#include + +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" +using namespace bn254; + +#include "examples_utils.h" +#include "icicle/backend/ntt_config.h" + +void initialize_input(const unsigned ntt_size, const unsigned batch_size, scalar_t* elements) +{ + for (unsigned i = 0; i < ntt_size * batch_size; i++) { + elements[i] = scalar_t::from(i + 1); + } +} + +int main(int argc, char* argv[]) +{ + try_load_and_set_backend_device(argc, argv); + + // set these parameters to match the desired NTT size and batch size + const unsigned log_ntt_size = 20; + const unsigned batch_size = 16; + + scalar_t basic_root = scalar_t::omega(log_ntt_size); + + const unsigned ntt_size = 1 << log_ntt_size; + std::cout << "log NTT size: " << log_ntt_size << std::endl; + std::cout << "Batch size: " << batch_size << std::endl; + + // Create separate streams for overlapping data transfers and kernel execution. + icicleStreamHandle stream_compute, stream_h2d, stream_d2h; + ICICLE_CHECK(icicle_create_stream(&stream_compute)); + ICICLE_CHECK(icicle_create_stream(&stream_h2d)); + ICICLE_CHECK(icicle_create_stream(&stream_d2h)); + + // Initialize NTT domain + std::cout << "Init NTT domain" << std::endl; + auto ntt_init_domain_cfg = default_ntt_init_domain_config(); + // set CUDA backend specific flag for init_domain + ConfigExtension backend_cfg_ext; + backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); + ntt_init_domain_cfg.ext = &backend_cfg_ext; + ICICLE_CHECK(bn254_ntt_init_domain(&basic_root, &ntt_init_domain_cfg)); + + std::cout << "Concurrent Download, Upload, and Compute In-place NTT" << std::endl; + int nof_blocks = 32; + int block_size = ntt_size * batch_size / nof_blocks; + std::cout << "Number of blocks: " << nof_blocks << ", block size: " << block_size << " Bytes" << std::endl; + + // on-host pinned data + scalar_t* h_inp[2]; + scalar_t* h_out[2]; + for (int i = 0; i < 2; i++) { + h_inp[i] = new scalar_t[ntt_size * batch_size]; + h_out[i] = new scalar_t[ntt_size * batch_size]; + } + + // on-device in-place data + // we need two on-device vectors to overlap data transfers with NTT kernel execution + scalar_t* d_vec[2]; + for (int i = 0; i < 2; i++) { + ICICLE_CHECK(icicle_malloc((void**)&d_vec[i], sizeof(scalar_t) * ntt_size * batch_size)); + } + + // initialize input data + initialize_input(ntt_size, batch_size, h_inp[0]); + initialize_input(ntt_size, batch_size, h_inp[1]); + + // ntt configuration + NTTConfig config_compute = default_ntt_config(); + config_compute.batch_size = batch_size; + config_compute.are_inputs_on_device = true; + config_compute.are_outputs_on_device = true; + config_compute.is_async = true; + config_compute.stream = stream_compute; + // backend specific config extension + ConfigExtension ntt_cfg_ext; + ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); + config_compute.ext = &ntt_cfg_ext; + + for (int run = 0; run < 10; run++) { + int vec_compute = run % 2; + int vec_transfer = (run + 1) % 2; + std::cout << "Run: " << run << std::endl; + std::cout << "Compute Vector: " << vec_compute << std::endl; + std::cout << "Transfer Vector: " << vec_transfer << std::endl; + START_TIMER(inplace); + bn254_ntt(d_vec[vec_compute], ntt_size, NTTDir::kForward, &config_compute, d_vec[vec_compute]); + // we have to delay upload to device relative to download from device by one block: preserve write after read + for (int i = 0; i <= nof_blocks; i++) { + if (i < nof_blocks) { + // copy result back from device to host + ICICLE_CHECK(icicle_copy_async( + &h_out[vec_transfer][i * block_size], &d_vec[vec_transfer][i * block_size], sizeof(scalar_t) * block_size, + stream_d2h)); + } + if (i > 0) { + // copy next input from host to device to alternate buffer + ICICLE_CHECK(icicle_copy_async( + &d_vec[vec_transfer][(i - 1) * block_size], &h_inp[vec_transfer][(i - 1) * block_size], + sizeof(scalar_t) * block_size, stream_h2d)); + } + // synchronize upload and download at the end of the block to ensure data integrity + ICICLE_CHECK(icicle_stream_synchronize(stream_d2h)); + ICICLE_CHECK(icicle_stream_synchronize(stream_h2d)); + } + // synchronize compute stream with the end of the computation + ICICLE_CHECK(icicle_stream_synchronize(stream_compute)); + END_TIMER(inplace, "Concurrent In-Place NTT"); + } + + // Clean-up + for (int i = 0; i < 2; i++) { + ICICLE_CHECK(icicle_free(d_vec[i])); + delete[](h_inp[i]); + delete[](h_out[i]); + } + ICICLE_CHECK(icicle_destroy_stream(stream_compute)); + ICICLE_CHECK(icicle_destroy_stream(stream_d2h)); + ICICLE_CHECK(icicle_destroy_stream(stream_h2d)); + return 0; +} diff --git a/examples/c++/best-practice-ntt/example.cu b/examples/c++/best-practice-ntt/example.cu deleted file mode 100644 index 341f42a9e..000000000 --- a/examples/c++/best-practice-ntt/example.cu +++ /dev/null @@ -1,149 +0,0 @@ -#include -#include -#include -#include - -#include "curves/params/bn254.cuh" -#include "api/bn254.h" -using namespace bn254; -using namespace ntt; - -const std::string curve = "BN254"; - -typedef scalar_t S; -typedef scalar_t E; - -const unsigned max_log_ntt_size = 27; - -void initialize_input(const unsigned ntt_size, const unsigned nof_ntts, E* elements) -{ - for (unsigned i = 0; i < ntt_size * nof_ntts; i++) { - elements[i] = E::from(i + 1); - } -} - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char** argv) -{ - cudaDeviceReset(); - cudaDeviceProp deviceProperties; - int deviceId = 0; - cudaGetDeviceProperties(&deviceProperties, deviceId); - std::string gpu_full_name = deviceProperties.name; - std::cout << gpu_full_name << std::endl; - std::string gpu_name = gpu_full_name; - - std::cout << "Curve: " << curve << std::endl; - - S basic_root = S::omega(max_log_ntt_size); - - // change these parameters to match the desired NTT size and batch size - const unsigned log_ntt_size = 22; - const unsigned nof_ntts = 16; - - std::cout << "log NTT size: " << log_ntt_size << std::endl; - const unsigned ntt_size = 1 << log_ntt_size; - - std::cout << "Batch size: " << nof_ntts << std::endl; - - // Create separate CUDA streams for overlapping data transfers and kernel execution. - cudaStream_t stream_compute, stream_h2d, stream_d2h; - cudaStreamCreate(&stream_compute); - cudaStreamCreate(&stream_h2d); - cudaStreamCreate(&stream_d2h); - - // Create device context for NTT computation - auto ctx_compute = device_context::DeviceContext{ - stream_compute, // stream - 0, // device_id - 0, // mempool - }; - - // Initialize NTT domain and configuration - bn254_initialize_domain(&basic_root, ctx_compute, /* fast twiddles */ true); - NTTConfig config_compute = default_ntt_config(ctx_compute); - config_compute.ntt_algorithm = NttAlgorithm::MixedRadix; - config_compute.batch_size = nof_ntts; - config_compute.are_inputs_on_device = true; - config_compute.are_outputs_on_device = true; - config_compute.is_async = true; - - std::cout << "Concurrent Download, Upload, and Compute In-place NTT" << std::endl; - int nof_blocks = 32; - std::cout << "Number of blocks: " << nof_blocks << std::endl; - int block_size = ntt_size * nof_ntts / nof_blocks; - - // on-host pinned data - E* h_inp[2]; - E* h_out[2]; - for (int i = 0; i < 2; i++) { - cudaHostAlloc((void**)&h_inp[i], sizeof(E) * ntt_size * nof_ntts, cudaHostAllocDefault); - cudaHostAlloc((void**)&h_out[i], sizeof(E) * ntt_size * nof_ntts, cudaHostAllocDefault); - } - - // on-device in-place data - // we need two on-device vectors to overlap data transfers with NTT kernel execution - E* d_vec[2]; - for (int i = 0; i < 2; i++) { - cudaMalloc((void**)&d_vec[i], sizeof(E) * ntt_size * nof_ntts); - } - - // initialize input data - initialize_input(ntt_size, nof_ntts, h_inp[0]); - initialize_input(ntt_size, nof_ntts, h_inp[1]); - - cudaEvent_t compute_start, compute_stop; - cudaEventCreate(&compute_start); - cudaEventCreate(&compute_stop); - - for (int run = 0; run < 10; run++) { - int vec_compute = run % 2; - int vec_transfer = (run + 1) % 2; - std::cout << "Run: " << run << std::endl; - std::cout << "Compute Vector: " << vec_compute << std::endl; - std::cout << "Transfer Vector: " << vec_transfer << std::endl; - START_TIMER(inplace); - cudaEventRecord(compute_start, stream_compute); - bn254_ntt_cuda(d_vec[vec_compute], ntt_size, NTTDir::kForward, config_compute, d_vec[vec_compute]); - cudaEventRecord(compute_stop, stream_compute); - // we have to delay upload to device relative to download from device by one block: preserve write after read - for (int i = 0; i <= nof_blocks; i++) { - if (i < nof_blocks) { - cudaMemcpyAsync( - &h_out[vec_transfer][i * block_size], &d_vec[vec_transfer][i * block_size], sizeof(E) * block_size, - cudaMemcpyDeviceToHost, stream_d2h); - } - if (i > 0) { - cudaMemcpyAsync( - &d_vec[vec_transfer][(i - 1) * block_size], &h_inp[vec_transfer][(i - 1) * block_size], - sizeof(E) * block_size, cudaMemcpyHostToDevice, stream_h2d); - } - // synchronize upload and download at the end of the block to ensure data integrity - cudaStreamSynchronize(stream_d2h); - cudaStreamSynchronize(stream_h2d); - } - // synchronize compute stream with the end of the computation - cudaEventSynchronize(compute_stop); - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, compute_start, compute_stop); - END_TIMER(inplace, "Concurrent In-Place NTT"); - std::cout << "NTT time: " << milliseconds << " ms" << std::endl; - }; - - // Clean-up - for (int i = 0; i < 2; i++) { - cudaFree(d_vec[i]); - cudaFreeHost(h_inp[i]); - cudaFreeHost(h_out[i]); - } - cudaEventDestroy(compute_start); - cudaEventDestroy(compute_stop); - cudaStreamDestroy(stream_compute); - cudaStreamDestroy(stream_d2h); - cudaStreamDestroy(stream_h2d); - return 0; -} diff --git a/examples/c++/best-practice-ntt/run.sh b/examples/c++/best-practice-ntt/run.sh index 01eca66ba..879390d0a 100755 --- a/examples/c++/best-practice-ntt/run.sh +++ b/examples/c++/best-practice-ntt/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DMSM=OFF -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/examples_utils.h b/examples/c++/examples_utils.h new file mode 100644 index 000000000..e71c03bd3 --- /dev/null +++ b/examples/c++/examples_utils.h @@ -0,0 +1,34 @@ +#pragma once + +#include +#include "icicle/runtime.h" + +// Timer +using FpMilliseconds = std::chrono::duration; +#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); +#define END_TIMER(timer, msg) \ + printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); + +// Load and choose backend +void try_load_and_set_backend_device(int argc = 0, char** argv = nullptr) +{ + icicle_load_backend_from_env_or_default(); + + const char* selected_device = argc > 1 ? argv[1] : nullptr; + if (selected_device) { + ICICLE_LOG_INFO << "selecting " << selected_device << " device"; + ICICLE_CHECK(icicle_set_device(selected_device)); + return; + } + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_LOG_INFO << "setting " << device; + ICICLE_CHECK(icicle_set_device(device)); + return; + } + + ICICLE_LOG_INFO << "CUDA device not available, falling back to CPU"; +} \ No newline at end of file diff --git a/examples/c++/install-and-use-icicle/CMakeLists.txt b/examples/c++/install-and-use-icicle/CMakeLists.txt new file mode 100644 index 000000000..1b86fab2b --- /dev/null +++ b/examples/c++/install-and-use-icicle/CMakeLists.txt @@ -0,0 +1,19 @@ +# Add the executable +add_executable(example example.cpp) +# Link the libraries +target_link_libraries(example icicle_device icicle_field_bn254 icicle_curve_bn254) + +# OPTIONAL (if not installed in default location) + +# The following is setting compile and runtime paths for headers and libs assuming +# - headers in /custom/path/icicle/include +# - libs in/custom/path/icicle/lib + +# Include directories +target_include_directories(example PUBLIC /custom/path/icicle/include) +# Library directories +target_link_directories(example PUBLIC /custom/path/icicle/lib/) +# Set the RPATH so linker finds icicle libs at runtime +set_target_properties(example PROPERTIES + BUILD_RPATH /custom/path/icicle/lib/ + INSTALL_RPATH /custom/path/icicle/lib/) \ No newline at end of file diff --git a/examples/c++/install-and-use-icicle/README.md b/examples/c++/install-and-use-icicle/README.md new file mode 100644 index 000000000..f0a161833 --- /dev/null +++ b/examples/c++/install-and-use-icicle/README.md @@ -0,0 +1,117 @@ +# Example: Install and use ICICLE (C++) + +This example demonstrates how to install ICICLE binaries and use them in a C++ application. + +Download release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases): +- **Frontend** icicle30-ubuntu22.tar.gz +- **Backend** icicle30-ubuntu22-cuda122.tar.gz + +> [!NOTE] +> The names of the files are based on the release version. Ensure you update the tar file names in the example if you’re using a different release. + +## Optional: Using Docker + +While not mandatory, this example can be demonstrated in an Ubuntu 22 Docker container. +```bash +docker run -it --rm --gpus all -v ./:/workspace -w /workspace icicle-release-ubuntu22-cuda122 bash +``` + +This command starts a bash session in the Docker container, with GPUs enabled and the example files mapped to /workspace in the container. + +### Building the docker image + +The Docker image is based on NVIDIA’s image for Ubuntu 22.04 and can be built from the following Dockerfile: + +```dockerfile +# Use the official NVIDIA development runtime image for Ubuntu 22.04 +FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + build-essential \ + cmake \ + tar +``` + +Build the Docker image with the following command: +```bash +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 .` +``` + +## Extract tars and install ICICLE + +Extracting and Installing the Frontend +```bash +cd release +# extract frontend part +tar xzvf icicle30-ubuntu22.tar.gz +cp -r ./icicle/lib/* /usr/lib/ +cp -r ./icicle/include/icicle/ /usr/local/include/ # copy C++ headers +``` + +Extracting and Installing the CUDA Backend (Optional) + +```bash +# extract CUDA backend (OPTIONAL) +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt +rm -rf icicle # remove the extracted dir +``` + +## Compile and Link the C++ Example with ICICLE + +```bash +cd .. +mkdir build +cmake -S . -B build && cmake --build build +``` + +## Launch the executable + +```bash +./build/example +``` + +## Install ICICLE in a Custom Location + +If installing in a custom location such as /custom/path: +```bash +mkdir -p /custom/path +cd release +tar xzvf icicle30-ubuntu22.tar.gz -C /custom/path +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /custom/path # OPTIONAL +``` + +### Build your app and link to ICICLE + +When installing ICICLE in a custom location, you need to specify the paths for the include and library directories so that the compiler, linker, and loader can find them during compile and runtime. Add the following to your CMake file: +```cmake +# Include directories +target_include_directories(example PUBLIC /custom/path/icicle/include) +# Library directories +target_link_directories(example PUBLIC /custom/path/icicle/lib/) +# Set the RPATH so linker finds icicle libs at runtime +set_target_properties(example PROPERTIES + BUILD_RPATH /custom/path/icicle/lib/ + INSTALL_RPATH /custom/path/icicle/lib/) +``` + +Compile and Launch the Executable + +```bash +cd .. +mkdir build +cmake -S . -B build && cmake --build build +``` + +### Launch the executable + +Since CUDA backend is installed to `/custom/path` we need to set the env variable accordingly: +```bash +export ICICLE_BACKEND_INSTALL_DIR=/custom/path/icicle/lib/backend +./build/example +``` + +Alternatively, you can use the following API in your code: +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` diff --git a/examples/c++/install-and-use-icicle/example.cpp b/examples/c++/install-and-use-icicle/example.cpp new file mode 100644 index 000000000..f86eceb97 --- /dev/null +++ b/examples/c++/install-and-use-icicle/example.cpp @@ -0,0 +1,92 @@ +#include +#include +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" + +using namespace bn254; // This makes scalar_t a bn254 scalar instead of bn254::scalar_t + +// Utility function to print arrays +template +void print_array(const T* arr, int size) +{ + for (int i = 0; i < size; ++i) { + std::cout << "\t" << i << ": " << arr[i] << std::endl; + } +} + +int main(int argc, char* argv[]) +{ + // Load installed backends + icicle_load_backend_from_env_or_default(); + + // Check if GPU is available + Device device_cpu = {"CPU", 0}; + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + Device device_gpu = {"CUDA", 0}; + if (is_cuda_device_available) { + ICICLE_LOG_INFO << "GPU is available"; + } else { + ICICLE_LOG_INFO << "GPU is not available, falling back to CPU only"; + device_gpu = device_cpu; + } + + // Example input (on host memory) for NTT + const unsigned log_ntt_size = 2; + const unsigned ntt_size = 1 << log_ntt_size; + auto input_cpu = std::make_unique(ntt_size); + scalar_t::rand_host_many(input_cpu.get(), ntt_size); + + // Allocate output on host memory + auto output_cpu = std::make_unique(ntt_size); + scalar_t root_of_unity = scalar_t::omega(log_ntt_size); + auto ntt_config = default_ntt_config(); + + // Part 1: Running NTT on CPU + std::cout << "Part 1: compute on CPU: " << std::endl; + icicle_set_device(device_cpu); + ntt_init_domain(root_of_unity, default_ntt_init_domain_config()); // Initialize NTT domain for CPU + ntt(input_cpu.get(), ntt_size, NTTDir::kForward, default_ntt_config(), output_cpu.get()); + print_array(output_cpu.get(), ntt_size); + + // Part 2: Running NTT on GPU + std::cout << "Part 2: compute on GPU (from/to CPU memory): " << std::endl; + icicle_set_device(device_gpu); + ntt_init_domain(root_of_unity, default_ntt_init_domain_config()); // Initialize NTT domain for GPU + ntt(input_cpu.get(), ntt_size, NTTDir::kForward, ntt_config, output_cpu.get()); + print_array(output_cpu.get(), ntt_size); + + // Allocate, copy data to GPU and compute on GPU memory + std::cout << "Part 2: compute on GPU (from/to GPU memory): " << std::endl; + scalar_t* input_gpu = nullptr; + scalar_t* output_gpu = nullptr; + icicle_malloc((void**)&input_gpu, ntt_size * sizeof(scalar_t)); + icicle_malloc((void**)&output_gpu, ntt_size * sizeof(scalar_t)); + icicle_copy(input_gpu, input_cpu.get(), ntt_size * sizeof(scalar_t)); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt(input_gpu, ntt_size, NTTDir::kForward, ntt_config, output_gpu); + icicle_copy(output_cpu.get(), output_gpu, ntt_size * sizeof(scalar_t)); + print_array(output_cpu.get(), ntt_size); + + // Part 3: Using both CPU and GPU to compute NTT (GPU) and inverse INTT (CPU) + auto output_intt_cpu = std::make_unique(ntt_size); + + // Step 1: Compute NTT on GPU + std::cout << "Part 3: compute NTT on GPU (NTT input): " << std::endl; + icicle_set_device(device_gpu); + ntt_config.are_inputs_on_device = false; // using host memory now + ntt_config.are_outputs_on_device = false; + ntt(input_cpu.get(), ntt_size, NTTDir::kForward, ntt_config, output_cpu.get()); + print_array(input_cpu.get(), ntt_size); + + // Step 2: Compute INTT on CPU + std::cout << "Part 3: compute INTT on CPU (INTT output): " << std::endl; + icicle_set_device(device_cpu); + ntt(output_cpu.get(), ntt_size, NTTDir::kInverse, ntt_config, output_intt_cpu.get()); + print_array(output_intt_cpu.get(), ntt_size); + + // Assert that INTT output is the same as NTT input + assert(0 == memcmp(input_cpu.get(), output_intt_cpu.get(), ntt_size * sizeof(scalar_t))); + + return 0; +} \ No newline at end of file diff --git a/examples/c++/install-and-use-icicle/run.sh b/examples/c++/install-and-use-icicle/run.sh new file mode 100755 index 000000000..c2bed7d1b --- /dev/null +++ b/examples/c++/install-and-use-icicle/run.sh @@ -0,0 +1 @@ +echo "Check out the README file. You will have to download an ICICLE release and follow instructions" diff --git a/examples/c++/msm/CMakeLists.txt b/examples/c++/msm/CMakeLists.txt index b29e07c79..d523b645c 100644 --- a/examples/c++/msm/CMakeLists.txt +++ b/examples/c++/msm/CMakeLists.txt @@ -1,23 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") +project(example) + +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_curve_bn254.a) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) diff --git a/examples/c++/msm/README.md b/examples/c++/msm/README.md index dbb62be6e..ca6d7e8da 100644 --- a/examples/c++/msm/README.md +++ b/examples/c++/msm/README.md @@ -1,46 +1,43 @@ # Icicle example: Muli-Scalar Multiplication (MSM) -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - ## Key-Takeaway `Icicle` provides CUDA C++ template function `MSM` to accelerate [Multi-Scalar Multiplication](https://github.com/ingonyama-zk/ingopedia/blob/master/src/msm.md). ## Concise Usage Explanation -1. Select the curve -2. Include an MSM template -3. Configure MSM -4. Call the template +1. Include the curve api +2. Configure MSM +3. Call msm api ```c++ -#define CURVE_ID 1 -#include "icicle/appUtils/msm/msm.cu" +#include "icicle/api/bn254.h" ... -msm::MSMConfig config = {...}; +MSMConfig config = default_msm_config(); ... -msm::MSM(scalars, points, size, config, &result); +bn254_msm(scalars, points, size, config, &result); ``` -In this example we use `BN254` curve (`CURVE_ID=1`). The function computes $result = \sum_{i=0}^{size-1} scalars[i] \cdot points[i]$, where input `points[]` use affine coordinates, and `result` uses projective coordinates. +In this example we use `BN254` curve. The function computes $result = \sum_{i=0}^{size-1} scalars[i] \cdot points[i]$, where input `points[]` use affine coordinates, and `result` uses projective coordinates. **Parameters:** -The configuration is passed to the kernel as a structure of type `msm::MSMConfig`. Some of the most important fields are listed below: +The configuration is passed to the kernel as a structure of type `MSMConfig`. Some of the most important fields are listed below: - `are_scalars_on_device`, `are_points_on_device`, `are_results_on_device`: location of the data - `is_async`: blocking vs. non-blocking kernel call -- `large_bucket_factor`: distinguishes between large bucket and normal bucket sizes. If there is a scalar distribution that is skewed heavily to a few values we can operate on those separately from the rest of the values. The ideal value here can vary by circuit (based on the distribution of scalars) but start with 10 and adjust to see if it improves performance. +- In addition can pass backend-specific params via config.extConfig. For example CUDA backend accepts a `large_bucket_factor` param. ## Running the example -- `cd` to your example directory -- compile with `./compile.sh` -- run with `./run.sh` +```sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir +``` ## What's in the example @@ -49,4 +46,4 @@ The configuration is passed to the kernel as a structure of type `msm::MSMConfig 3. Configure and execute MSM using on-host data 4. Copy inputs on-device 5. Configure and execute MSM using on-device data -6. Repeat the above steps for G2 points +6. Repeat step 3 G2 msm points diff --git a/examples/c++/msm/compile.sh b/examples/c++/msm/compile.sh deleted file mode 100755 index 7e8d781a0..000000000 --- a/examples/c++/msm/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DG2=ON -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/msm/example.cpp b/examples/c++/msm/example.cpp new file mode 100644 index 000000000..b3b3acc6f --- /dev/null +++ b/examples/c++/msm/example.cpp @@ -0,0 +1,101 @@ +#include +#include +#include + +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" +using namespace bn254; + +#include "examples_utils.h" + +int main(int argc, char* argv[]) +{ + try_load_and_set_backend_device(argc, argv); + + std::cout << "\nIcicle example: Muli-Scalar Multiplication (MSM)" << std::endl; + std::cout << "Example parameters" << std::endl; + + int batch_size = 1; + unsigned msm_size = 1 << 10; + int N = batch_size * msm_size; + std::cout << "Batch size: " << batch_size << std::endl; + std::cout << "MSM size: " << msm_size << std::endl; + + std::cout << "\nPart I: use G1 points" << std::endl; + + std::cout << "Generating random inputs on-host" << std::endl; + auto scalars = std::make_unique(N); + auto points = std::make_unique(N); + projective_t result; + scalar_t::rand_host_many(scalars.get(), N); + projective_t::rand_host_many(points.get(), N); + + std::cout << "Using default MSM configuration with on-host inputs" << std::endl; + + auto config = default_msm_config(); + config.batch_size = batch_size; + + std::cout << "\nRunning MSM kernel with on-host inputs" << std::endl; + // Execute the MSM kernel + START_TIMER(MSM_host_mem); + ICICLE_CHECK(bn254_msm(scalars.get(), points.get(), msm_size, &config, &result)); + END_TIMER(MSM_host_mem, "MSM from host-memory took"); + std::cout << projective_t::to_affine(result) << std::endl; + + DeviceProperties device_props; + ICICLE_CHECK(icicle_get_device_properties(device_props)); + // If device does not share memory with host, copy inputs explicitly and execute msm with device pointers + if (!device_props.using_host_memory) { + std::cout << "\nReconfiguring MSM to use on-device inputs" << std::endl; + config.are_results_on_device = true; + config.are_scalars_on_device = true; + config.are_points_on_device = true; + + std::cout << "Copying inputs to-device" << std::endl; + scalar_t* scalars_d; + affine_t* points_d; + projective_t* result_d; + + ICICLE_CHECK(icicle_malloc((void**)&scalars_d, sizeof(scalar_t) * N)); + ICICLE_CHECK(icicle_malloc((void**)&points_d, sizeof(affine_t) * N)); + ICICLE_CHECK(icicle_malloc((void**)&result_d, sizeof(projective_t))); + ICICLE_CHECK(icicle_copy(scalars_d, scalars.get(), sizeof(scalar_t) * N)); + ICICLE_CHECK(icicle_copy(points_d, points.get(), sizeof(affine_t) * N)); + + std::cout << "Running MSM kernel with on-device inputs" << std::endl; + // Execute the MSM kernel + START_TIMER(MSM_device_mem); + ICICLE_CHECK(msm(scalars_d, points_d, msm_size, config, result_d)); + END_TIMER(MSM_device_mem, "MSM from device-memory took"); + + // Copy the result back to the host + icicle_copy(&result, result_d, sizeof(projective_t)); + // Print the result + std::cout << projective_t::to_affine(result) << std::endl; + // Free the device memory + icicle_free(scalars_d); + icicle_free(points_d); + icicle_free(result_d); + } + + std::cout << "\nPart II: use G2 points" << std::endl; + + std::cout << "Generating random inputs on-host" << std::endl; + // use the same scalars + auto g2_points = std::make_unique(N); + g2_projective_t::rand_host_many(g2_points.get(), N); + + std::cout << "Reconfiguring MSM to use on-host inputs" << std::endl; + config.are_results_on_device = false; + config.are_scalars_on_device = false; + config.are_points_on_device = false; + g2_projective_t g2_result; + START_TIMER(MSM_g2); + ICICLE_CHECK(bn254_g2_msm(scalars.get(), g2_points.get(), msm_size, &config, &g2_result)); + END_TIMER(MSM_g2, "MSM G2 from host-memory took"); + std::cout << g2_projective_t::to_affine(g2_result) << std::endl; + + // Similar to G1 MSM, can explicitly copy to device and execute the G2 MSM using device pointers + + return 0; +} diff --git a/examples/c++/msm/example.cu b/examples/c++/msm/example.cu deleted file mode 100644 index abdc3f5cb..000000000 --- a/examples/c++/msm/example.cu +++ /dev/null @@ -1,124 +0,0 @@ -#include -#include -#include - -#include "api/bn254.h" -using namespace bn254; - -int main(int argc, char* argv[]) -{ - std::cout << "Icicle example: Muli-Scalar Multiplication (MSM)" << std::endl; - std::cout << "Example parameters" << std::endl; - int batch_size = 1; - std::cout << "Batch size: " << batch_size << std::endl; - unsigned msm_size = 1048576; - std::cout << "MSM size: " << msm_size << std::endl; - int N = batch_size * msm_size; - - std::cout << "Part I: use G1 points" << std::endl; - - std::cout << "Generating random inputs on-host" << std::endl; - scalar_t* scalars = new scalar_t[N]; - affine_t* points = new affine_t[N]; - projective_t result; - scalar_t::rand_host_many(scalars, N); - projective_t::rand_host_many_affine(points, N); - - std::cout << "Using default MSM configuration with on-host inputs" << std::endl; - device_context::DeviceContext ctx = device_context::get_default_device_context(); - msm::MSMConfig config = { - ctx, // ctx - 0, // points_size - 1, // precompute_factor - 0, // c - 0, // bitsize - 10, // large_bucket_factor - 1, // batch_size - false, // are_scalars_on_device - false, // are_scalars_montgomery_form - false, // are_points_on_device - false, // are_points_montgomery_form - false, // are_results_on_device - false, // is_big_triangle - false, // is_async - }; - config.batch_size = batch_size; - - std::cout << "Running MSM kernel with on-host inputs" << std::endl; - cudaStream_t stream = config.ctx.stream; - // Execute the MSM kernel - bn254_msm_cuda(scalars, points, msm_size, config, &result); - std::cout << projective_t::to_affine(result) << std::endl; - - std::cout << "Copying inputs on-device" << std::endl; - scalar_t* scalars_d; - affine_t* points_d; - projective_t* result_d; - cudaMalloc(&scalars_d, sizeof(scalar_t) * N); - cudaMalloc(&points_d, sizeof(affine_t) * N); - cudaMalloc(&result_d, sizeof(projective_t)); - cudaMemcpy(scalars_d, scalars, sizeof(scalar_t) * N, cudaMemcpyHostToDevice); - cudaMemcpy(points_d, points, sizeof(affine_t) * N, cudaMemcpyHostToDevice); - - std::cout << "Reconfiguring MSM to use on-device inputs" << std::endl; - config.are_results_on_device = true; - config.are_scalars_on_device = true; - config.are_points_on_device = true; - - std::cout << "Running MSM kernel with on-device inputs" << std::endl; - // Execute the MSM kernel - bn254_msm_cuda(scalars_d, points_d, msm_size, config, result_d); - - // Copy the result back to the host - cudaMemcpy(&result, result_d, sizeof(projective_t), cudaMemcpyDeviceToHost); - // Print the result - std::cout << projective_t::to_affine(result) << std::endl; - // Free the device memory - cudaFree(scalars_d); - cudaFree(points_d); - cudaFree(result_d); - // Free the host memory, keep scalars for G2 example - delete[] points; - - std::cout << "Part II: use G2 points" << std::endl; - - std::cout << "Generating random inputs on-host" << std::endl; - // use the same scalars - g2_affine_t* g2_points = new g2_affine_t[N]; - g2_projective_t::rand_host_many_affine(g2_points, N); - - std::cout << "Reconfiguring MSM to use on-host inputs" << std::endl; - config.are_results_on_device = false; - config.are_scalars_on_device = false; - config.are_points_on_device = false; - g2_projective_t g2_result; - bn254_g2_msm_cuda(scalars, g2_points, msm_size, config, &g2_result); - std::cout << g2_projective_t::to_affine(g2_result) << std::endl; - - std::cout << "Copying inputs on-device" << std::endl; - g2_affine_t* g2_points_d; - g2_projective_t* g2_result_d; - cudaMalloc(&scalars_d, sizeof(scalar_t) * N); - cudaMalloc(&g2_points_d, sizeof(g2_affine_t) * N); - cudaMalloc(&g2_result_d, sizeof(g2_projective_t)); - cudaMemcpy(scalars_d, scalars, sizeof(scalar_t) * N, cudaMemcpyHostToDevice); - cudaMemcpy(g2_points_d, g2_points, sizeof(g2_affine_t) * N, cudaMemcpyHostToDevice); - - std::cout << "Reconfiguring MSM to use on-device inputs" << std::endl; - config.are_results_on_device = true; - config.are_scalars_on_device = true; - config.are_points_on_device = true; - - std::cout << "Running MSM kernel with on-device inputs" << std::endl; - bn254_g2_msm_cuda(scalars_d, g2_points_d, msm_size, config, g2_result_d); - cudaMemcpy(&g2_result, g2_result_d, sizeof(g2_projective_t), cudaMemcpyDeviceToHost); - std::cout << g2_projective_t::to_affine(g2_result) << std::endl; - - cudaFree(scalars_d); - cudaFree(g2_points_d); - cudaFree(g2_result_d); - delete[] g2_points; - delete[] scalars; - cudaStreamDestroy(stream); - return 0; -} diff --git a/examples/c++/msm/run.sh b/examples/c++/msm/run.sh index 01eca66ba..9a5bdf29d 100755 --- a/examples/c++/msm/run.sh +++ b/examples/c++/msm/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/multi-gpu-poseidon/CMakeLists.txt b/examples/c++/multi-gpu-poseidon/CMakeLists.txt deleted file mode 100644 index 24746ce79..000000000 --- a/examples/c++/multi-gpu-poseidon/CMakeLists.txt +++ /dev/null @@ -1,27 +0,0 @@ -cmake_minimum_required(VERSION 3.18) -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) -set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(icicle LANGUAGES CUDA CXX) - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -# change the path to your Icicle location -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_curve_bn254.a) -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -find_library(NVML_LIBRARY nvidia-ml PATHS /usr/local/cuda/targets/x86_64-linux/lib/stubs/ ) -target_link_libraries(example ${NVML_LIBRARY}) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - diff --git a/examples/c++/multi-gpu-poseidon/README.md b/examples/c++/multi-gpu-poseidon/README.md deleted file mode 100644 index 5fb110fe6..000000000 --- a/examples/c++/multi-gpu-poseidon/README.md +++ /dev/null @@ -1,52 +0,0 @@ -# Icicle example: using multiple GPU to hash large dataset - -## Best-Practices - -This example builds on [single GPU Poseidon example](../poseidon/README.md) so we recommend to run it first. - -## Key-Takeaway - -Use `device_context::DeviceContext` variable to select GPU to use. -Use C++ threads to compute `Icicle` primitives on different GPUs in parallel. - -## Concise Usage Explanation - -1. Include c++ threads - -```c++ -#include -``` - -2. Define a __thread function__. Importantly, device context `ctx` will hold the GPU id. - -```c++ -void threadPoseidon(device_context::DeviceContext ctx, ...) {...} -``` - -3. Initialize device contexts for different GPUs - -```c++ -device_context::DeviceContext ctx0 = device_context::get_default_device_context(); -ctx0.device_id=0; -device_context::DeviceContext ctx1 = device_context::get_default_device_context(); -ctx1.device_id=1; -``` - -4. Finally, spawn the threads and wait for their completion - -```c++ -std::thread thread0(threadPoseidon, ctx0, ...); -std::thread thread1(threadPoseidon, ctx1, ...); -thread0.join(); -thread1.join(); -``` - -## What's in the example - -This is a **toy** example executing the first step of the Filecoin's Pre-Commit 2 phase: compute $2^{30}$ Poseison hashes for each column of $11 \times 2^{30}$ matrix. - -1. Define the size of the example: $2^{30}$ won't fit on a typical machine, so we partition the problem into `nof_partitions` -2. Hash two partitions in parallel on two GPUs -3. Hash two partitions in series on one GPU -4. Compare execution times - diff --git a/examples/c++/multi-gpu-poseidon/compile.sh b/examples/c++/multi-gpu-poseidon/compile.sh deleted file mode 100755 index ab4e191d9..000000000 --- a/examples/c++/multi-gpu-poseidon/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/multi-gpu-poseidon/example.cu b/examples/c++/multi-gpu-poseidon/example.cu deleted file mode 100644 index fcc3d5275..000000000 --- a/examples/c++/multi-gpu-poseidon/example.cu +++ /dev/null @@ -1,152 +0,0 @@ -#include -#include -#include -#include - -#include "api/bn254.h" -#include "gpu-utils/error_handler.cuh" - -#include "poseidon/poseidon.cuh" -#include "hash/hash.cuh" - -using namespace poseidon; -using namespace bn254; - -void checkCudaError(cudaError_t error) -{ - if (error != cudaSuccess) { - std::cerr << "CUDA error: " << cudaGetErrorString(error) << std::endl; - // Handle the error, e.g., exit the program or throw an exception. - } -} - -// these global constants go into template calls -const int size_col = 11; - -void threadPoseidon( - device_context::DeviceContext ctx, - unsigned size_partition, - scalar_t* layers, - scalar_t* column_hashes, - Poseidon * poseidon) -{ - cudaError_t err_result = CHK_STICKY(cudaSetDevice(ctx.device_id)); - if (err_result != cudaSuccess) { - std::cerr << "CUDA error: " << cudaGetErrorString(err_result) << std::endl; - return; - } - HashConfig column_config = default_hash_config(ctx); - cudaError_t err = poseidon->hash_many(layers, column_hashes, (size_t) size_partition, size_col, 1, column_config); - checkCudaError(err); -} - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -#define CHECK_ALLOC(ptr) \ - if ((ptr) == nullptr) { \ - std::cerr << "Memory allocation for '" #ptr "' failed." << std::endl; \ - exit(EXIT_FAILURE); \ - } - -#define CHECK_ALLOC(ptr) if ((ptr) == nullptr) { \ - std::cerr << "Memory allocation for '" #ptr "' failed." << std::endl; \ - exit(EXIT_FAILURE); \ -} - -int main() -{ - const unsigned size_row = (1 << 30); - const unsigned nof_partitions = 64; - const unsigned size_partition = size_row / nof_partitions; - // layers is allocated only for one partition, need to reuse for different partitions - const uint32_t size_layers = size_col * size_partition; - - nvmlInit(); - unsigned int deviceCount; - nvmlDeviceGetCount(&deviceCount); - std::cout << "Available GPUs: " << deviceCount << std::endl; - - for (unsigned int i = 0; i < deviceCount; ++i) { - nvmlDevice_t device; - nvmlMemory_t memory; - char name[NVML_DEVICE_NAME_BUFFER_SIZE]; - nvmlDeviceGetHandleByIndex(i, &device); - nvmlDeviceGetName(device, name, NVML_DEVICE_NAME_BUFFER_SIZE); - nvmlDeviceGetMemoryInfo(device, &memory); - std::cout << "Device ID: " << i << ", Type: " << name << ", Memory Total/Free (MiB) " << memory.total / 1024 / 1024 - << "/" << memory.free / 1024 / 1024 << std::endl; - } - - const unsigned memory_partition = sizeof(scalar_t) * (size_col + 1) * size_partition / 1024 / 1024; - std::cout << "Required Memory (MiB) " << memory_partition << std::endl; - - //=============================================================================== - // Key: multiple devices are supported by device context - //=============================================================================== - - device_context::DeviceContext ctx0 = device_context::get_default_device_context(); - ctx0.device_id = 0; - device_context::DeviceContext ctx1 = device_context::get_default_device_context(); - ctx1.device_id = 1; - - std::cout << "Allocate and initialize the memory for layers and hashes" << std::endl; - scalar_t* layers0 = static_cast(malloc(size_layers * sizeof(scalar_t))); - CHECK_ALLOC(layers0); - scalar_t s = scalar_t::zero(); - for (unsigned i = 0; i < size_col * size_partition; i++) { - layers0[i] = s; - s = s + scalar_t::one(); - } - scalar_t* layers1 = static_cast(malloc(size_layers * sizeof(scalar_t))); - CHECK_ALLOC(layers1); - s = scalar_t::zero() + scalar_t::one(); - for (unsigned i = 0; i < size_col * size_partition; i++) { - layers1[i] = s; - s = s + scalar_t::one(); - } - - scalar_t* column_hash0 = static_cast(malloc(size_partition * sizeof(scalar_t))); - CHECK_ALLOC(column_hash0); - scalar_t* column_hash1 = static_cast(malloc(size_partition * sizeof(scalar_t))); - CHECK_ALLOC(column_hash1); - - Poseidon column_poseidon0(size_col, ctx0); - cudaError_t err_result = CHK_STICKY(cudaSetDevice(ctx1.device_id)); - if (err_result != cudaSuccess) { - std::cerr << "CUDA error: " << cudaGetErrorString(err_result) << std::endl; - return; - } - Poseidon column_poseidon1(size_col, ctx1); - - std::cout << "Parallel execution of Poseidon threads" << std::endl; - START_TIMER(parallel); - std::thread thread0(threadPoseidon, ctx0, size_partition, layers0, column_hash0, &column_poseidon0); - std::thread thread1(threadPoseidon, ctx1, size_partition, layers1, column_hash1, &column_poseidon1); - - // Wait for the threads to finish - thread0.join(); - thread1.join(); - END_TIMER(parallel, "2 GPUs"); - std::cout << "Output Data from Thread 0: "; - std::cout << column_hash0[0] << std::endl; - std::cout << "Output Data from Thread 1: "; - std::cout << column_hash1[0] << std::endl; - - std::cout << "Sequential execution of Poseidon threads" << std::endl; - START_TIMER(sequential); - std::thread thread2(threadPoseidon, ctx0, size_partition, layers0, column_hash0, &column_poseidon0); - thread2.join(); - std::thread thread3(threadPoseidon, ctx0, size_partition, layers1, column_hash1, &column_poseidon0); - thread3.join(); - END_TIMER(sequential, "1 GPU"); - std::cout << "Output Data from Thread 2: "; - std::cout << column_hash0[0] << std::endl; - std::cout << "Output Data from Thread 3: "; - std::cout << column_hash1[0] << std::endl; - - nvmlShutdown(); - return 0; -} diff --git a/examples/c++/multi-gpu-poseidon/run.sh b/examples/c++/multi-gpu-poseidon/run.sh deleted file mode 100755 index 01eca66ba..000000000 --- a/examples/c++/multi-gpu-poseidon/run.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -./build/example/example diff --git a/examples/c++/multiply/.devcontainer/Dockerfile b/examples/c++/multiply/.devcontainer/Dockerfile deleted file mode 100644 index 770ad6fe4..000000000 --- a/examples/c++/multiply/.devcontainer/Dockerfile +++ /dev/null @@ -1,23 +0,0 @@ -# Make sure NVIDIA Container Toolkit is installed on your host - -# Use NVIDIA base image -FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 - -# Update and install dependencies -RUN apt-get update && apt-get install -y \ - nsight-systems-12.2 \ - cmake \ - protobuf-compiler \ - curl \ - build-essential \ - git \ - && rm -rf /var/lib/apt/lists/* - -# Clone Icicle from a GitHub repository -RUN git clone https://github.com/ingonyama-zk/icicle.git /icicle - -# Set the working directory in the container -WORKDIR /icicle-example - -# Specify the default command for the container -CMD ["/bin/bash"] diff --git a/examples/c++/multiply/.devcontainer/devcontainer.json b/examples/c++/multiply/.devcontainer/devcontainer.json deleted file mode 100644 index 2dd93aa30..000000000 --- a/examples/c++/multiply/.devcontainer/devcontainer.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "name": "Icicle Examples - Multiply", - "build": { - "dockerfile": "Dockerfile" - }, - "workspaceMount": "source=${localWorkspaceFolder}/.,target=/icicle-example,type=bind", - "workspaceFolder": "/icicle-example", - "runArgs": [ - "--gpus", - "all" - ], - "postCreateCommand": [ - "nvidia-smi" - ], - "customizations": { - "vscode": { - "extensions": [ - "ms-vscode.cmake-tools", - "ms-azuretools.vscode-docker", - "ms-vscode.cpptools-extension-pack" - ] - } - } -} diff --git a/examples/c++/multiply/CMakeLists.txt b/examples/c++/multiply/CMakeLists.txt deleted file mode 100644 index f7048bb8a..000000000 --- a/examples/c++/multiply/CMakeLists.txt +++ /dev/null @@ -1,25 +0,0 @@ -cmake_minimum_required(VERSION 3.18) -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) -set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -find_library(NVML_LIBRARY nvidia-ml PATHS /usr/local/cuda/targets/x86_64-linux/lib/stubs/ ) -target_link_libraries(example ${NVML_LIBRARY}) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - diff --git a/examples/c++/multiply/README.md b/examples/c++/multiply/README.md deleted file mode 100644 index 0e253d3af..000000000 --- a/examples/c++/multiply/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Icicle example: Multiplication - -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - -## Key-Takeaway - -`Icicle` accelerates multiplication operation `*` using [Karatsuba algorithm](https://en.wikipedia.org/wiki/Karatsuba_algorithm) - -## Concise Usage Explanation - -Define a `CURVE_ID` and include curve configuration header: - -```c++ -#define CURVE_ID 1 -#include "curves/curve_config.cuh" -``` - -The values of `CURVE_ID` for different curves are in the above header. Multiplication is accelerated both for field scalars and point fields. - -```c++ -using namespace curve_config; -scalar_t a; -point_field_t b; -``` - -## Running the example - -- `cd` to your example directory -- compile with `./compile.sh` -- run with `./run.sh` - -## What's in the example - -1. Define the parameters for the example such as vector size -2. Generate random vectors on-host -3. Copy them on-device -4. Execute element-wise vector multiplication on-device -5. Copy results on-host - diff --git a/examples/c++/multiply/compile.sh b/examples/c++/multiply/compile.sh deleted file mode 100755 index de35c62da..000000000 --- a/examples/c++/multiply/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DMSM=OFF -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/multiply/example.cu b/examples/c++/multiply/example.cu deleted file mode 100644 index 77eb7a0b4..000000000 --- a/examples/c++/multiply/example.cu +++ /dev/null @@ -1,162 +0,0 @@ -#include -#include -#include -#include - -#include "api/bn254.h" -#include "vec_ops/vec_ops.cuh" - -using namespace vec_ops; -using namespace bn254; - -typedef scalar_t T; - -int vector_mult(T* vec_b, T* vec_a, T* vec_result, size_t n_elments, device_context::DeviceContext ctx) -{ - vec_ops::VecOpsConfig config = vec_ops::DefaultVecOpsConfig(); - config.is_a_on_device = true; - config.is_b_on_device = true; - config.is_result_on_device = true; - cudaError_t err = bn254_mul_cuda(vec_a, vec_b, n_elments, config, vec_result); - if (err != cudaSuccess) { - std::cerr << "Failed to multiply vectors - " << cudaGetErrorString(err) << std::endl; - return 0; - } - return 0; -} - -int main(int argc, char** argv) -{ - const unsigned vector_size = 1 << 15; - const unsigned repetitions = 1 << 15; - - cudaError_t err; - nvmlInit(); - nvmlDevice_t device; - nvmlDeviceGetHandleByIndex(0, &device); // for GPU 0 - std::cout << "Icicle-Examples: vector multiplications" << std::endl; - char name[NVML_DEVICE_NAME_BUFFER_SIZE]; - if (nvmlDeviceGetName(device, name, NVML_DEVICE_NAME_BUFFER_SIZE) == NVML_SUCCESS) { - std::cout << "GPU Model: " << name << std::endl; - } else { - std::cerr << "Failed to get GPU model name." << std::endl; - } - unsigned power_limit; - nvmlDeviceGetPowerManagementLimit(device, &power_limit); - - std::cout << "Vector size: " << vector_size << std::endl; - std::cout << "Repetitions: " << repetitions << std::endl; - std::cout << "Power limit: " << std::fixed << std::setprecision(3) << 1.0e-3 * power_limit << " W" << std::endl; - - unsigned int baseline_power; - nvmlDeviceGetPowerUsage(device, &baseline_power); - std::cout << "Baseline power: " << std::fixed << std::setprecision(3) << 1.0e-3 * baseline_power << " W" << std::endl; - unsigned baseline_temperature; - if (nvmlDeviceGetTemperature(device, NVML_TEMPERATURE_GPU, &baseline_temperature) == NVML_SUCCESS) { - std::cout << "Baseline GPU Temperature: " << baseline_temperature << " C" << std::endl; - } else { - std::cerr << "Failed to get GPU temperature." << std::endl; - } - - // host data - T* host_in1 = (T*)malloc(vector_size * sizeof(T)); - T* host_in2 = (T*)malloc(vector_size * sizeof(T)); - std::cout << "Initializing vectors with random data" << std::endl; - T::rand_host_many(host_in1, vector_size); - T::rand_host_many(host_in2, vector_size); - // device data - device_context::DeviceContext ctx = device_context::get_default_device_context(); - T* device_in1; - T* device_in2; - T* device_out; - - err = cudaMalloc((void**)&device_in1, vector_size * sizeof(T)); - if (err != cudaSuccess) { - std::cerr << "Failed to allocate device memory - " << cudaGetErrorString(err) << std::endl; - return 0; - } - - err = cudaMalloc((void**)&device_in2, vector_size * sizeof(T)); - if (err != cudaSuccess) { - std::cerr << "Failed to allocate device memory - " << cudaGetErrorString(err) << std::endl; - return 0; - } - - err = cudaMalloc((void**)&device_out, vector_size * sizeof(T)); - if (err != cudaSuccess) { - std::cerr << "Failed to allocate device memory - " << cudaGetErrorString(err) << std::endl; - return 0; - } - - // copy from host to device - err = cudaMemcpy(device_in1, host_in1, vector_size * sizeof(T), cudaMemcpyHostToDevice); - if (err != cudaSuccess) { - std::cerr << "Failed to copy data from host to device - " << cudaGetErrorString(err) << std::endl; - return 0; - } - - err = cudaMemcpy(device_in2, host_in2, vector_size * sizeof(T), cudaMemcpyHostToDevice); - if (err != cudaSuccess) { - std::cerr << "Failed to copy data from host to device - " << cudaGetErrorString(err) << std::endl; - return 0; - } - - std::cout << "Starting warm-up" << std::endl; - // Warm-up loop - for (int i = 0; i < repetitions; i++) { - vector_mult(device_in1, device_in2, device_out, vector_size, ctx); - } - - std::cout << "Starting benchmarking" << std::endl; - unsigned power_before; - nvmlDeviceGetPowerUsage(device, &power_before); - std::cout << "Power before: " << std::fixed << std::setprecision(3) << 1.0e-3 * power_before << " W" << std::endl; - std::cout << "Power utilization: " << std::fixed << std::setprecision(1) << (float)100.0 * power_before / power_limit - << " %" << std::endl; - unsigned temperature_before; - if (nvmlDeviceGetTemperature(device, NVML_TEMPERATURE_GPU, &temperature_before) == NVML_SUCCESS) { - std::cout << "GPU Temperature before: " << temperature_before << " C" << std::endl; - } else { - std::cerr << "Failed to get GPU temperature." << std::endl; - } - auto start_time = std::chrono::high_resolution_clock::now(); - // Benchmark loop - for (int i = 0; i < repetitions; i++) { - vector_mult(device_in1, device_in2, device_out, vector_size, ctx); - } - auto end_time = std::chrono::high_resolution_clock::now(); - auto duration = std::chrono::duration_cast(end_time - start_time); - std::cout << "Elapsed time: " << duration.count() << " microseconds" << std::endl; - unsigned power_after; - nvmlDeviceGetPowerUsage(device, &power_after); - std::cout << "Power after: " << std::fixed << std::setprecision(3) << 1.0e-3 * power_after << " W" << std::endl; - std::cout << "Power utilization: " << std::fixed << std::setprecision(1) << (float)100.0 * power_after / power_limit - << " %" << std::endl; - unsigned temperature_after; - if (nvmlDeviceGetTemperature(device, NVML_TEMPERATURE_GPU, &temperature_after) == NVML_SUCCESS) { - std::cout << "GPU Temperature after: " << temperature_after << " C" << std::endl; - } else { - std::cerr << "Failed to get GPU temperature." << std::endl; - } - - // Report performance in GMPS: Giga Multiplications Per Second - double GMPS = 1.0e-9 * repetitions * vector_size / (1.0e-6 * duration.count()); - std::cout << "Performance: " << GMPS << " Giga Multiplications Per Second" << std::endl; - - // Optional: validate multiplication - T* host_out = (T*)malloc(vector_size * sizeof(T)); - - cudaMemcpy(host_out, device_out, vector_size * sizeof(T), cudaMemcpyDeviceToHost); - - // validate multiplication here... - - // clean up and exit - free(host_in1); - free(host_in2); - free(host_out); - cudaFree(device_in1); - cudaFree(device_in2); - cudaFree(device_out); - nvmlShutdown(); - return 0; -} \ No newline at end of file diff --git a/examples/c++/multiply/run.sh b/examples/c++/multiply/run.sh deleted file mode 100755 index 01eca66ba..000000000 --- a/examples/c++/multiply/run.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -./build/example/example diff --git a/examples/c++/ntt/CMakeLists.txt b/examples/c++/ntt/CMakeLists.txt index de0a1a22d..d523b645c 100644 --- a/examples/c++/ntt/CMakeLists.txt +++ b/examples/c++/ntt/CMakeLists.txt @@ -1,23 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") +project(example) + +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) \ No newline at end of file diff --git a/examples/c++/ntt/README.md b/examples/c++/ntt/README.md index 28e8dd451..f506eba99 100644 --- a/examples/c++/ntt/README.md +++ b/examples/c++/ntt/README.md @@ -1,33 +1,35 @@ # Icicle example: Number-Theoretical Transform (NTT) -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - ## Key-Takeaway `Icicle` provides CUDA C++ template function NTT for [Number Theoretical Transform](https://github.com/ingonyama-zk/ingopedia/blob/master/src/fft.md), also known as Discrete Fourier Transform. ## Concise Usage Explanation +1. Include the curve api +2. Init NTT domain +3. Call ntt api + ```c++ -// Select the curve -#define CURVE_ID 1 -// Include NTT template -#include "appUtils/ntt/ntt.cu" -using namespace curve_config; -using namespace ntt; -// Configure NTT -NTTConfig config=DefaultNTTConfig(); -// Call NTT -NTT(input, ntt_size, NTTDir::kForward, config, output); +#include "icicle/api/bn254.h" +... +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +... +bn254_ntt_init_domain(&basic_root, ntt_init_domain_cfg); +NTTConfig config = default_ntt_config(); +... +bn254_ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()) ``` + ## Running the example -- `cd` to your example directory -- compile with `./compile.sh` -- run with `./run.sh` +```sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir +``` ## What's in the example diff --git a/examples/c++/ntt/compile.sh b/examples/c++/ntt/compile.sh deleted file mode 100755 index de35c62da..000000000 --- a/examples/c++/ntt/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DMSM=OFF -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/ntt/example.cpp b/examples/c++/ntt/example.cpp new file mode 100644 index 000000000..67aeb7963 --- /dev/null +++ b/examples/c++/ntt/example.cpp @@ -0,0 +1,106 @@ +#include + +#include "icicle/runtime.h" + +#include "icicle/api/bn254.h" +using namespace bn254; + +#include "examples_utils.h" +#include "icicle/backend/ntt_config.h" + +void initialize_input(const unsigned ntt_size, const unsigned nof_ntts, scalar_t* elements); +int validate_output(const unsigned ntt_size, const unsigned nof_ntts, scalar_t* elements); + +int main(int argc, char* argv[]) +{ + try_load_and_set_backend_device(argc, argv); + + std::cout << "\nIcicle Examples: Number Theoretical Transform (NTT)" << std::endl; + const unsigned log_ntt_size = 20; + const unsigned ntt_size = 1 << log_ntt_size; + const unsigned batch_size = 2; + + std::cout << "Example parameters:" << std::endl; + std::cout << "NTT size: " << ntt_size << std::endl; + std::cout << "batch size: " << batch_size << std::endl; + + std::cout << "\nGenerating input data for lowest and highest harmonics" << std::endl; + auto input = std::make_unique(batch_size * ntt_size); + auto output = std::make_unique(batch_size * ntt_size); + initialize_input(ntt_size, batch_size, input.get()); + + // Initialize NTT domain + std::cout << "\nInit NTT domain" << std::endl; + scalar_t basic_root = scalar_t::omega(log_ntt_size /*NTT_LOG_SIZscalar_t*/); + auto ntt_init_domain_cfg = default_ntt_init_domain_config(); + ConfigExtension backend_cfg_ext; + backend_cfg_ext.set( + CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); // optionally construct fast_twiddles for CUDA backend + ntt_init_domain_cfg.ext = &backend_cfg_ext; + ICICLE_CHECK(bn254_ntt_init_domain(&basic_root, &ntt_init_domain_cfg)); + + // ntt configuration + NTTConfig config = default_ntt_config(); + ConfigExtension ntt_cfg_ext; + config.ext = &ntt_cfg_ext; + config.batch_size = batch_size; + + // warmup + ICICLE_CHECK(bn254_ntt(input.get(), ntt_size, NTTDir::kForward, &config, output.get())); + + // NTT radix-2 alg + std::cout << "\nRunning NTT radix-2 alg with on-host data" << std::endl; + ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::Radix2); + START_TIMER(Radix2); + ICICLE_CHECK(bn254_ntt(input.get(), ntt_size, NTTDir::kForward, &config, output.get())); + END_TIMER(Radix2, "Radix2 NTT"); + + std::cout << "Validating output" << std::endl; + validate_output(ntt_size, batch_size, output.get()); + + // NTT mixed-radix alg + std::cout << "\nRunning NTT mixed-radix alg with on-host data" << std::endl; + ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); + START_TIMER(MixedRadix); + ICICLE_CHECK(bn254_ntt(input.get(), ntt_size, NTTDir::kForward, &config, output.get())); + END_TIMER(MixedRadix, "MixedRadix NTT"); + + std::cout << "Validating output" << std::endl; + validate_output(ntt_size, batch_size, output.get()); + + return 0; +} + +void initialize_input(const unsigned ntt_size, const unsigned nof_ntts, scalar_t* elements) +{ + // Lowest Harmonics + for (unsigned i = 0; i < ntt_size; i = i + 1) { + elements[i] = scalar_t::one(); + } + // Highest Harmonics + for (unsigned i = 1 * ntt_size; i < 2 * ntt_size; i = i + 2) { + elements[i] = scalar_t::one(); + elements[i + 1] = scalar_t::neg(scalar_t::one()); + } +} + +int validate_output(const unsigned ntt_size, const unsigned nof_ntts, scalar_t* elements) +{ + int nof_errors = 0; + scalar_t amplitude = scalar_t::from((uint32_t)ntt_size); + // Lowest Harmonics + if (elements[0] != amplitude) { + ++nof_errors; + std::cout << "Error in lowest harmonicscalar_t 0! " << std::endl; + } else { + std::cout << "Validated lowest harmonics" << std::endl; + } + // Highest Harmonics + if (elements[1 * ntt_size + ntt_size / 2] != amplitude) { + ++nof_errors; + std::cout << "Error in highest harmonics! " << std::endl; + } else { + std::cout << "Validated highest harmonics" << std::endl; + } + return nof_errors; +} \ No newline at end of file diff --git a/examples/c++/ntt/example.cu b/examples/c++/ntt/example.cu deleted file mode 100644 index 5e50f0dd7..000000000 --- a/examples/c++/ntt/example.cu +++ /dev/null @@ -1,113 +0,0 @@ -#include -#include - -// include NTT template - -#include "curves/params/bn254.cuh" -#include "api/bn254.h" -using namespace bn254; -using namespace ntt; - -// Operate on scalars -typedef scalar_t S; -typedef scalar_t E; - -void print_elements(const unsigned n, E* elements) -{ - for (unsigned i = 0; i < n; i++) { - std::cout << i << ": " << elements[i] << std::endl; - } -} - -void initialize_input(const unsigned ntt_size, const unsigned nof_ntts, E* elements) -{ - // Lowest Harmonics - for (unsigned i = 0; i < ntt_size; i = i + 1) { - elements[i] = E::one(); - } - // print_elements(ntt_size, elements ); - // Highest Harmonics - for (unsigned i = 1 * ntt_size; i < 2 * ntt_size; i = i + 2) { - elements[i] = E::one(); - elements[i + 1] = E::neg(scalar_t::one()); - } - // print_elements(ntt_size, &elements[1*ntt_size] ); -} - -int validate_output(const unsigned ntt_size, const unsigned nof_ntts, E* elements) -{ - int nof_errors = 0; - E amplitude = E::from((uint32_t)ntt_size); - // std::cout << "Amplitude: " << amplitude << std::endl; - // Lowest Harmonics - if (elements[0] != amplitude) { - ++nof_errors; - std::cout << "Error in lowest harmonics 0! " << std::endl; - // print_elements(ntt_size, elements ); - } else { - std::cout << "Validated lowest harmonics" << std::endl; - } - // Highest Harmonics - if (elements[1 * ntt_size + ntt_size / 2] != amplitude) { - ++nof_errors; - std::cout << "Error in highest harmonics! " << std::endl; - // print_elements(ntt_size, &elements[1*ntt_size] ); - } else { - std::cout << "Validated highest harmonics" << std::endl; - } - return nof_errors; -} - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - std::cout << "Icicle Examples: Number Theoretical Transform (NTT)" << std::endl; - std::cout << "Example parameters" << std::endl; - const unsigned log_ntt_size = 20; - std::cout << "Log2(NTT size): " << log_ntt_size << std::endl; - const unsigned ntt_size = 1 << log_ntt_size; - std::cout << "NTT size: " << ntt_size << std::endl; - const unsigned nof_ntts = 2; - std::cout << "Number of NTTs: " << nof_ntts << std::endl; - const unsigned batch_size = nof_ntts * ntt_size; - - std::cout << "Generating input data for lowest and highest harmonics" << std::endl; - E* input; - input = (E*)malloc(sizeof(E) * batch_size); - initialize_input(ntt_size, nof_ntts, input); - E* output; - output = (E*)malloc(sizeof(E) * batch_size); - - std::cout << "Running NTT with on-host data" << std::endl; - // Create a device context - auto ctx = device_context::get_default_device_context(); - S basic_root = S::omega(log_ntt_size /*NTT_LOG_SIZE*/); - bn254_initialize_domain(&basic_root, ctx, true); - // Create an NTTConfig instance - NTTConfig config = default_ntt_config(); - config.ntt_algorithm = NttAlgorithm::MixedRadix; - config.batch_size = nof_ntts; - START_TIMER(MixedRadix); - cudaError_t err = bn254_ntt_cuda(input, ntt_size, NTTDir::kForward, config, output); - END_TIMER(MixedRadix, "MixedRadix NTT"); - - std::cout << "Validating output" << std::endl; - validate_output(ntt_size, nof_ntts, output); - - config.ntt_algorithm = NttAlgorithm::Radix2; - START_TIMER(Radix2); - err = bn254_ntt_cuda(input, ntt_size, NTTDir::kForward, config, output); - END_TIMER(Radix2, "Radix2 NTT"); - - std::cout << "Validating output" << std::endl; - validate_output(ntt_size, nof_ntts, output); - - std::cout << "Cleaning-up memory" << std::endl; - free(input); - free(output); - return 0; -} diff --git a/examples/c++/ntt/run.sh b/examples/c++/ntt/run.sh index ce22b116a..d3e9edc35 100755 --- a/examples/c++/ntt/run.sh +++ b/examples/c++/ntt/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example \ No newline at end of file + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DMSM=OFF -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/pedersen-commitment/CMakeLists.txt b/examples/c++/pedersen-commitment/CMakeLists.txt index b559716ef..d523b645c 100644 --- a/examples/c++/pedersen-commitment/CMakeLists.txt +++ b/examples/c++/pedersen-commitment/CMakeLists.txt @@ -1,26 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -add_executable( - example - example.cu -) +project(example) + +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_curve_bn254.a) -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -find_library(NVML_LIBRARY nvidia-ml PATHS /usr/local/cuda/targets/x86_64-linux/lib/stubs/ ) -target_link_libraries(example ${NVML_LIBRARY}) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) diff --git a/examples/c++/pedersen-commitment/README.md b/examples/c++/pedersen-commitment/README.md index c9b8693f0..97dffa20c 100644 --- a/examples/c++/pedersen-commitment/README.md +++ b/examples/c++/pedersen-commitment/README.md @@ -1,9 +1,5 @@ # ICICLE example: Pedersen Commitment -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - ## Key-Takeaway A Pedersen Commitment is a cryptographic primitive to commit to a value or a vector of values while keeping it hidden, yet enabling the committer to reveal the value later. It provides both hiding (the commitment does not reveal any information about the value) and binding properties (once a value is committed, it cannot be changed without detection). @@ -14,10 +10,12 @@ An example of MSM is [here](../msm/README.md). ## Running the example -- `cd` to your example directory -- compile with `./compile.sh` -- run with `./run.sh` - +```sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir +``` ## Concise Explanation We recommend this simple [explanation](https://www.rareskills.io/post/pedersen-commitment). diff --git a/examples/c++/pedersen-commitment/compile.sh b/examples/c++/pedersen-commitment/compile.sh deleted file mode 100755 index ab4e191d9..000000000 --- a/examples/c++/pedersen-commitment/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/pedersen-commitment/example.cu b/examples/c++/pedersen-commitment/example.cpp similarity index 82% rename from examples/c++/pedersen-commitment/example.cu rename to examples/c++/pedersen-commitment/example.cpp index 106a47df1..e8afabdb7 100644 --- a/examples/c++/pedersen-commitment/example.cu +++ b/examples/c++/pedersen-commitment/example.cpp @@ -1,16 +1,16 @@ #include #include -#include #include -#include -#include "api/bn254.h" -#include "msm/msm.cuh" +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" +#include "icicle/curves/params/bn254.h" using namespace bn254; -typedef point_field_t T; +#include "examples_utils.h" // modular power +template T modPow(T base, T exp) { T r = T::one(); @@ -27,10 +27,15 @@ T modPow(T base, T exp) } // Check if y2 is a quadratic residue using Euler's Criterion -bool quadratic_residue(T y2) { return modPow(y2, T::div2(T::zero() - T::one())) == T::one(); } +template +bool quadratic_residue(T y2) +{ + return modPow(y2, T::div2(T::zero() - T::one())) == T::one(); +} // modular square root adapted from: // https://github.com/ShahjalalShohag/code-library/blob/main/Number%20Theory/Tonelli%20Shanks%20Algorithm.cpp +template bool mySQRT(T a, T* result) { if (a == T::zero()) { @@ -72,9 +77,10 @@ bool mySQRT(T a, T* result) } } +template void point_near_x(T x, affine_t* point) { - const T wb = T{weierstrass_b}; + const T wb = T{G1::weierstrass_b}; T y2; while (y2 = x * x * x + wb, quadratic_residue(y2) == false) { x = x + T::one(); @@ -87,7 +93,8 @@ void point_near_x(T x, affine_t* point) } static int seed = 0; -static HOST_INLINE T rand_host_seed() +template +static T rand_host_seed() { std::mt19937_64 generator(seed++); std::uniform_int_distribution distribution; @@ -101,16 +108,13 @@ static HOST_INLINE T rand_host_seed() return value; } -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - int main(int argc, char** argv) { + try_load_and_set_backend_device(argc, argv); + const unsigned N = pow(2, 10); std::cout << "Commitment vector size: " << N << "+1 for salt (a.k.a blinding factor)" << std::endl; - T* xs = new T[N + 1]; + point_field_t* xs = new point_field_t[N + 1]; std::cout << "Generating random points transparently using publicly chosen seed" << std::endl; std::cout << "Public seed prevents committer from knowing the discrete logs of points used in the commitment" @@ -119,9 +123,8 @@ int main(int argc, char** argv) std::cout << "Using seed: " << seed << std::endl; std::cout << "Generating random field values" << std::endl; START_TIMER(gen); - for (unsigned i = 0; i < N; i++) { - xs[i] = rand_host_seed(); + xs[i] = rand_host_seed(); } END_TIMER(gen, "Time to generate field values"); std::cout << "xs[0]: " << xs[0] << std::endl; @@ -145,9 +148,9 @@ int main(int argc, char** argv) scalars[N] = scalar_t::rand_host(); std::cout << "Executing MSM" << std::endl; - auto config = msm::default_msm_config(); + auto config = default_msm_config(); START_TIMER(msm); - bn254_msm_cuda(scalars, points, N + 1, config, &result); + bn254_msm(scalars, points, N + 1, &config, &result); END_TIMER(msm, "Time to execute MSM"); std::cout << "Computed commitment: " << result << std::endl; diff --git a/examples/c++/pedersen-commitment/run.sh b/examples/c++/pedersen-commitment/run.sh index 01eca66ba..8bed8b8c2 100755 --- a/examples/c++/pedersen-commitment/run.sh +++ b/examples/c++/pedersen-commitment/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/polynomial-api/CMakeLists.txt b/examples/c++/polynomial-api/CMakeLists.txt index 616e5188d..d523b645c 100644 --- a/examples/c++/polynomial-api/CMakeLists.txt +++ b/examples/c++/polynomial-api/CMakeLists.txt @@ -1,30 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr -DCURVE_ID=BN254") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -add_executable( - example - example.cu -) +project(example) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -target_include_directories(example PRIVATE "../../../icicle/include") +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -# can link to another curve/field by changing the following lib and FIELD_ID -target_link_libraries(example -${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_curve_bn254.a -${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a -) -target_compile_definitions(example PUBLIC FIELD_ID BN254) \ No newline at end of file diff --git a/examples/c++/polynomial-api/README.md b/examples/c++/polynomial-api/README.md index 5e12ccb00..d1b214f62 100644 --- a/examples/c++/polynomial-api/README.md +++ b/examples/c++/polynomial-api/README.md @@ -1,9 +1,5 @@ # ICICLE examples: computations with polynomials -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - ## Key-Takeaway Polynomials are crucial for Zero-Knowledge Proofs (ZKPs): they enable efficient representation and verification of computational statements, facilitate privacy-preserving protocols, and support complex mathematical operations essential for constructing and verifying proofs without revealing underlying data. Polynomial API is documented [here](https://dev.ingonyama.com/icicle/polynomials/overview) @@ -13,9 +9,10 @@ Polynomials are crucial for Zero-Knowledge Proofs (ZKPs): they enable efficient To run example, from project root directory: ```sh -cd examples/c++/polynomial-api -./compile.sh -./run.sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir ``` To change the scalar field, modify `compile.h` to build the corresponding lib and `CMakeLists.txt` to link to that lib and set `FIELD_ID` correspondingly. diff --git a/examples/c++/polynomial-api/compile.sh b/examples/c++/polynomial-api/compile.sh deleted file mode 100755 index 78dd11a8a..000000000 --- a/examples/c++/polynomial-api/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DG2=OFF -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/polynomial-api/example.cu b/examples/c++/polynomial-api/example.cpp similarity index 79% rename from examples/c++/polynomial-api/example.cu rename to examples/c++/polynomial-api/example.cpp index 762298346..06e33fa5c 100644 --- a/examples/c++/polynomial-api/example.cu +++ b/examples/c++/polynomial-api/example.cpp @@ -1,14 +1,13 @@ #include #include -#include "polynomials/polynomials.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" -#include "ntt/ntt.cuh" -#include "api/bn254.h" -#include +#include "icicle/api/bn254.h" +#include "icicle/polynomials/polynomials.h" -using namespace polynomials; -using namespace bn254; +#include "examples_utils.h" + +using namespace icicle; +using namespace bn254; // typedef scalar_t as bn254-scalar type // define the polynomial type typedef Polynomial Polynomial_t; @@ -22,27 +21,28 @@ const auto four = scalar_t::from(4); const auto five = scalar_t::from(5); const auto minus_one = zero - one; -static std::unique_ptr generate_pows(scalar_t tau, uint32_t size){ - auto vec = std::make_unique(size); - vec[0] = scalar_t::one(); - for (size_t i = 1; i < size; ++i) { - vec[i] = vec[i-1] * tau; +static std::unique_ptr generate_pows(scalar_t tau, uint32_t size) +{ + auto vec = std::make_unique(size); + vec[0] = scalar_t::one(); + for (size_t i = 1; i < size; ++i) { + vec[i] = vec[i - 1] * tau; } return std::move(vec); } -static std::unique_ptr generate_SRS(uint32_t size) { +static std::unique_ptr generate_SRS(uint32_t size) +{ auto secret_scalar = scalar_t::rand_host(); auto gen = projective_t::generator(); - auto pows_of_tau = generate_pows(secret_scalar,size); + auto pows_of_tau = generate_pows(secret_scalar, size); auto SRS = std::make_unique(size); for (size_t i = 0; i < size; ++i) { - SRS[i] = projective_t::to_affine(pows_of_tau[i] * gen); + SRS[i] = projective_t::to_affine(pows_of_tau[i] * gen); } return std::move(SRS); } - void example_evaluate() { std::cout << std::endl << "Example: Polynomial evaluation on random value" << std::endl; @@ -310,122 +310,120 @@ void example_device_memory_view() const int log_size = 6; const int size = 1 << log_size; auto f = randomize_polynomial(size); - auto [d_coeffs, N, device_id] = f.get_coefficients_view(); + auto [d_coeffs, N] = f.get_coefficients_view(); // compute coset evaluations auto coset_evals = std::make_unique(size); - auto ntt_config = ntt::default_ntt_config(); + auto ntt_config = default_ntt_config(); ntt_config.are_inputs_on_device = true; // using the device data directly as a view - ntt_config.coset_gen = ntt::get_root_of_unity(size * 2); - ntt::ntt(d_coeffs.get(), size, ntt::NTTDir::kForward, ntt_config, coset_evals.get()); + ICICLE_CHECK(get_root_of_unity(size * 2, &ntt_config.coset_gen)); + ntt(d_coeffs.get(), size, NTTDir::kForward, ntt_config, coset_evals.get()); } - void example_commit_with_device_memory_view() { - //declare time vars + // declare time vars std::chrono::time_point start, end; std::chrono::milliseconds duration; - std::cout << std::endl << "Example: a) commit with Polynomial views [(f1+f2)^2 + (f1-f2)^2 ]_1 = [4 (f1^2+ f_2^2)]_1" << std::endl; - std::cout<< "Example: b) commit with Polynomial views [(f1+f2)^2 - (f1-f2)^2 ]_1 = [4 f1 *f_2]_1" << std::endl; + std::cout << std::endl + << "Example: a) commit with Polynomial views [(f1+f2)^2 + (f1-f2)^2 ]_1 = [4 (f1^2+ f_2^2)]_1" << std::endl; + std::cout << "Example: b) commit with Polynomial views [(f1+f2)^2 - (f1-f2)^2 ]_1 = [4 f1 *f_2]_1" << std::endl; int N = 1025; - //generate group elements string of length N: (1, beta,beta^2....,beta^{N-1}). g + // generate group elements string of length N: (1, beta,beta^2....,beta^{N-1}). g std::cout << "Setup: Generating mock SRS" << std::endl; start = std::chrono::high_resolution_clock::now(); - auto SRS = generate_SRS(2*N); - //Allocate memory on device (points) + auto SRS = generate_SRS(2 * N); + // Allocate memory on device (points) affine_t* points_d; - cudaMalloc(&points_d, sizeof(affine_t)* 2 * N); + ICICLE_CHECK(icicle_malloc((void**)&points_d, sizeof(affine_t) * 2 * N)); // copy SRS to device (could have generated on device, but gives an indicator) - cudaMemcpy(points_d, SRS.get(), sizeof(affine_t)* 2 * N, cudaMemcpyHostToDevice); + ICICLE_CHECK(icicle_copy(points_d, SRS.get(), sizeof(affine_t) * 2 * N)); end = std::chrono::high_resolution_clock::now(); duration = std::chrono::duration_cast(end - start); - std::cout << "Setup: SRS of length "<< N << " generated and loaded to device. Took: " << duration.count() << " milliseconds" << std::endl; - - //goal: - //test commitment equality [(f1+f2)^2 + (f1-f2)^2 ]_1 = [4 (f1^2+ f_2^2)]_1 - //test commitment equality [(f1+f2)^2 - (f1-f2)^2 ]_1 = [4 f1 *f_2]_1 - //note: using polyapi to gen scalars: already on device. - std::cout << "Setup: Generating polys (on device) f1,f2 of log degree " << log2(N-1) << std::endl; + std::cout << "Setup: SRS of length " << N << " generated and loaded to device. Took: " << duration.count() + << " milliseconds" << std::endl; + + // goal: + // test commitment equality [(f1+f2)^2 + (f1-f2)^2 ]_1 = [4 (f1^2+ f_2^2)]_1 + // test commitment equality [(f1+f2)^2 - (f1-f2)^2 ]_1 = [4 f1 *f_2]_1 + // note: using polyapi to gen scalars: already on device. + std::cout << "Setup: Generating polys (on device) f1,f2 of log degree " << log2(N - 1) << std::endl; start = std::chrono::high_resolution_clock::now(); auto f1 = randomize_polynomial(N); auto f2 = randomize_polynomial(N); end = std::chrono::high_resolution_clock::now(); duration = std::chrono::duration_cast(end - start); std::cout << "Setup: Gen poly done. Took: " << duration.count() << " milliseconds" << std::endl; - - //deg 2N constraints (f1+f2)^2 + (f1-f2)^2 = 2 (f1^2+ f_2^2) - std::cout << "Computing constraints..start "<< std::endl; + + // deg 2N constraints (f1+f2)^2 + (f1-f2)^2 = 2 (f1^2+ f_2^2) + std::cout << "Computing constraints..start " << std::endl; start = std::chrono::high_resolution_clock::now(); - auto L1 = (f1+f2)*(f1+f2) + (f1-f2)*(f1-f2); - auto R1 = scalar_t::from(2) * (f1*f1 + f2*f2); - //deg 2N constraints (f1+f2)^2 - (f1-f2)^2 = 4 f1 *f_2 - auto L2 = (f1+f2)*(f1+f2) - (f1-f2)*(f1-f2); + auto L1 = (f1 + f2) * (f1 + f2) + (f1 - f2) * (f1 - f2); + auto R1 = scalar_t::from(2) * (f1 * f1 + f2 * f2); + // deg 2N constraints (f1+f2)^2 - (f1-f2)^2 = 4 f1 *f_2 + auto L2 = (f1 + f2) * (f1 + f2) - (f1 - f2) * (f1 - f2); auto R2 = scalar_t::from(4) * f1 * f2; end = std::chrono::high_resolution_clock::now(); duration = std::chrono::duration_cast(end - start); - std::cout << "Computing constraints..done. Took: " << duration.count() << " milliseconds"<< std::endl; - + std::cout << "Computing constraints..done. Took: " << duration.count() << " milliseconds" << std::endl; + // extract coeff using coeff view - auto [viewL1, sizeL1, device_idL1] = L1.get_coefficients_view(); - auto [viewL2, sizeL2, device_idL2] = L2.get_coefficients_view(); - auto [viewR1, sizeR1, device_idR1] = R1.get_coefficients_view(); - auto [viewR2, sizeR2, device_idR2] = R2.get_coefficients_view(); - - std::cout << "Computing Commitments with poly view"<< std::endl; + auto [viewL1, sizeL1] = L1.get_coefficients_view(); + auto [viewL2, sizeL2] = L2.get_coefficients_view(); + auto [viewR1, sizeR1] = R1.get_coefficients_view(); + auto [viewR2, sizeR2] = R2.get_coefficients_view(); + + std::cout << "Computing Commitments with poly view" << std::endl; start = std::chrono::high_resolution_clock::now(); - msm::MSMConfig config = msm::default_msm_config(); + MSMConfig config = default_msm_config(); config.are_points_on_device = true; config.are_scalars_on_device = true; - - //host vars (for result) + + // host vars (for result) projective_t hL1{}, hL2{}, hR1{}, hR2{}; - //straightforward msm bn254 api: no batching - bn254_msm_cuda(viewL1.get(),points_d,N,config,&hL1); - bn254_msm_cuda(viewL2.get(),points_d,N,config,&hL2); - bn254_msm_cuda(viewR1.get(),points_d,N,config,&hR1); - bn254_msm_cuda(viewR2.get(),points_d,N,config,&hR2); + // straightforward msm bn254 api: no batching + msm(viewL1.get(), points_d, N, config, &hL1); + msm(viewL2.get(), points_d, N, config, &hL2); + msm(viewR1.get(), points_d, N, config, &hR1); + msm(viewR2.get(), points_d, N, config, &hR2); end = std::chrono::high_resolution_clock::now(); duration = std::chrono::duration_cast(end - start); - std::cout << "Commitments done. Took: " << duration.count() << " milliseconds"<< std::endl; - - //sanity checks + std::cout << "Commitments done. Took: " << duration.count() << " milliseconds" << std::endl; + + // sanity checks auto affL1 = projective_t::to_affine(hL1); auto affR1 = projective_t::to_affine(hR1); auto affL2 = projective_t::to_affine(hL2); auto affR2 = projective_t::to_affine(hR2); - //test commitment equality [(f1+f2)^2 + (f1-f2)^2]_1 = [4 (f_1^2+f_2^2]_1 - assert(affL1.x==affR1.x && affL1.y==affR1.y); - std::cout << "commitment [(f1+f2)^2 + (f1-f2)^2]_1:" << std::endl; + // test commitment equality [(f1+f2)^2 + (f1-f2)^2]_1 = [4 (f_1^2+f_2^2]_1 + assert(affL1.x == affR1.x && affL1.y == affR1.y); + std::cout << "commitment [(f1+f2)^2 + (f1-f2)^2]_1:" << std::endl; std::cout << "[x: " << affL1.x << ", y: " << affL1.y << "]" << std::endl; - std::cout << "commitment [[2 (f_1^2+f_2^2]_1:" <(); const scalar_t basic_root = scalar_t::omega(MAX_NTT_LOG_SIZE); - ntt::init_domain(basic_root, ntt_config.ctx); + ntt_init_domain(basic_root, default_ntt_init_domain_config()); - // Virtual factory design pattern: initializing polynomimals factory for CUDA backend - Polynomial_t::initialize(std::make_unique>()); + START_TIMER(polyapi); example_evaluate(); example_clone(10); @@ -444,5 +442,7 @@ int main(int argc, char** argv) example_device_memory_view(); example_commit_with_device_memory_view(); + END_TIMER(polyapi, "polyapi example took"); + return 0; } \ No newline at end of file diff --git a/examples/c++/polynomial-api/run.sh b/examples/c++/polynomial-api/run.sh index 01eca66ba..8bed8b8c2 100755 --- a/examples/c++/polynomial-api/run.sh +++ b/examples/c++/polynomial-api/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/polynomial_multiplication/CMakeLists.txt b/examples/c++/polynomial_multiplication/CMakeLists.txt index 8879690ee..d523b645c 100644 --- a/examples/c++/polynomial_multiplication/CMakeLists.txt +++ b/examples/c++/polynomial_multiplication/CMakeLists.txt @@ -1,27 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -# change the path to your Icicle location -include_directories("../../../icicle") -add_executable( - example - example.cu -) +project(example) + +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_curve_bn254 icicle_field_bn254 icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -find_library(NVML_LIBRARY nvidia-ml PATHS /usr/local/cuda/targets/x86_64-linux/lib/stubs/ ) -target_link_libraries(example ${NVML_LIBRARY}) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) \ No newline at end of file diff --git a/examples/c++/polynomial_multiplication/README.md b/examples/c++/polynomial_multiplication/README.md new file mode 100644 index 000000000..9bf144990 --- /dev/null +++ b/examples/c++/polynomial_multiplication/README.md @@ -0,0 +1,30 @@ +# Icicle Example: Polynomial Multiplication with NTT + +## Key-Takeaway + +Icicle provides polynomial multiplication using the Number Theoretical Transform (NTT), including forward and inverse transforms. + +## Concise Usage Explanation + +1. Include the necessary headers. +2. Initialize the NTT domain. +3. Prepare and transform the polynomials from host to device memory. +4. Perform pointwise multiplication. +5. Apply the inverse NTT. + +## Running the example + +```sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir +``` + +## What's in the example + +1. Define the size of the example. +2. Initialize input polynomials. +3. Perform Radix-2 or Mixed-Radix NTT. +4. Perform pointwise polynomial multiplication. +5. Apply the inverse NTT. diff --git a/examples/c++/polynomial_multiplication/compile.sh b/examples/c++/polynomial_multiplication/compile.sh deleted file mode 100755 index de35c62da..000000000 --- a/examples/c++/polynomial_multiplication/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DMSM=OFF -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/polynomial_multiplication/example.cpp b/examples/c++/polynomial_multiplication/example.cpp new file mode 100644 index 000000000..9bd90b842 --- /dev/null +++ b/examples/c++/polynomial_multiplication/example.cpp @@ -0,0 +1,103 @@ +#include +#include +#include + +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" +using namespace bn254; + +#include "examples_utils.h" + +void random_samples(scalar_t* res, uint32_t count) +{ + for (int i = 0; i < count; i++) + res[i] = i < 1000 ? scalar_t::rand_host() : res[i - 1000]; +} + +void incremental_values(scalar_t* res, uint32_t count) +{ + for (int i = 0; i < count; i++) { + res[i] = i ? res[i - 1] + scalar_t::one() * scalar_t::omega(4) : scalar_t::zero(); + } +} + +// calcaulting polynomial multiplication A*B via NTT,pointwise-multiplication and INTT +// (1) allocate A,B on HOST. Randomize first half, zero second half +// (2) allocate A,B,Res on device +// (3) calc NTT for A and for B from host to device +// (4) multiply d_polyRes = NttAGpu * NttBGpu (pointwise) +// (5) INTT d_polyRes inplace + +int main(int argc, char** argv) +{ + try_load_and_set_backend_device(argc, argv); + + int NTT_LOG_SIZE = 20; + int NTT_SIZE = 1 << NTT_LOG_SIZE; + + // init domain + scalar_t basic_root = scalar_t::omega(NTT_LOG_SIZE); + auto config = default_ntt_init_domain_config(); + bn254_ntt_init_domain(&basic_root, &config); + + // (1) cpu allocation + auto polyA = std::make_unique(NTT_SIZE); + auto polyB = std::make_unique(NTT_SIZE); + random_samples(polyA.get(), NTT_SIZE >> 1); // second half zeros + random_samples(polyB.get(), NTT_SIZE >> 1); // second half zeros + + scalar_t *d_polyA, *d_polyB, *d_polyRes; + + DeviceProperties device_props; + ICICLE_CHECK(icicle_get_device_properties(device_props)); + + auto benchmark = [&](bool print) { + // (2) device input allocation. If device does not share memory with host, copy inputs explicitly and + ICICLE_CHECK(icicle_malloc((void**)&d_polyA, sizeof(scalar_t) * NTT_SIZE)); + ICICLE_CHECK(icicle_malloc((void**)&d_polyB, sizeof(scalar_t) * NTT_SIZE)); + ICICLE_CHECK(icicle_malloc((void**)&d_polyRes, sizeof(scalar_t) * NTT_SIZE)); + + // start recording + START_TIMER(poly_multiply) + + // (3) NTT for A,B from host memory to device-memory + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = false; + ntt_config.are_outputs_on_device = true; + ntt_config.ordering = Ordering::kNM; + ICICLE_CHECK(bn254_ntt(polyA.get(), NTT_SIZE, NTTDir::kForward, &ntt_config, d_polyA)); + ICICLE_CHECK(bn254_ntt(polyB.get(), NTT_SIZE, NTTDir::kForward, &ntt_config, d_polyB)); + + // (4) multiply A,B + VecOpsConfig config{ + nullptr, + true, // is_a_on_device + true, // is_b_on_device + true, // is_result_on_device + false, // is_async + nullptr // ext + }; + ICICLE_CHECK(bn254_vector_mul(d_polyA, d_polyB, NTT_SIZE, &config, d_polyRes)); + + // (5) INTT (in place) + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.ordering = Ordering::kMN; + ICICLE_CHECK(bn254_ntt(d_polyRes, NTT_SIZE, NTTDir::kInverse, &ntt_config, d_polyRes)); + + if (print) { END_TIMER(poly_multiply, "polynomial multiplication took"); } + + ICICLE_CHECK(icicle_free(d_polyA)); + ICICLE_CHECK(icicle_free(d_polyB)); + ICICLE_CHECK(icicle_free(d_polyRes)); + + return eIcicleError::SUCCESS; + }; + + benchmark(false); // warmup + benchmark(true); + + ICICLE_CHECK(bn254_ntt_release_domain()); + + return 0; +} \ No newline at end of file diff --git a/examples/c++/polynomial_multiplication/example.cu b/examples/c++/polynomial_multiplication/example.cu deleted file mode 100644 index 9c80864e5..000000000 --- a/examples/c++/polynomial_multiplication/example.cu +++ /dev/null @@ -1,119 +0,0 @@ -#include -#include -#include -#include - -#include "api/bn254.h" -#include "gpu-utils/error_handler.cuh" - -using namespace bn254; -typedef scalar_t test_scalar; -typedef scalar_t test_data; - -void random_samples(test_data* res, uint32_t count) -{ - for (int i = 0; i < count; i++) - res[i] = i < 1000 ? test_data::rand_host() : res[i - 1000]; -} - -void incremental_values(test_scalar* res, uint32_t count) -{ - for (int i = 0; i < count; i++) { - res[i] = i ? res[i - 1] + test_scalar::one() * test_scalar::omega(4) : test_scalar::zero(); - } -} - -// calcaulting polynomial multiplication A*B via NTT,pointwise-multiplication and INTT -// (1) allocate A,B on CPU. Randomize first half, zero second half -// (2) allocate NttAGpu, NttBGpu on GPU -// (3) calc NTT for A and for B from cpu to GPU -// (4) multiply MulGpu = NttAGpu * NttBGpu (pointwise) -// (5) INTT MulGpu inplace - -int main(int argc, char** argv) -{ - cudaEvent_t start, stop; - float measured_time; - - int NTT_LOG_SIZE = 23; - int NTT_SIZE = 1 << NTT_LOG_SIZE; - - CHK_IF_RETURN(cudaFree(nullptr)); // init GPU context - - // init domain - auto ntt_config = ntt::default_ntt_config(); - const bool is_radix2_alg = (argc > 1) ? atoi(argv[1]) : false; - ntt_config.ntt_algorithm = is_radix2_alg ? ntt::NttAlgorithm::Radix2 : ntt::NttAlgorithm::MixedRadix; - - const char* ntt_alg_str = is_radix2_alg ? "Radix-2" : "Mixed-Radix"; - std::cout << "Polynomial multiplication with " << ntt_alg_str << " NTT: "; - - CHK_IF_RETURN(cudaEventCreate(&start)); - CHK_IF_RETURN(cudaEventCreate(&stop)); - - test_scalar basic_root = test_scalar::omega(NTT_LOG_SIZE); - bn254_initialize_domain(&basic_root, ntt_config.ctx, true /*=fast_twidddles_mode*/); - - // (1) cpu allocation - auto CpuA = std::make_unique(NTT_SIZE); - auto CpuB = std::make_unique(NTT_SIZE); - random_samples(CpuA.get(), NTT_SIZE >> 1); // second half zeros - random_samples(CpuB.get(), NTT_SIZE >> 1); // second half zeros - - test_data *GpuA, *GpuB, *MulGpu; - - auto benchmark = [&](bool print, int iterations = 1) { - // start recording - CHK_IF_RETURN(cudaEventRecord(start, ntt_config.ctx.stream)); - - for (int iter = 0; iter < iterations; ++iter) { - // (2) gpu input allocation - CHK_IF_RETURN(cudaMallocAsync(&GpuA, sizeof(test_data) * NTT_SIZE, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaMallocAsync(&GpuB, sizeof(test_data) * NTT_SIZE, ntt_config.ctx.stream)); - - // (3) NTT for A,B from cpu to gpu - ntt_config.are_inputs_on_device = false; - ntt_config.are_outputs_on_device = true; - ntt_config.ordering = ntt::Ordering::kNM; - CHK_IF_RETURN(bn254_ntt_cuda(CpuA.get(), NTT_SIZE, ntt::NTTDir::kForward, ntt_config, GpuA)); - CHK_IF_RETURN(bn254_ntt_cuda(CpuB.get(), NTT_SIZE, ntt::NTTDir::kForward, ntt_config, GpuB)); - - // (4) multiply A,B - CHK_IF_RETURN(cudaMallocAsync(&MulGpu, sizeof(test_data) * NTT_SIZE, ntt_config.ctx.stream)); - vec_ops::VecOpsConfig config{ - ntt_config.ctx, - true, // is_a_on_device - true, // is_b_on_device - true, // is_result_on_device - false // is_async - }; - CHK_IF_RETURN(bn254_mul_cuda(GpuA, GpuB, NTT_SIZE, config, MulGpu)); - - // (5) INTT (in place) - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.ordering = ntt::Ordering::kMN; - CHK_IF_RETURN(bn254_ntt_cuda(MulGpu, NTT_SIZE, ntt::NTTDir::kInverse, ntt_config, MulGpu)); - - CHK_IF_RETURN(cudaFreeAsync(GpuA, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(GpuB, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(MulGpu, ntt_config.ctx.stream)); - } - - CHK_IF_RETURN(cudaEventRecord(stop, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaStreamSynchronize(ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaEventElapsedTime(&measured_time, start, stop)); - - if (print) { std::cout << measured_time / iterations << " MS" << std::endl; } - - return CHK_LAST(); - }; - - benchmark(false); // warmup - benchmark(true, 20); - - bn254_release_domain(ntt_config.ctx); - CHK_IF_RETURN(cudaStreamSynchronize(ntt_config.ctx.stream)); - - return 0; -} \ No newline at end of file diff --git a/examples/c++/polynomial_multiplication/run.sh b/examples/c++/polynomial_multiplication/run.sh index eac9b1b2b..879390d0a 100755 --- a/examples/c++/polynomial_multiplication/run.sh +++ b/examples/c++/polynomial_multiplication/run.sh @@ -1,3 +1,66 @@ #!/bin/bash -./build/example/example 1 # radix2 -./build/example/example 0 # mixed-radix + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -DMSM=OFF -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/c++/poseidon/CMakeLists.txt b/examples/c++/poseidon/CMakeLists.txt deleted file mode 100644 index ac06091f6..000000000 --- a/examples/c++/poseidon/CMakeLists.txt +++ /dev/null @@ -1,23 +0,0 @@ -cmake_minimum_required(VERSION 3.18) -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) -set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(icicle LANGUAGES CUDA CXX) - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") - -add_executable( - example - example.cu -) -target_include_directories(example PRIVATE "../../../icicle/include") -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_bn254.a) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) \ No newline at end of file diff --git a/examples/c++/poseidon/README.md b/examples/c++/poseidon/README.md deleted file mode 100644 index 7c936694e..000000000 --- a/examples/c++/poseidon/README.md +++ /dev/null @@ -1,72 +0,0 @@ -# Icicle example: build a Merkle tree using Poseidon hash - -## Best-Practices - -We recommend to run our examples in [ZK-containers](../../ZK-containers.md) to save your time and mental energy. - -## Key-Takeaway - -`Icicle` provides CUDA C++ template `poseidon_hash` to accelerate the popular [Poseidon hash function](https://www.poseidon-hash.info/). - -## Concise Usage Explanation - -```c++ -#include "appUtils/poseidon/poseidon.cu" -... -poseidon_hash(input, output, n, constants, config); -``` - -**Parameters:** - -- **`scalar_t`:** a scalar field of the selected curve. -You can think of field's elements as 32-byte integers modulo `p`, where `p` is a prime number, specific to this field. - -- **arity:** number of elements in a hashed block. - -- **n:** number of blocks we hash in parallel. - -- **input, output:** `scalar_t` arrays of size $arity*n$ and $n$ respectively. - -- **constants:** are defined as below - -```c++ -device_context::DeviceContext ctx= device_context::get_default_device_context(); -PoseidonConstants constants; -init_optimized_poseidon_constants(ctx, &constants); -``` - -## What's in the example - -1. Define the size of the example: the height of the full binary Merkle tree. -2. Hash blocks in parallel. The tree width determines the number of blocks to hash. -3. Build a Merkle tree from the hashes. -4. Use the tree to generate a membership proof for one of computed hashes. -5. Validate the hash membership. -6. Tamper the hash. -7. Invalidate the membership of the tempered hash. - -## Details - -### Merkle tree structure - -Our Merkle tree is a **full binary tree** stored in a 1D array. -The tree nodes are stored following a level-first traversal of the binary tree. -For a given level, we use offset to number elements from left to right. The node numbers on the figure below correspond to their locations in the array. - -```text - Tree Level - 0 0 - / \ - 1 2 1 - / \ / \ - 3 4 5 6 2 - -1D array representation: {0, 1, 2, 3, 4, 5, 6} -``` - -### Membership proof structure - -We use two arrays: - -- position (left/right) of the node along the path toward the root -- hash of a second node with the same parent \ No newline at end of file diff --git a/examples/c++/poseidon/compile.sh b/examples/c++/poseidon/compile.sh deleted file mode 100755 index de35c62da..000000000 --- a/examples/c++/poseidon/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DMSM=OFF -DCMAKE_BUILD_TYPE=Release -DCURVE=bn254 -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/poseidon/example.cu b/examples/c++/poseidon/example.cu deleted file mode 100644 index edc408a03..000000000 --- a/examples/c++/poseidon/example.cu +++ /dev/null @@ -1,146 +0,0 @@ -#include -#include -#include - -#include "api/bn254.h" -#include "curves/params/bn254.cuh" -#include "poseidon/poseidon.cuh" -#include "hash/hash.cuh" -using namespace poseidon; -using namespace bn254; - -device_context::DeviceContext ctx = device_context::get_default_device_context(); - -// location of a tree node in the array for a given level and offset -inline uint32_t tree_index(uint32_t level, uint32_t offset) { return (1 << level) - 1 + offset; } - -// We assume the tree has leaves already set, compute all other levels -void build_tree( - const uint32_t tree_height, scalar_t* tree, Poseidon &poseidon, HashConfig &config) -{ - for (uint32_t level = tree_height - 1; level > 0; level--) { - const uint32_t next_level = level - 1; - const uint32_t next_level_width = 1 << next_level; - poseidon.hash_many(&tree[tree_index(level, 0)], &tree[tree_index(next_level, 0)], next_level_width, 2, 1, config); - } -} - -// linear search leaves for a given hash, return offset -uint32_t query_membership(scalar_t query, scalar_t* tree, const uint32_t tree_height) -{ - const uint32_t tree_width = (1 << (tree_height - 1)); - for (uint32_t i = 0; i < tree_width; i++) { - const scalar_t leaf = tree[tree_index(tree_height - 1, i)]; - if (leaf == query) { - return i; // found the hash - } - } - return tree_height; // hash not found -} - -void generate_proof( - uint32_t position, scalar_t* tree, const uint32_t tree_height, uint32_t* proof_lr, scalar_t* proof_hash) -{ - uint32_t level_index = position; - for (uint32_t level = tree_height - 1; level > 0; level--) { - uint32_t lr; - uint32_t neighbour_index; - lr = level_index % 2; - if (lr == 0) { - // left - neighbour_index = level_index + 1; - } else { - // right - neighbour_index = level_index - 1; - } - proof_lr[level] = lr; - proof_hash[level] = tree[tree_index(level, neighbour_index)]; - level_index /= 2; - } - // the proof must match this: - proof_hash[0] = tree[tree_index(0, 0)]; -} - -uint32_t validate_proof( - const scalar_t hash, - const uint32_t tree_height, - const uint32_t* proof_lr, - const scalar_t* proof_hash, - Poseidon &poseidon, - HashConfig &config) -{ - scalar_t hashes_in[2], hash_out[1], level_hash; - level_hash = hash; - for (uint32_t level = tree_height - 1; level > 0; level--) { - if (proof_lr[level] == 0) { - hashes_in[0] = level_hash; - hashes_in[1] = proof_hash[level]; - } else { - hashes_in[0] = proof_hash[level]; - hashes_in[1] = level_hash; - } - // next level hash - poseidon.hash_many(hashes_in, hash_out, 1, 2, 1, config); - level_hash = hash_out[0]; - } - return proof_hash[0] == level_hash; -} - -int main(int argc, char* argv[]) -{ - std::cout << "1. Defining the size of the example: height of the full binary Merkle tree" << std::endl; - const uint32_t tree_height = 21; - std::cout << "Tree height: " << tree_height << std::endl; - const uint32_t tree_arity = 2; - const uint32_t leaf_level = tree_height - 1; - const uint32_t tree_width = 1 << leaf_level; - std::cout << "Tree width: " << tree_width << std::endl; - const uint32_t tree_size = (1 << tree_height) - 1; - std::cout << "Tree size: " << tree_size << std::endl; - scalar_t* tree = static_cast(malloc(tree_size * sizeof(scalar_t))); - - std::cout << "2. Hashing blocks in parallel" << std::endl; - const uint32_t data_arity = 4; - std::cout << "Block size (arity): " << data_arity << std::endl; - std::cout << "Initializing blocks..." << std::endl; - scalar_t d = scalar_t::zero(); - scalar_t* data = static_cast(malloc(tree_width * data_arity * sizeof(scalar_t))); - for (uint32_t i = 0; i < tree_width * data_arity; i++) { - data[i] = d; - d = d + scalar_t::one(); - } - std::cout << "Hashing blocks into tree leaves..." << std::endl; - - Poseidon poseidon(data_arity, ctx); - HashConfig config = default_hash_config(ctx); - poseidon.hash_many(data, &tree[tree_index(leaf_level, 0)], tree_width, data_arity, 1, config); - - std::cout << "3. Building Merkle tree" << std::endl; - Poseidon tree_poseidon(tree_arity, ctx); - HashConfig tree_config = default_hash_config(ctx); - build_tree(tree_height, tree, tree_poseidon, tree_config); - - std::cout << "4. Generate membership proof" << std::endl; - uint32_t position = tree_width - 1; - std::cout << "Using the hash for block: " << position << std::endl; - scalar_t query = tree[tree_index(leaf_level, position)]; - uint32_t query_position = query_membership(query, tree, tree_height); - // allocate arrays for the proof - uint32_t* proof_lr = static_cast(malloc(tree_height * sizeof(uint32_t))); - scalar_t* proof_hash = static_cast(malloc(tree_height * sizeof(scalar_t))); - generate_proof(query_position, tree, tree_height, proof_lr, proof_hash); - - std::cout << "5. Validate the hash membership" << std::endl; - uint32_t validated; - const scalar_t hash = tree[tree_index(leaf_level, query_position)]; - validated = validate_proof(hash, tree_height, proof_lr, proof_hash, tree_poseidon, tree_config); - std::cout << "Validated: " << validated << std::endl; - - std::cout << "6. Tamper the hash" << std::endl; - const scalar_t tampered_hash = hash + scalar_t::one(); - validated = validate_proof(tampered_hash, tree_height, proof_lr, proof_hash, tree_poseidon, tree_config); - - std::cout << "7. Invalidate tamper hash membership" << std::endl; - std::cout << "Validated: " << validated << std::endl; - return 0; -} diff --git a/examples/c++/poseidon/run.sh b/examples/c++/poseidon/run.sh deleted file mode 100755 index ce22b116a..000000000 --- a/examples/c++/poseidon/run.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -./build/example/example \ No newline at end of file diff --git a/examples/c++/risc0/CMakeLists.txt b/examples/c++/risc0/CMakeLists.txt index b77102142..9f8826c79 100644 --- a/examples/c++/risc0/CMakeLists.txt +++ b/examples/c++/risc0/CMakeLists.txt @@ -1,28 +1,16 @@ cmake_minimum_required(VERSION 3.18) + set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) -if (${CMAKE_VERSION} VERSION_LESS "3.24.0") - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) -else() - set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed -endif () -project(example LANGUAGES CUDA CXX) - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr -DFIELD_ID=1001") -# set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") -set(CMAKE_CUDA_FLAGS_RELEASE "") -set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") -add_executable( - example - example.cu -) +project(example) -set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -target_include_directories(example PRIVATE "../../../icicle/include") +add_executable(example example.cpp) +target_include_directories(example PRIVATE "../../../icicle/include" "..") +target_link_directories(example PRIVATE "${CMAKE_SOURCE_DIR}/build/icicle") +message("${CMAKE_BINARY_DIR}/icicle") +target_link_libraries(example PRIVATE icicle_field_babybear icicle_device) +if(BACKEND_DIR) + add_compile_definitions(BACKEND_DIR="${BACKEND_DIR}") +endif() -# can link to another curve/field by changing the following lib and FIELD_ID -target_link_libraries(example ${CMAKE_SOURCE_DIR}/build/icicle/lib/libingo_field_babybear.a) -# target_compile_definitions(example PUBLIC FIELD_ID babybear) \ No newline at end of file diff --git a/examples/c++/risc0/README.md b/examples/c++/risc0/README.md index 743d003d8..fc10fdcb2 100644 --- a/examples/c++/risc0/README.md +++ b/examples/c++/risc0/README.md @@ -21,9 +21,10 @@ The key enabler for *recursion* is the *redundancy* of polynomial commitments, h To run example, from project root directory: ```sh -cd examples/c++/risc0 -./compile.sh -./run.sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir ``` ## What's in the example diff --git a/examples/c++/risc0/compile.sh b/examples/c++/risc0/compile.sh deleted file mode 100755 index 433925721..000000000 --- a/examples/c++/risc0/compile.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# Exit immediately on error -set -e - -mkdir -p build/example -mkdir -p build/icicle - -# Configure and build Icicle -cmake -S ../../../icicle/ -B build/icicle -DCMAKE_BUILD_TYPE=Release -DFIELD=babybear -cmake --build build/icicle - -# Configure and build the example application -cmake -S . -B build/example -cmake --build build/example \ No newline at end of file diff --git a/examples/c++/risc0/example.cu b/examples/c++/risc0/example.cpp similarity index 76% rename from examples/c++/risc0/example.cu rename to examples/c++/risc0/example.cpp index e07128e8b..fe5c48e4b 100644 --- a/examples/c++/risc0/example.cu +++ b/examples/c++/risc0/example.cpp @@ -4,11 +4,11 @@ #include #include -#include "polynomials/polynomials.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" -#include "ntt/ntt.cuh" +#include "examples_utils.h" +#include "icicle/polynomials/polynomials.h" +#include "icicle/api/babybear.h" -using namespace polynomials; +using namespace babybear; // define the polynomial type typedef Polynomial Polynomial_t; @@ -17,44 +17,49 @@ typedef Polynomial Polynomial_t; typedef int64_t rv_t; // Convert RISC-V registers to Finite Fields -void to_ff(rv_t* rv, scalar_t* s, size_t n) { +void to_ff(rv_t* rv, scalar_t* s, size_t n) +{ for (int i = 0; i < n; ++i) { s[i] = scalar_t::from(rv[i]); } } -void p_print(Polynomial_t * p, int logn, scalar_t shift, std::string header = "Print Vector") { +void p_print(Polynomial_t* p, int logn, scalar_t shift, std::string header = "Print Vector") +{ std::cout << header << std::endl; auto n = 1 << logn; auto omega = scalar_t::omega(logn); auto x = shift; for (int i = 0; i < n; ++i) { std::cout << i << ": " << (*p)(x) << std::endl; - x = x*omega; + x = x * omega; } } // value to polynomial -Polynomial_t p_value(scalar_t value) { - auto p_value = Polynomial_t::from_coefficients(&value , 1); +Polynomial_t p_value(scalar_t value) +{ + auto p_value = Polynomial_t::from_coefficients(&value, 1); return p_value; } -Polynomial_t p_rotate(Polynomial_t* p, int logn) { +Polynomial_t p_rotate(Polynomial_t* p, int logn) +{ // rotate polynomial coefficients right by one position auto n = 1 << logn; auto evaluations_rou_domain = std::make_unique(n); - p->evaluate_on_rou_domain(logn, evaluations_rou_domain.get() ); - scalar_t tmp = evaluations_rou_domain[n-1]; - for (int i = n-1; i > 0; --i) { - evaluations_rou_domain[i] = evaluations_rou_domain[i-1]; + p->evaluate_on_rou_domain(logn, evaluations_rou_domain.get()); + scalar_t tmp = evaluations_rou_domain[n - 1]; + for (int i = n - 1; i > 0; --i) { + evaluations_rou_domain[i] = evaluations_rou_domain[i - 1]; } evaluations_rou_domain[0] = tmp; - return Polynomial_t::from_rou_evaluations(evaluations_rou_domain.get(), n); + return Polynomial_t::from_rou_evaluations(evaluations_rou_domain.get(), n); } // mix polynomials (c.f. mix polynomial evaluations) -Polynomial_t p_mix(Polynomial_t* in[], size_t nmix, scalar_t mix_parameter) { +Polynomial_t p_mix(Polynomial_t* in[], size_t nmix, scalar_t mix_parameter) +{ scalar_t factor = mix_parameter; Polynomial_t out = in[0]->clone(); for (int i = 1; i < nmix; ++i) { @@ -64,47 +69,50 @@ Polynomial_t p_mix(Polynomial_t* in[], size_t nmix, scalar_t mix_parameter) { return out; } -void solve_linear(scalar_t xa, scalar_t ya, scalar_t xb, scalar_t yb, scalar_t * coeffs) { +void solve_linear(scalar_t xa, scalar_t ya, scalar_t xb, scalar_t yb, scalar_t* coeffs) +{ coeffs[1] = (ya - yb) * scalar_t::inverse(xa - xb); coeffs[0] = ya - coeffs[1] * xa; } -std::unique_ptr InterpolateOnLargerDomain(Polynomial_t * p, int n, scalar_t shift = scalar_t::one()) { +std::unique_ptr InterpolateOnLargerDomain(Polynomial_t* p, int n, scalar_t shift = scalar_t::one()) +{ const int deg = p->degree(); auto input = std::make_unique(n); // TBD: check if scalar_t constructor initializes to zero for (int i = 0; i < n; ++i) { input[i] = scalar_t::zero(); } - p->copy_coeffs(input.get(), 0/*start*/, deg); - auto ntt_config = ntt::default_ntt_config(); + p->copy_coeffs(input.get(), 0 /*start*/, deg); + auto ntt_config = default_ntt_config(); ntt_config.coset_gen = shift; auto evals_h = std::make_unique(n); - auto err = ntt::ntt(input.get(), n, ntt::NTTDir::kForward, ntt_config, evals_h.get()); + ICICLE_CHECK(ntt(input.get(), n, NTTDir::kForward, ntt_config, evals_h.get())); return evals_h; } int main(int argc, char** argv) { + try_load_and_set_backend_device(argc, argv); + + START_TIMER(risc0_example); + std::cout << "This is an ICICLE C++ implementation of the STARK by Hand Explainer." << std::endl; std::cout << "https://dev.risczero.com/proof-system/stark-by-hand" << std::endl; - const int logn=3; + const int logn = 3; const int n = 1 << logn; - + std::cout << "Initializing NTT" << std::endl; static const int MAX_NTT_LOG_SIZE = 24; - auto ntt_config = ntt::default_ntt_config(); + auto ntt_config = default_ntt_config(); const scalar_t basic_root = scalar_t::omega(MAX_NTT_LOG_SIZE); - ntt::init_domain(basic_root, ntt_config.ctx); - std::cout << "Initializing Polynomials" << std::endl; - // Virtual factory design pattern: initializing polynomimals factory for CUDA backend - Polynomial_t::initialize(std::make_unique>()); + ntt_init_domain(basic_root, default_ntt_init_domain_config()); - std::cout << std::endl << "Lesson 1: The Execution Trace" << std::endl; + std::cout << std::endl << "Lesson 1: The Execution Trace" << std::endl; // Trace: Data Columns - rv_t rv_d1_trace[] = {24, 30, 54, 84, 78, 15, 29, 50}; - rv_t rv_d2_trace[] = {30, 54, 84, 138, 2, 77, 21, 36}; + rv_t rv_d1_trace[] = {24, 30, 54, 84, 78, 15, 29, 50}; + rv_t rv_d2_trace[] = {30, 54, 84, 138, 2, 77, 21, 36}; rv_t rv_d3_trace[] = {54, 84, 138, 222, 71, 17, 92, 33}; auto d1_trace = std::make_unique(n); auto d2_trace = std::make_unique(n); @@ -151,57 +159,64 @@ int main(int argc, char** argv) auto p_c3 = Polynomial_t::from_rou_evaluations(c3_trace.get(), n); std::cout << "Lesson 5: ZK Commitments of the Trace Data" << std::endl; - std::cout << "To maintain a zk protocol, the trace polynomials are evaluated over a zk commitment domain" << std::endl; + std::cout << "To maintain a zk protocol, the trace polynomials are evaluated over a zk commitment domain" + << std::endl; std::cout << "zk commitment domain is a coset of Reed Solomon domain shifted by a basic root of unity" << std::endl; scalar_t xzk = basic_root; - p_print(&p_d1, logn, xzk, "ZK commitment for d1 polynomial"); + p_print(&p_d1, logn, xzk, "ZK commitment for d1 polynomial"); std::cout << "Build Merkle Tree for ZK commitments (outside the scope of this example)" << std::endl; std::cout << "Lesson 6: Constraint Polynomials" << std::endl; - std::cout << "The constraints are used to check the correctness of the trace. In this example, we check 6 rules to establish the validity of the trace." << std::endl; - auto p_fib_constraint = (p_d3 - p_d2 - p_d1) * (p_c1 + p_c2 + p_c3); - auto fib_constraint_zkcommitment = InterpolateOnLargerDomain(&p_fib_constraint, 4*n, xzk); - + std::cout << "The constraints are used to check the correctness of the trace. In this example, we check 6 rules to " + "establish the validity of the trace." + << std::endl; + auto p_fib_constraint = (p_d3 - p_d2 - p_d1) * (p_c1 + p_c2 + p_c3); + auto fib_constraint_zkcommitment = InterpolateOnLargerDomain(&p_fib_constraint, 4 * n, xzk); + auto p_init1_constraint = (p_d1 - p_value(scalar_t::from(24))) * p_c1; // sanity checks printing - p_print(&p_init1_constraint, logn+2, scalar_t::one(), "Reed-Solomon constraint polynomial gives 0s in every 4th row"); - p_print(&p_init1_constraint, logn+2, xzk, "ZK Commitment constraint polynomial gives no 0s"); + p_print( + &p_init1_constraint, logn + 2, scalar_t::one(), "Reed-Solomon constraint polynomial gives 0s in every 4th row"); + p_print(&p_init1_constraint, logn + 2, xzk, "ZK Commitment constraint polynomial gives no 0s"); auto p_init2_constraint = (p_d2 - p_value(scalar_t::from(30))) * p_c1; auto p_termination_constraint = (p_d3 - p_value(scalar_t::from(222))) * p_c3; auto p_recursion_constraint1 = (p_d1 - p_rotate(&p_d2, logn)) * p_c2; auto p_recursion_constraint2 = (p_d2 - p_rotate(&p_d3, logn)) * p_c2; - std::cout << std::endl << "Lesson 7: Mixing Constraint Polynomials" << std::endl; - Polynomial_t * p_all_constraints[] = {&p_fib_constraint, &p_init1_constraint, &p_init2_constraint, &p_termination_constraint, &p_recursion_constraint1, &p_recursion_constraint2}; + std::cout << std::endl << "Lesson 7: Mixing Constraint Polynomials" << std::endl; + Polynomial_t* p_all_constraints[] = {&p_fib_constraint, &p_init1_constraint, &p_init2_constraint, + &p_termination_constraint, &p_recursion_constraint1, &p_recursion_constraint2}; const size_t nmix = sizeof(p_all_constraints) / sizeof(p_all_constraints[0]); auto p_mixed_constraints = p_mix(p_all_constraints, nmix, scalar_t::from(5)); std::cout << "All constraint polynomials are low-degree:" << std::endl; - for( int i = 0; i < nmix; ++i) { + for (int i = 0; i < nmix; ++i) { std::cout << i << ": " << p_all_constraints[i]->degree() << std::endl; } std::cout << "Lesson 8: The Core of the RISC Zero STARK" << std::endl; - std::cout << "Degree of the mixed constraints polynomial: " << p_mixed_constraints.degree() << std::endl; + std::cout << "Degree of the mixed constraints polynomial: " << p_mixed_constraints.degree() << std::endl; auto p_validity = p_mixed_constraints.divide_by_vanishing_polynomial(n); std::cout << "Degree of the validity polynomial: " << p_validity.degree() << std::endl; std::cout << "The Verifier should provide the Merke commitment for the above" << std::endl; std::cout << "Lesson 9: The DEEP Technique" << std::endl; - std::cout << "The DEEP technique improves the security of a single query by sampling outside of the commitment domain." << std::endl; - // In the original STARK protocol, the Verifier tests validity polynomial at a number of test points; - // the soundness of the protocol depends on the number of tests. - // The DEEP-ALI technique allows us to achieve a high degree of soundness with a single test. + std::cout + << "The DEEP technique improves the security of a single query by sampling outside of the commitment domain." + << std::endl; + // In the original STARK protocol, the Verifier tests validity polynomial at a number of test points; + // the soundness of the protocol depends on the number of tests. + // The DEEP-ALI technique allows us to achieve a high degree of soundness with a single test. // The details of DEEP are described in the following lesson. auto DEEP_point = scalar_t::from(93); std::cout << "The prover convinces the verifier that V=C/Z at the DEEP_test_point, " << DEEP_point << std::endl; - const scalar_t coeffs1[2] = {scalar_t::zero()-DEEP_point, scalar_t::one()}; + const scalar_t coeffs1[2] = {scalar_t::zero() - DEEP_point, scalar_t::one()}; auto denom_DEEP1 = Polynomial_t::from_coefficients(coeffs1, 2); auto [p_d1_DEEP, r] = (p_d1 - p_value(DEEP_point)).divide(denom_DEEP1); std::cout << "The DEEP d1 degree is: " << p_d1_DEEP.degree() << std::endl; // d2, d3 use recursion constraints and need the point corresponding to the previous state (clock cycle) auto omega = scalar_t::omega(logn); - auto DEEP_prev_point = DEEP_point*scalar_t::inverse(omega); + auto DEEP_prev_point = DEEP_point * scalar_t::inverse(omega); auto coeffs2 = std::make_unique(2); coeffs2[0] = scalar_t::zero() - DEEP_prev_point; coeffs2[1] = scalar_t::one(); @@ -210,13 +225,13 @@ int main(int argc, char** argv) auto coeffs_d2bar = std::make_unique(2); solve_linear(DEEP_point, p_d2(DEEP_point), DEEP_prev_point, p_d2(DEEP_prev_point), coeffs_d2bar.get()); auto d2bar = Polynomial_t::from_coefficients(coeffs_d2bar.get(), 2); - auto [p_d2_DEEP, r2] = (p_d2 - d2bar).divide(denom_DEEP1*denom_DEEP2); + auto [p_d2_DEEP, r2] = (p_d2 - d2bar).divide(denom_DEEP1 * denom_DEEP2); std::cout << "The DEEP d2 degree is: " << p_d2_DEEP.degree() << std::endl; auto coeffs_d3bar = std::make_unique(2); solve_linear(DEEP_point, p_d3(DEEP_point), DEEP_prev_point, p_d3(DEEP_prev_point), coeffs_d3bar.get()); auto d3bar = Polynomial_t::from_coefficients(coeffs_d3bar.get(), 2); - auto [p_d3_DEEP, r3] = (p_d3 - d3bar).divide(denom_DEEP1*denom_DEEP2); + auto [p_d3_DEEP, r3] = (p_d3 - d3bar).divide(denom_DEEP1 * denom_DEEP2); std::cout << "The DEEP d3 degree is: " << p_d3_DEEP.degree() << std::endl; // DEEP c{1,2,3} polynomials @@ -246,17 +261,18 @@ int main(int argc, char** argv) std::cout << "The degree of the mixed DEEP polynomial is: " << fri_input.degree() << std::endl; std::cout << "Lesson 11: FRI Protocol (Commit Phase)" << std::endl; - std::cout << "The prover provides information to convince the verifier that the DEEP polynomials are low-degree." << std::endl; + std::cout << "The prover provides information to convince the verifier that the DEEP polynomials are low-degree." + << std::endl; int nof_rounds = 3; - Polynomial_t feven[nof_rounds], fodd[nof_rounds], fri[nof_rounds+1]; + Polynomial_t feven[nof_rounds], fodd[nof_rounds], fri[nof_rounds + 1]; scalar_t rfri[nof_rounds]; fri[0] = fri_input.clone(); for (int i = 0; i < nof_rounds; ++i) { feven[i] = fri[i].even(); fodd[i] = fri[i].odd(); - rfri[i] = scalar_t::rand_host(); - fri[i+1] = feven[i] + rfri[i]*fodd[i]; - std::cout << "The degree of the Round " << i << " polynomial is: " << fri[i+1].degree() << std::endl; + rfri[i] = scalar_t::rand_host(); + fri[i + 1] = feven[i] + rfri[i] * fodd[i]; + std::cout << "The degree of the Round " << i << " polynomial is: " << fri[i + 1].degree() << std::endl; } std::cout << "Lesson 12: FRI Protocol (Query Phase)" << std::endl; @@ -266,10 +282,12 @@ int main(int argc, char** argv) auto xm = scalar_t::zero() - xp; scalar_t lhs[nof_rounds], rhs[nof_rounds]; for (int i = 0; i < nof_rounds; ++i) { - rhs[i] = (rfri[i]+xp)*fri[i](xp)*scalar_t::inverse(scalar_t::from(2)*xp) + (rfri[i]+xm)*fri[i](xm)*scalar_t::inverse(scalar_t::from(2)*xm); - lhs[i] = fri[i+1](xp*xp); + rhs[i] = (rfri[i] + xp) * fri[i](xp) * scalar_t::inverse(scalar_t::from(2) * xp) + + (rfri[i] + xm) * fri[i](xm) * scalar_t::inverse(scalar_t::from(2) * xm); + lhs[i] = fri[i + 1](xp * xp); std::cout << "Round " << i << std::endl << "rhs: " << rhs[i] << std::endl << "lhs: " << lhs[i] << std::endl; } + END_TIMER(risc0_example, "risc0 example"); return 0; } \ No newline at end of file diff --git a/examples/c++/risc0/run.sh b/examples/c++/risc0/run.sh index 01eca66ba..24e6f6358 100755 --- a/examples/c++/risc0/run.sh +++ b/examples/c++/risc0/run.sh @@ -1,2 +1,66 @@ #!/bin/bash -./build/example/example + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cmake -DCMAKE_BUILD_TYPE=Release -DFIELD=babybear -DMSM=OFF -DG2=OFF -DECNTT=OFF -DCUDA_BACKEND=local -S "${ICILE_DIR}" -B build/icicle + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "build/icicle/backend") +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cmake -DCMAKE_BUILD_TYPE=Release -DFIELD=babybear -S "${ICILE_DIR}" -B build/icicle +fi +cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/example + +cmake --build build/icicle -j +cmake --build build/example -j + +./build/example/example "$DEVICE_TYPE" diff --git a/examples/golang/install-and-use-icicle/README.md b/examples/golang/install-and-use-icicle/README.md new file mode 100644 index 000000000..b3e7d4e29 --- /dev/null +++ b/examples/golang/install-and-use-icicle/README.md @@ -0,0 +1,96 @@ +# Example: Install and use ICICLE + +This example shows how to install CUDA backend and use it in a Go application. + +Download release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases): +- **Backend** icicle30-ubuntu22-cuda122.tar.gz + +> [!NOTE] +> The names of the files are based on the release version. Ensure you update the tar file names in the example if you are using a different release. + +## Optional: Using Docker with Ubuntu 22 + +While not mandatory, this example can be demonstrated in an Ubuntu 22 Docker container. +```bash +docker run -it --rm --gpus all -v ./:/workspace -w /workspace icicle-release-ubuntu22-cuda122 bash +``` + +This command starts a bash session in the Docker container with GPUs enabled and the example files mapped to /workspace in the container. + +### Building the docker image + +The Docker image is based on NVIDIA’s image for Ubuntu 22.04 and can be built from the following Dockerfile: +```dockerfile +# Use the official NVIDIA development runtime image for Ubuntu 22.04 +FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + build-essential \ + cmake \ + tar + +ENV GOLANG_VERSION 1.21.1 +RUN curl -L https://go.dev/dl/go${GOLANG_VERSION}.linux-amd64.tar.gz | tar -xz -C /usr/local +ENV PATH="/usr/local/go/bin:${PATH}" +``` + +Build the Docker image with the following command: +```bash +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 . +``` + +## Extract and Install the CUDA Backend + +```bash +cd release +# extract CUDA backend +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt +``` + +## Build the Go Frontend and Execute + +Update your go.mod to include ICICLE as a dependency, navigate to the dependency in your GOMODCACHE and build ICICLE there + +```sh +go get github.com/ingonyama-zk/icicle/v3 +cd $(go env GOMODCACHE)/github.com/ingonyama-zk/icicle/v3@/wrappers/golang +chmod +x build.sh +./build.sh -curve=bn254 +./build.sh -curve=bls12_377 +``` + +Once ICICLE has been built, you can add specific packages when you need them in your application and load the backend + +```go +import ( + runtime "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + core "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254MSM "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) + +// This loads the CUDA backend that you extracted to /opt +runtime.LoadBackendFromEnvOrDefault() +``` + +## Install in a Custom Location +If you prefer to install the CUDA backend in a custom location such as /custom/path, follow these steps: + +```bash +mkdir -p /custom/path +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /custom/path +``` + +If installed in a custom location, you need to define the environment variable: + +```bash +export ICICLE_BACKEND_INSTALL_DIR=/custom/path/icicle/lib/backend +``` + +Alternatively, you can load the backend programmatically in your Go code using the `LoadBackend` function from the `runtime` package: +```go +func LoadBackend(path string, isRecursive bool) EIcicleError + +runtime.LoadBackend("/custom/path/to/backend", true) +``` diff --git a/examples/golang/install-and-use-icicle/go.mod b/examples/golang/install-and-use-icicle/go.mod new file mode 100644 index 000000000..a1cbf320a --- /dev/null +++ b/examples/golang/install-and-use-icicle/go.mod @@ -0,0 +1,10 @@ +module ingonyama.com/install_and_use_icicle + +go 1.22.1 + +// TODO - When v3 is pushed to main, switch to this +// require ( +// github.com/ingonyama-zk/icicle/v3 v3.0.0 +// ) + +require github.com/ingonyama-zk/icicle/v3 v3.0.0-20240902084701-0e5a616c7ea6 diff --git a/examples/golang/install-and-use-icicle/go.sum b/examples/golang/install-and-use-icicle/go.sum new file mode 100644 index 000000000..3fa728cd9 --- /dev/null +++ b/examples/golang/install-and-use-icicle/go.sum @@ -0,0 +1,10 @@ +github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c= +github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= +github.com/ingonyama-zk/icicle/v3 v3.0.0-20240902084701-0e5a616c7ea6 h1:xxK3awsYhfTRNT3IeUzIF31miwq8ECla0NxHaibGi9U= +github.com/ingonyama-zk/icicle/v3 v3.0.0-20240902084701-0e5a616c7ea6/go.mod h1:jWb8eWG0p+5PAOLtweqW3NOxR/HwM3ywrjXM/diiUcY= +github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM= +github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4= +github.com/stretchr/testify v1.8.2 h1:+h33VjcLVPDHtOdpUCuF+7gSuG3yGIftsP1YvFihtJ8= +github.com/stretchr/testify v1.8.2/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4= +gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA= +gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM= diff --git a/examples/golang/install-and-use-icicle/main.go b/examples/golang/install-and-use-icicle/main.go new file mode 100644 index 000000000..ae3a20217 --- /dev/null +++ b/examples/golang/install-and-use-icicle/main.go @@ -0,0 +1,221 @@ +package main + +import ( + "flag" + "fmt" + "time" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377" + bls12377G2 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377/g2" + bls12377Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377/msm" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254G2 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" + bn254Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) + +func main() { + runtime.LoadBackendFromEnvOrDefault() + + var logSizeMin int + var logSizeMax int + var deviceType string + + flag.IntVar(&logSizeMin, "l", 10, "Minimum log size") + flag.IntVar(&logSizeMax, "u", 10, "Maximum log size") + flag.StringVar(&deviceType, "device", "CUDA", "Device type") + flag.Parse() + + device := runtime.CreateDevice(deviceType, 0) + runtime.SetDevice(&device) + + sizeMax := 1 << logSizeMax + + print("Generating BN254 scalars ... ") + startTime := time.Now() + scalarsBn254Max := bn254.GenerateScalars(sizeMax) + println(time.Since(startTime).String()) + + print("Generating BN254 points ... ") + startTime = time.Now() + pointsBn254Max := bn254.GenerateAffinePoints(sizeMax) + println(time.Since(startTime).String()) + + print("Generating BN254 G2 points ... ") + startTime = time.Now() + pointsBn254G2Max := bn254G2.G2GenerateAffinePoints(sizeMax) + println(time.Since(startTime).String()) + + print("Generating BLS12_377 scalars ... ") + startTime = time.Now() + scalarsBls12377Max := bls12377.GenerateScalars(sizeMax) + println(time.Since(startTime).String()) + + print("Generating BLS12_377 points ... ") + startTime = time.Now() + pointsBls12377Max := bls12377.GenerateAffinePoints(sizeMax) + println(time.Since(startTime).String()) + + print("Generating BLS12_377 G2 points ... ") + startTime = time.Now() + pointsBls12377G2Max := bls12377G2.G2GenerateAffinePoints(sizeMax) + println(time.Since(startTime).String()) + + for logSize := logSizeMin; logSize <= logSizeMax; logSize++ { + + // Define the size of the problem, here 2^18. + size := 1 << logSize + + fmt.Printf("---------------------- MSM size 2^%d=%d ------------------------\n", logSize, size) + + // println(scalarsBls12377, pointsBls12377, pointsBn254G2) + // println(scalarsBn254, pointsBn254, pointsBls12377G2) + + print("Configuring bn254 MSM ... ") + startTime = time.Now() + + scalarsBn254 := scalarsBn254Max[:size] + pointsBn254 := pointsBn254Max[:size] + pointsBn254G2 := pointsBn254G2Max[:size] + + cfgBn254 := core.GetDefaultMSMConfig() + cfgBn254G2 := core.GetDefaultMSMConfig() + cfgBn254.IsAsync = true + cfgBn254G2.IsAsync = true + + streamBn254, _ := runtime.CreateStream() + streamBn254G2, _ := runtime.CreateStream() + + cfgBn254.StreamHandle = streamBn254 + cfgBn254G2.StreamHandle = streamBn254G2 + + var projectiveBn254 bn254.Projective + var projectiveBn254G2 bn254G2.G2Projective + + var msmResultBn254 core.DeviceSlice + var msmResultBn254G2 core.DeviceSlice + + _, e := msmResultBn254.MallocAsync(projectiveBn254.Size(), 1, streamBn254) + if e != runtime.Success { + errorString := fmt.Sprint( + "Bn254 Malloc failed: ", e) + panic(errorString) + } + _, e = msmResultBn254G2.MallocAsync(projectiveBn254G2.Size(), 1, streamBn254G2) + if e != runtime.Success { + errorString := fmt.Sprint( + "Bn254 Malloc G2 failed: ", e) + panic(errorString) + } + + println(time.Since(startTime).String()) + + print("Configuring Bls12377 MSM ... ") + startTime = time.Now() + + scalarsBls12377 := scalarsBls12377Max[:size] + pointsBls12377 := pointsBls12377Max[:size] + pointsBls12377G2 := pointsBls12377G2Max[:size] + + cfgBls12377 := core.GetDefaultMSMConfig() + cfgBls12377G2 := core.GetDefaultMSMConfig() + cfgBls12377.IsAsync = true + cfgBls12377G2.IsAsync = true + + streamBls12377, _ := runtime.CreateStream() + streamBls12377G2, _ := runtime.CreateStream() + + cfgBls12377.StreamHandle = streamBls12377 + cfgBls12377G2.StreamHandle = streamBls12377G2 + + var projectiveBls12377 bls12377.Projective + var projectiveBls12377G2 bls12377G2.G2Projective + + var msmResultBls12377 core.DeviceSlice + var msmResultBls12377G2 core.DeviceSlice + + _, e = msmResultBls12377.MallocAsync(projectiveBls12377.Size(), 1, streamBls12377) + if e != runtime.Success { + errorString := fmt.Sprint( + "Bls12_377 Malloc failed: ", e) + panic(errorString) + } + _, e = msmResultBls12377G2.MallocAsync(projectiveBls12377G2.Size(), 1, streamBls12377G2) + if e != runtime.Success { + errorString := fmt.Sprint( + "Bls12_377 Malloc G2 failed: ", e) + panic(errorString) + } + + println(time.Since(startTime).String()) + + print("Executing bn254 MSM on device ... ") + startTime = time.Now() + + currentDevice, _ := runtime.GetActiveDevice() + print("Device: ", currentDevice.GetDeviceType()) + + e = bn254Msm.Msm(scalarsBn254, pointsBn254, &cfgBn254, msmResultBn254) + if e != runtime.Success { + errorString := fmt.Sprint( + "bn254 Msm failed: ", e) + panic(errorString) + } + e = bn254G2.G2Msm(scalarsBn254, pointsBn254G2, &cfgBn254G2, msmResultBn254G2) + if e != runtime.Success { + errorString := fmt.Sprint( + "bn254 Msm G2 failed: ", e) + panic(errorString) + } + + msmResultBn254Host := make(core.HostSlice[bn254.Projective], 1) + msmResultBn254G2Host := make(core.HostSlice[bn254G2.G2Projective], 1) + + msmResultBn254Host.CopyFromDeviceAsync(&msmResultBn254, streamBn254) + msmResultBn254G2Host.CopyFromDeviceAsync(&msmResultBn254G2, streamBn254G2) + + msmResultBn254.FreeAsync(streamBn254) + msmResultBn254G2.FreeAsync(streamBn254G2) + + runtime.SynchronizeStream(streamBn254) + runtime.SynchronizeStream(streamBn254G2) + + println(time.Since(startTime).String()) + + print("Executing Bls12377 MSM on device ... ") + startTime = time.Now() + + currentDevice, _ = runtime.GetActiveDevice() + print("Device: ", currentDevice.GetDeviceType()) + + e = bls12377Msm.Msm(scalarsBls12377, pointsBls12377, &cfgBls12377, msmResultBls12377) + if e != runtime.Success { + errorString := fmt.Sprint( + "bls12_377 Msm failed: ", e) + panic(errorString) + } + e = bls12377G2.G2Msm(scalarsBls12377, pointsBls12377G2, &cfgBls12377G2, msmResultBls12377G2) + if e != runtime.Success { + errorString := fmt.Sprint( + "bls12_377 Msm G2 failed: ", e) + panic(errorString) + } + + msmResultBls12377Host := make(core.HostSlice[bls12377.Projective], 1) + msmResultBls12377G2Host := make(core.HostSlice[bls12377G2.G2Projective], 1) + + msmResultBls12377Host.CopyFromDeviceAsync(&msmResultBls12377, streamBls12377) + msmResultBls12377G2Host.CopyFromDeviceAsync(&msmResultBls12377G2, streamBls12377G2) + + msmResultBls12377.FreeAsync(streamBls12377) + msmResultBls12377G2.FreeAsync(streamBls12377G2) + + runtime.SynchronizeStream(streamBls12377) + runtime.SynchronizeStream(streamBls12377G2) + + println(time.Since(startTime).String()) + } +} diff --git a/examples/golang/msm/main.go b/examples/golang/msm/main.go index ffc2b407a..48658194c 100644 --- a/examples/golang/msm/main.go +++ b/examples/golang/msm/main.go @@ -5,27 +5,34 @@ import ( "fmt" "time" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12377" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377" - bls12377G2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12377/g2" - bls12377Msm "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12377/msm" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + bls12377G2 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377/g2" + bls12377Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" - bn254G2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" - bn254Msm "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/msm" + bn254G2 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" + bn254Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" ) func main() { + runtime.LoadBackendFromEnvOrDefault() + var logSizeMin int var logSizeMax int + var deviceType string - flag.IntVar(&logSizeMin, "l", 17, "Minimum log size") - flag.IntVar(&logSizeMax, "u", 22, "Maximum log size") + flag.IntVar(&logSizeMin, "l", 10, "Minimum log size") + flag.IntVar(&logSizeMax, "u", 10, "Maximum log size") + flag.StringVar(&deviceType, "device", "CUDA", "Device type") flag.Parse() + device := runtime.CreateDevice(deviceType, 0) + runtime.SetDevice(&device) + sizeMax := 1 << logSizeMax print("Generating BN254 scalars ... ") @@ -80,11 +87,11 @@ func main() { cfgBn254.IsAsync = true cfgBn254G2.IsAsync = true - streamBn254, _ := cr.CreateStream() - streamBn254G2, _ := cr.CreateStream() + streamBn254, _ := runtime.CreateStream() + streamBn254G2, _ := runtime.CreateStream() - cfgBn254.Ctx.Stream = &streamBn254 - cfgBn254G2.Ctx.Stream = &streamBn254G2 + cfgBn254.StreamHandle = streamBn254 + cfgBn254G2.StreamHandle = streamBn254G2 var projectiveBn254 bn254.Projective var projectiveBn254G2 bn254G2.G2Projective @@ -92,14 +99,14 @@ func main() { var msmResultBn254 core.DeviceSlice var msmResultBn254G2 core.DeviceSlice - _, e := msmResultBn254.MallocAsync(projectiveBn254.Size(), projectiveBn254.Size(), streamBn254) - if e != cr.CudaSuccess { + _, e := msmResultBn254.MallocAsync(projectiveBn254.Size(), 1, streamBn254) + if e != runtime.Success { errorString := fmt.Sprint( "Bn254 Malloc failed: ", e) panic(errorString) } - _, e = msmResultBn254G2.MallocAsync(projectiveBn254G2.Size(), projectiveBn254G2.Size(), streamBn254G2) - if e != cr.CudaSuccess { + _, e = msmResultBn254G2.MallocAsync(projectiveBn254G2.Size(), 1, streamBn254G2) + if e != runtime.Success { errorString := fmt.Sprint( "Bn254 Malloc G2 failed: ", e) panic(errorString) @@ -119,11 +126,11 @@ func main() { cfgBls12377.IsAsync = true cfgBls12377G2.IsAsync = true - streamBls12377, _ := cr.CreateStream() - streamBls12377G2, _ := cr.CreateStream() + streamBls12377, _ := runtime.CreateStream() + streamBls12377G2, _ := runtime.CreateStream() - cfgBls12377.Ctx.Stream = &streamBls12377 - cfgBls12377G2.Ctx.Stream = &streamBls12377G2 + cfgBls12377.StreamHandle = streamBls12377 + cfgBls12377G2.StreamHandle = streamBls12377G2 var projectiveBls12377 bls12377.Projective var projectiveBls12377G2 bls12377G2.G2Projective @@ -131,14 +138,14 @@ func main() { var msmResultBls12377 core.DeviceSlice var msmResultBls12377G2 core.DeviceSlice - _, e = msmResultBls12377.MallocAsync(projectiveBls12377.Size(), projectiveBls12377.Size(), streamBls12377) - if e != cr.CudaSuccess { + _, e = msmResultBls12377.MallocAsync(projectiveBls12377.Size(), 1, streamBls12377) + if e != runtime.Success { errorString := fmt.Sprint( "Bls12_377 Malloc failed: ", e) panic(errorString) } - _, e = msmResultBls12377G2.MallocAsync(projectiveBls12377G2.Size(), projectiveBls12377G2.Size(), streamBls12377G2) - if e != cr.CudaSuccess { + _, e = msmResultBls12377G2.MallocAsync(projectiveBls12377G2.Size(), 1, streamBls12377G2) + if e != runtime.Success { errorString := fmt.Sprint( "Bls12_377 Malloc G2 failed: ", e) panic(errorString) @@ -149,14 +156,17 @@ func main() { print("Executing bn254 MSM on device ... ") startTime = time.Now() + currentDevice, _ := runtime.GetActiveDevice() + print("Device: ", currentDevice.GetDeviceType()) + e = bn254Msm.Msm(scalarsBn254, pointsBn254, &cfgBn254, msmResultBn254) - if e != cr.CudaSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "bn254 Msm failed: ", e) panic(errorString) } e = bn254G2.G2Msm(scalarsBn254, pointsBn254G2, &cfgBn254G2, msmResultBn254G2) - if e != cr.CudaSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "bn254 Msm G2 failed: ", e) panic(errorString) @@ -171,22 +181,25 @@ func main() { msmResultBn254.FreeAsync(streamBn254) msmResultBn254G2.FreeAsync(streamBn254G2) - cr.SynchronizeStream(&streamBn254) - cr.SynchronizeStream(&streamBn254G2) + runtime.SynchronizeStream(streamBn254) + runtime.SynchronizeStream(streamBn254G2) println(time.Since(startTime).String()) print("Executing Bls12377 MSM on device ... ") startTime = time.Now() + currentDevice, _ = runtime.GetActiveDevice() + print("Device: ", currentDevice.GetDeviceType()) + e = bls12377Msm.Msm(scalarsBls12377, pointsBls12377, &cfgBls12377, msmResultBls12377) - if e != cr.CudaSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "bls12_377 Msm failed: ", e) panic(errorString) } e = bls12377G2.G2Msm(scalarsBls12377, pointsBls12377G2, &cfgBls12377G2, msmResultBls12377G2) - if e != cr.CudaSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "bls12_377 Msm G2 failed: ", e) panic(errorString) @@ -201,8 +214,8 @@ func main() { msmResultBls12377.FreeAsync(streamBls12377) msmResultBls12377G2.FreeAsync(streamBls12377G2) - cr.SynchronizeStream(&streamBls12377) - cr.SynchronizeStream(&streamBls12377G2) + runtime.SynchronizeStream(streamBls12377) + runtime.SynchronizeStream(streamBls12377G2) println(time.Since(startTime).String()) } diff --git a/examples/golang/ntt/main.go b/examples/golang/ntt/main.go index 4f1ee03ac..4594d5ecc 100644 --- a/examples/golang/ntt/main.go +++ b/examples/golang/ntt/main.go @@ -5,26 +5,33 @@ import ( "fmt" "time" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + runtime "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12377" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377" - bls12377Ntt "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12377/ntt" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + bls12377Ntt "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bls12377/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" - bn254Ntt "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/ntt" + bn254Ntt "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" bls12377Fft "github.com/consensys/gnark-crypto/ecc/bls12-377/fr/fft" bn254Fft "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" ) func main() { + runtime.LoadBackendFromEnvOrDefault() + var logSize int + var deviceType string flag.IntVar(&logSize, "s", 20, "Log size") + flag.StringVar(&deviceType, "device", "CUDA", "Device type") flag.Parse() + device := runtime.CreateDevice(deviceType, 0) + runtime.SetDevice(&device) + size := 1 << logSize fmt.Printf("---------------------- NTT size 2^%d=%d ------------------------\n", logSize, size) @@ -44,32 +51,33 @@ func main() { cfgBls12377 := bls12377Ntt.GetDefaultNttConfig() cfgBls12377.IsAsync = true + cfgInitDomainBls := core.GetDefaultNTTInitDomainConfig() rouMontBn254, _ := bn254Fft.Generator(uint64(size)) rouBn254 := rouMontBn254.Bits() rouIcicleBn254 := bn254.ScalarField{} limbsBn254 := core.ConvertUint64ArrToUint32Arr(rouBn254[:]) rouIcicleBn254.FromLimbs(limbsBn254) - bn254Ntt.InitDomain(rouIcicleBn254, cfgBn254.Ctx, false) + bn254Ntt.InitDomain(rouIcicleBn254, cfgInitDomainBls) rouMontBls12377, _ := bls12377Fft.Generator(uint64(size)) rouBls12377 := rouMontBls12377.Bits() rouIcicleBls12377 := bls12377.ScalarField{} limbsBls12377 := core.ConvertUint64ArrToUint32Arr(rouBls12377[:]) rouIcicleBls12377.FromLimbs(limbsBls12377) - bls12377Ntt.InitDomain(rouIcicleBls12377, cfgBls12377.Ctx, false) + bls12377Ntt.InitDomain(rouIcicleBls12377, cfgInitDomainBls) print("Configuring bn254 NTT ... ") startTime = time.Now() - streamBn254, _ := cr.CreateStream() + streamBn254, _ := runtime.CreateStream() - cfgBn254.Ctx.Stream = &streamBn254 + cfgBn254.StreamHandle = streamBn254 var nttResultBn254 core.DeviceSlice - _, e := nttResultBn254.MallocAsync(size*scalarsBn254.SizeOfElement(), scalarsBn254.SizeOfElement(), streamBn254) - if e != cr.CudaSuccess { + _, e := nttResultBn254.MallocAsync(size*scalarsBn254.SizeOfElement(), 1, streamBn254) + if e != runtime.Success { errorString := fmt.Sprint( "Bn254 Malloc failed: ", e) panic(errorString) @@ -80,14 +88,14 @@ func main() { print("Configuring Bls12377 NTT ... ") startTime = time.Now() - streamBls12377, _ := cr.CreateStream() + streamBls12377, _ := runtime.CreateStream() - cfgBls12377.Ctx.Stream = &streamBls12377 + cfgBls12377.StreamHandle = streamBls12377 var nttResultBls12377 core.DeviceSlice - _, e = nttResultBls12377.MallocAsync(size*scalarsBls12377.SizeOfElement(), scalarsBls12377.SizeOfElement(), streamBls12377) - if e != cr.CudaSuccess { + _, e = nttResultBls12377.MallocAsync(size*scalarsBls12377.SizeOfElement(), 1, streamBls12377) + if e != runtime.Success { errorString := fmt.Sprint( "Bls12_377 Malloc failed: ", e) panic(errorString) @@ -99,7 +107,7 @@ func main() { startTime = time.Now() err := bn254Ntt.Ntt(scalarsBn254, core.KForward, &cfgBn254, nttResultBn254) - if err.CudaErrorCode != cr.CudaSuccess { + if err != runtime.Success { errorString := fmt.Sprint( "bn254 Ntt failed: ", e) panic(errorString) @@ -108,14 +116,14 @@ func main() { nttResultBn254Host := make(core.HostSlice[bn254.ScalarField], size) nttResultBn254Host.CopyFromDeviceAsync(&nttResultBn254, streamBn254) nttResultBn254.FreeAsync(streamBn254) - cr.SynchronizeStream(&streamBn254) + runtime.SynchronizeStream(streamBn254) println(time.Since(startTime).String()) print("Executing Bls12377 NTT on device ... ") startTime = time.Now() err = bls12377Ntt.Ntt(scalarsBls12377, core.KForward, &cfgBls12377, nttResultBls12377) - if err.CudaErrorCode != cr.CudaSuccess { + if err != runtime.Success { errorString := fmt.Sprint( "bls12_377 Ntt failed: ", e) panic(errorString) @@ -125,7 +133,7 @@ func main() { nttResultBls12377Host.CopyFromDeviceAsync(&nttResultBls12377, streamBls12377) nttResultBls12377.FreeAsync(streamBls12377) - cr.SynchronizeStream(&streamBls12377) + runtime.SynchronizeStream(streamBls12377) println(time.Since(startTime).String()) } diff --git a/examples/golang/polynomials/main.go b/examples/golang/polynomials/main.go index 5238a92c3..563dfeedd 100644 --- a/examples/golang/polynomials/main.go +++ b/examples/golang/polynomials/main.go @@ -5,56 +5,61 @@ import ( "fmt" bn254Fft "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" - cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" - bn254Ntt "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/ntt" - bn254Polynomial "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/polynomial" - - "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" - babybear "github.com/ingonyama-zk/icicle/v2/wrappers/golang/fields/babybear" - babybearNtt "github.com/ingonyama-zk/icicle/v2/wrappers/golang/fields/babybear/ntt" - babybearPolynomial "github.com/ingonyama-zk/icicle/v2/wrappers/golang/fields/babybear/polynomial" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254Ntt "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + bn254Polynomial "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/polynomial" + runtime "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + babybear "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear" + babybearNtt "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/ntt" + babybearPolynomial "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/polynomial" ) var maxNttLogSize uint var polyLogSize uint -func initBn254Domain() core.IcicleError { - deviceCfg, _ := cr.GetDefaultDeviceContext() +func initBn254Domain() runtime.EIcicleError { + cfgInitDomain := core.GetDefaultNTTInitDomainConfig() rouMontBn254, _ := bn254Fft.Generator(uint64(1 << maxNttLogSize)) rouBn254 := rouMontBn254.Bits() rouIcicleBn254 := bn254.ScalarField{} limbsBn254 := core.ConvertUint64ArrToUint32Arr(rouBn254[:]) rouIcicleBn254.FromLimbs(limbsBn254) - return bn254Ntt.InitDomain(rouIcicleBn254, deviceCfg, false) + return bn254Ntt.InitDomain(rouIcicleBn254, cfgInitDomain) } -func initBabybearDomain() core.IcicleError { - deviceCfg, _ := cr.GetDefaultDeviceContext() +func initBabybearDomain() runtime.EIcicleError { + cfgInitDomain := core.GetDefaultNTTInitDomainConfig() rouIcicle := babybear.ScalarField{} rouIcicle.FromUint32(1461624142) - return babybearNtt.InitDomain(rouIcicle, deviceCfg, false) + return babybearNtt.InitDomain(rouIcicle, cfgInitDomain) } func init() { + runtime.LoadBackendFromEnvOrDefault() + + var deviceType string + flag.UintVar(&maxNttLogSize, "maxNttLogSize", 20, "") flag.UintVar(&polyLogSize, "polyLogSize", 15, "") + flag.StringVar(&deviceType, "device", "CUDA", "Device type") + + device := runtime.CreateDevice(deviceType, 0) + runtime.SetDevice(&device) e := initBn254Domain() - if e.IcicleErrorCode != core.IcicleSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "Bn254 Domain initialization failed: ", e) panic(errorString) } e = initBabybearDomain() - if e.IcicleErrorCode != core.IcicleSuccess { + if e != runtime.Success { errorString := fmt.Sprint( "Babybear Domain initialization failed: ", e) panic(errorString) } - - bn254Polynomial.InitPolyBackend() - babybearPolynomial.InitPolyBackend() } func main() { polySize := 1 << polyLogSize @@ -94,8 +99,8 @@ func main() { // in this example domain in on host and evals on device. hostDomain := core.HostSliceFromElements([]bn254.ScalarField{five, thirty}) var deviceImage core.DeviceSlice - _, err := deviceImage.Malloc(five.Size()*hostDomain.Len(), five.Size()) - if err != cr.CudaSuccess { + _, err := deviceImage.Malloc(five.Size(), hostDomain.Len()) + if err != runtime.Success { errorString := fmt.Sprint( "deviceImage allocation failed: ", err) panic(errorString) diff --git a/examples/rust/install-and-use-icicle/Cargo.toml b/examples/rust/install-and-use-icicle/Cargo.toml new file mode 100644 index 000000000..c1c83c0b5 --- /dev/null +++ b/examples/rust/install-and-use-icicle/Cargo.toml @@ -0,0 +1,11 @@ +[package] +name = "ntt" +version = "1.2.0" +edition = "2018" + +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="yshekel/V3" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="yshekel/V3" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="yshekel/V3" } + +[features] diff --git a/examples/rust/install-and-use-icicle/README.md b/examples/rust/install-and-use-icicle/README.md new file mode 100644 index 000000000..c371caa3b --- /dev/null +++ b/examples/rust/install-and-use-icicle/README.md @@ -0,0 +1,85 @@ + + +# Example: Install and use ICICLE + +This example shows how to install CUDA backend and use it in Rust application. + +Download release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases): +- **Backend** icicle30-ubuntu22-cuda122.tar.gz + +> [!NOTE] +> The names of the files are based on the release version. Ensure you update the tar file names in the example if you are using a different release. + +## Optional: Using Docker with Ubuntu 22 + +While not mandatory, this example can be demonstrated in an Ubuntu 22 Docker container. +```bash +docker run -it --rm --gpus all -v ./:/workspace -w /workspace icicle-release-ubuntu22-cuda122 bash +``` + +This command starts a bash session in the Docker container with GPUs enabled and the example files mapped to /workspace in the container. + +### Building the docker image + +The Docker image is based on NVIDIA’s image for Ubuntu 22.04 and can be built from the following Dockerfile: +```dockerfile +# Use the official NVIDIA development runtime image for Ubuntu 22.04 +FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + build-essential \ + cmake \ + tar + +RUN apt install cargo -y +``` + +Build the Docker image with the following command: +```bash +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 . +``` + +## Extract and Install the CUDA Backend + +```bash +cd release +# extract CUDA backend +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt +``` + +## Build the Rust Application and Execute + +Add the following dependencies to your Cargo.toml file: + +```cargo +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +``` + +Build and Run the Application + +```bash +cargo run --release +``` + +## Install in a Custom Location +If you prefer to install the CUDA backend in a custom location such as /custom/path, follow these steps: + +```bash +mkdir -p /custom/path +tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /custom/path +``` + +If installed in a custom location, you need to define the environment variable: + +```bash +export ICICLE_BACKEND_INSTALL_DIR=/custom/path/icicle/lib/backend +``` + +Alternatively, you can load the backend programmatically in your Rust code using: +```bash +pub fn load_backend(path: &str) -> Result<(), eIcicleError> +``` diff --git a/examples/rust/install-and-use-icicle/run.sh b/examples/rust/install-and-use-icicle/run.sh new file mode 100755 index 000000000..c2bed7d1b --- /dev/null +++ b/examples/rust/install-and-use-icicle/run.sh @@ -0,0 +1 @@ +echo "Check out the README file. You will have to download an ICICLE release and follow instructions" diff --git a/examples/rust/install-and-use-icicle/src/main.rs b/examples/rust/install-and-use-icicle/src/main.rs new file mode 100644 index 000000000..fa47012f0 --- /dev/null +++ b/examples/rust/install-and-use-icicle/src/main.rs @@ -0,0 +1,103 @@ +use icicle_babybear::field::{ScalarCfg, ScalarField}; +use icicle_core::{ + ntt::{self, get_root_of_unity, initialize_domain, ntt, NTTConfig}, + traits::{FieldImpl, GenerateRandom}, +}; +use icicle_runtime::memory::{DeviceVec, HostSlice}; +use icicle_runtime::{self, Device}; + +fn main() { + // Load installed backends + let _ = icicle_runtime::load_backend_from_env_or_default(); + + // Check if GPU is available + let device_cpu = Device::new("CPU", 0); + let mut device_gpu = Device::new("CUDA", 0); + let is_cuda_device_available = icicle_runtime::is_device_available(&device_gpu); + + if is_cuda_device_available { + println!("GPU is available"); + } else { + println!("GPU is not available, falling back to CPU only"); + device_gpu = device_cpu.clone(); + } + + // Example input (on host memory) for NTT + let log_ntt_size = 2; + let ntt_size = 1 << log_ntt_size; + let input_cpu = ScalarCfg::generate_random(ntt_size); + + // Allocate output on host memory + let mut output_cpu = vec![ScalarField::zero(); ntt_size]; + let root_of_unity = get_root_of_unity::(ntt_size as u64); + let ntt_config = NTTConfig::::default(); + + // Part 1: Running NTT on CPU + println!("Part 1: compute on CPU: "); + icicle_runtime::set_device(&device_cpu).expect("Failed to set device to CPU"); + initialize_domain(root_of_unity, &ntt::NTTInitDomainConfig::default()).expect("Failed to initialize NTT domain"); + ntt( + HostSlice::from_slice(&input_cpu), + ntt::NTTDir::kForward, + &ntt_config, + HostSlice::from_mut_slice(&mut output_cpu), + ) + .expect("NTT computation failed on CPU"); + println!("{:?}", output_cpu); + + // Part 2: Running NTT on GPU (from/to CPU memory) + println!("Part 2: compute on GPU (from/to CPU memory): "); + icicle_runtime::set_device(&device_gpu).expect("Failed to set device to GPU"); + initialize_domain(root_of_unity, &ntt::NTTInitDomainConfig::default()).expect("Failed to initialize NTT domain"); + ntt( + HostSlice::from_slice(&input_cpu), + ntt::NTTDir::kForward, + &ntt_config, + HostSlice::from_mut_slice(&mut output_cpu), + ) + .expect("NTT computation failed on GPU"); + println!("{:?}", output_cpu); + + // Part 2 (cont.): Compute on GPU (from/to GPU memory) + println!("Part 2: compute on GPU (from/to GPU memory): "); + let mut input_gpu = + DeviceVec::::device_malloc(ntt_size).expect("Failed to allocate device memory for input"); + let mut output_gpu = + DeviceVec::::device_malloc(ntt_size).expect("Failed to allocate device memory for output"); + input_gpu + .copy_from_host(HostSlice::from_slice(&input_cpu)) + .expect("Failed to copy data to GPU"); + ntt(&input_gpu[..], ntt::NTTDir::kForward, &ntt_config, &mut output_gpu[..]) + .expect("NTT computation failed on GPU memory"); + output_gpu + .copy_to_host(HostSlice::from_mut_slice(&mut output_cpu)) + .expect("Failed to copy data back to CPU"); + println!("{:?}", output_cpu); + + // Part 3: Using both CPU and GPU to compute NTT (GPU) and inverse INTT (CPU) + let mut output_intt_cpu = vec![ScalarField::zero(); ntt_size]; + + // Step 1: Compute NTT on GPU + println!("Part 3: compute NTT on GPU (NTT input): "); + icicle_runtime::set_device(&device_gpu).expect("Failed to set device to GPU"); + ntt( + HostSlice::from_slice(&input_cpu), + ntt::NTTDir::kForward, + &ntt_config, + HostSlice::from_mut_slice(&mut output_cpu), + ) + .expect("NTT computation failed on GPU"); + println!("{:?}", input_cpu); + + // Step 2: Compute INTT on CPU + println!("Part 3: compute INTT on CPU (INTT output): "); + icicle_runtime::set_device(&device_cpu).expect("Failed to set device to CPU"); + ntt( + HostSlice::from_slice(&output_cpu), + ntt::NTTDir::kInverse, + &ntt_config, + HostSlice::from_mut_slice(&mut output_intt_cpu), + ) + .expect("INTT computation failed on CPU"); + println!("{:?}", output_intt_cpu); +} diff --git a/examples/rust/msm/Cargo.toml b/examples/rust/msm/Cargo.toml index 11e151083..02ae4bbbc 100644 --- a/examples/rust/msm/Cargo.toml +++ b/examples/rust/msm/Cargo.toml @@ -4,15 +4,16 @@ version = "1.2.0" edition = "2018" [dependencies] -icicle-cuda-runtime = { path = "../../../wrappers/rust/icicle-cuda-runtime" } +icicle-runtime = { path = "../../../wrappers/rust/icicle-runtime" } icicle-core = { path = "../../../wrappers/rust/icicle-core" } -icicle-bn254 = { path = "../../../wrappers/rust/icicle-curves/icicle-bn254", features = ["g2"] } +icicle-bn254 = { path = "../../../wrappers/rust/icicle-curves/icicle-bn254" } icicle-bls12-377 = { path = "../../../wrappers/rust/icicle-curves/icicle-bls12-377" } -ark-bn254 = { version = "0.4.0", optional = true } -ark-bls12-377 = { version = "0.4.0", optional = true } -ark-ec = { version = "0.4.0", optional = true } clap = { version = "<=4.4.12", features = ["derive"] } [features] -arkworks = ["ark-bn254", "ark-bls12-377", "ark-ec", "icicle-core/arkworks", "icicle-bn254/arkworks", "icicle-bls12-377/arkworks"] -profile = [] +cuda = ["icicle-runtime/cuda_backend", + "icicle-bn254/cuda_backend", + "icicle-bls12-377/cuda_backend", + "icicle-bn254/no_ecntt", + "icicle-bls12-377/no_ecntt" +] diff --git a/examples/rust/msm/README.md b/examples/rust/msm/README.md index fca5fb40c..6dfdbef8a 100644 --- a/examples/rust/msm/README.md +++ b/examples/rust/msm/README.md @@ -2,10 +2,6 @@ `ICICLE` provides Rust bindings to CUDA-accelerated C++ implementation of [Multi-Scalar Multiplication](https://github.com/ingonyama-zk/ingopedia/blob/master/src/msm.md). -## Best Practices - -In order to save time and setting up prerequisites manually, we recommend running this example in our [ZKContainer](../../ZKContainer.md). - ## Usage ```rust @@ -28,29 +24,9 @@ In this example we use `BN254` curve. The function computes $result = \sum_{i=0} Running the example: ```sh -cargo run --release +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir ``` -You can add the `--feature arkworks,profile` flag to measure times of both ICICLE and arkworks. - -> [!NOTE] -> The default sizes are 2^19 - 2^23. You can change this by passing the `--lower_bound_log_size --upper_bound_log_size ` options. To change the size range to 2^21 - 2^24, run the example like this: -> ```sh -> cargo run --release -- -l 21 -u 24 -> ``` - -## Benchmarks - -These benchmarks were run on a 16 core 24 thread i9-12900k CPU and an RTX 3090 Ti GPU - -### Single BN254 MSM -| Library\Size | 2^19 | 2^20 | 2^21 | 2^22 | 2^23 | -|--------------|------|------|------|------|------| -| ICICLE | 10 ms | 11 ms | 21 ms | 39 ms | 77 ms | -| Arkworks | 284 ms | 540 ms | 1,152 ms | 2,320 ms | 4,491 ms | - -### Single BLS12377 MSM -| Library\Size | 2^19 | 2^20 | 2^21 | 2^22 | 2^23 | -|--------------|------|------|------|------|------| -| ICICLE | 9 ms | 14 ms | 25 ms | 48 ms | 93 ms | -| Arkworks | 490 ms | 918 ms | 1,861 ms | 3,624 ms | 7,191 ms | diff --git a/examples/rust/msm/run.sh b/examples/rust/msm/run.sh new file mode 100755 index 000000000..cf4345bef --- /dev/null +++ b/examples/rust/msm/run.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cargo build --release --features=cuda + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "./target/release/deps/icicle/lib/backend") + cargo run --release --features=cuda -- --device-type "${DEVICE_TYPE}" +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="${ICICLE_BACKEND_INSTALL_DIR}" + cargo run --release -- --device-type "${DEVICE_TYPE}" +fi diff --git a/examples/rust/msm/src/main.rs b/examples/rust/msm/src/main.rs index 1e5118fab..4f40e6709 100644 --- a/examples/rust/msm/src/main.rs +++ b/examples/rust/msm/src/main.rs @@ -1,48 +1,53 @@ -use icicle_bn254::curve::{CurveCfg, G1Projective, G2CurveCfg, G2Projective, ScalarCfg}; +use icicle_runtime::{ + memory::{DeviceVec, HostSlice}, + stream::IcicleStream, +}; +// Using both bn254 and bls12-377 curves use icicle_bls12_377::curve::{ CurveCfg as BLS12377CurveCfg, G1Projective as BLS12377G1Projective, ScalarCfg as BLS12377ScalarCfg, }; - -use icicle_cuda_runtime::{ - memory::{DeviceVec, HostSlice}, - stream::CudaStream, -}; - -use icicle_core::{curve::Curve, msm, traits::GenerateRandom}; - -#[cfg(feature = "arkworks")] -use icicle_core::traits::ArkConvertible; - -#[cfg(feature = "arkworks")] -use ark_bls12_377::{Fr as Bls12377Fr, G1Affine as Bls12377G1Affine, G1Projective as Bls12377ArkG1Projective}; -#[cfg(feature = "arkworks")] -use ark_bn254::{Fr as Bn254Fr, G1Affine as Bn254G1Affine, G1Projective as Bn254ArkG1Projective}; -#[cfg(feature = "arkworks")] -use ark_ec::scalar_mul::variable_base::VariableBaseMSM; - -#[cfg(feature = "profile")] -use std::time::Instant; +use icicle_bn254::curve::{CurveCfg, G1Projective, G2CurveCfg, G2Projective, ScalarCfg}; use clap::Parser; +use icicle_core::{curve::Curve, msm, traits::GenerateRandom}; #[derive(Parser, Debug)] struct Args { /// Lower bound (inclusive) of MSM sizes to run for - #[arg(short, long, default_value_t = 19)] + #[arg(short, long, default_value_t = 10)] lower_bound_log_size: u8, /// Upper bound of MSM sizes to run for - #[arg(short, long, default_value_t = 22)] + #[arg(short, long, default_value_t = 10)] upper_bound_log_size: u8, + + /// Device type (e.g., "CPU", "CUDA") + #[arg(short, long, default_value = "CPU")] + device_type: String, +} + +// Load backend and set device +fn try_load_and_set_backend_device(args: &Args) { + if args.device_type != "CPU" { + icicle_runtime::runtime::load_backend_from_env_or_default().unwrap(); + } + println!("Setting device {}", args.device_type); + let device = icicle_runtime::Device::new(&args.device_type, 0 /* =device_id*/); + icicle_runtime::set_device(&device).unwrap(); } fn main() { let args = Args::parse(); + println!("{:?}", args); + + try_load_and_set_backend_device(&args); + let lower_bound = args.lower_bound_log_size; let upper_bound = args.upper_bound_log_size; println!("Running Icicle Examples: Rust MSM"); - let upper_size = 1 << (upper_bound); + let upper_size = 1 << upper_bound; + println!("Generating random inputs on host for bn254..."); let upper_points = CurveCfg::generate_random_affine_points(upper_size); let g2_upper_points = G2CurveCfg::generate_random_affine_points(upper_size); @@ -56,59 +61,43 @@ fn main() { let log_size = i; let size = 1 << log_size; println!( - "---------------------- MSM size 2^{}={} ------------------------", + "---------------------- MSM size 2^{} = {} ------------------------", log_size, size ); + // Setting Bn254 points and scalars let points = HostSlice::from_slice(&upper_points[..size]); let g2_points = HostSlice::from_slice(&g2_upper_points[..size]); let scalars = HostSlice::from_slice(&upper_scalars[..size]); // Setting bls12377 points and scalars - // let points_bls12377 = &upper_points_bls12377[..size]; - let points_bls12377 = HostSlice::from_slice(&upper_points_bls12377[..size]); // &upper_points_bls12377[..size]; + let points_bls12377 = HostSlice::from_slice(&upper_points_bls12377[..size]); let scalars_bls12377 = HostSlice::from_slice(&upper_scalars_bls12377[..size]); println!("Configuring bn254 MSM..."); - let mut msm_results = DeviceVec::::cuda_malloc(1).unwrap(); - let mut g2_msm_results = DeviceVec::::cuda_malloc(1).unwrap(); - let stream = CudaStream::create().unwrap(); - let g2_stream = CudaStream::create().unwrap(); + let mut msm_results = DeviceVec::::device_malloc(1).unwrap(); + let mut g2_msm_results = DeviceVec::::device_malloc(1).unwrap(); + let mut stream = IcicleStream::create().unwrap(); + let mut g2_stream = IcicleStream::create().unwrap(); let mut cfg = msm::MSMConfig::default(); let mut g2_cfg = msm::MSMConfig::default(); - cfg.ctx - .stream = &stream; - g2_cfg - .ctx - .stream = &g2_stream; + cfg.stream_handle = *stream; cfg.is_async = true; + g2_cfg.stream_handle = *g2_stream; g2_cfg.is_async = true; println!("Configuring bls12377 MSM..."); - let mut msm_results_bls12377 = DeviceVec::::cuda_malloc(1).unwrap(); - let stream_bls12377 = CudaStream::create().unwrap(); + let mut msm_results_bls12377 = DeviceVec::::device_malloc(1).unwrap(); + let mut stream_bls12377 = IcicleStream::create().unwrap(); let mut cfg_bls12377 = msm::MSMConfig::default(); - cfg_bls12377 - .ctx - .stream = &stream_bls12377; + cfg_bls12377.stream_handle = *stream_bls12377; cfg_bls12377.is_async = true; println!("Executing bn254 MSM on device..."); - #[cfg(feature = "profile")] - let start = Instant::now(); msm::msm(scalars, points, &cfg, &mut msm_results[..]).unwrap(); - #[cfg(feature = "profile")] - println!( - "ICICLE BN254 MSM on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); msm::msm(scalars, g2_points, &g2_cfg, &mut g2_msm_results[..]).unwrap(); println!("Executing bls12377 MSM on device..."); - #[cfg(feature = "profile")] - let start = Instant::now(); msm::msm( scalars_bls12377, points_bls12377, @@ -116,15 +105,8 @@ fn main() { &mut msm_results_bls12377[..], ) .unwrap(); - #[cfg(feature = "profile")] - println!( - "ICICLE BLS12377 MSM on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); - println!("Moving results to host.."); + println!("Moving results to host..."); let mut msm_host_result = vec![G1Projective::zero(); 1]; let mut g2_msm_host_result = vec![G2Projective::zero(); 1]; let mut msm_host_result_bls12377 = vec![BLS12377G1Projective::zero(); 1]; @@ -132,16 +114,17 @@ fn main() { stream .synchronize() .unwrap(); - g2_stream - .synchronize() - .unwrap(); msm_results .copy_to_host(HostSlice::from_mut_slice(&mut msm_host_result[..])) .unwrap(); + println!("bn254 result: {:#?}", msm_host_result); + + g2_stream + .synchronize() + .unwrap(); g2_msm_results .copy_to_host(HostSlice::from_mut_slice(&mut g2_msm_host_result[..])) .unwrap(); - println!("bn254 result: {:#?}", msm_host_result); println!("G2 bn254 result: {:#?}", g2_msm_host_result); stream_bls12377 @@ -152,69 +135,14 @@ fn main() { .unwrap(); println!("bls12377 result: {:#?}", msm_host_result_bls12377); - #[cfg(feature = "arkworks")] - { - println!("Checking against arkworks..."); - let ark_points: Vec = points - .iter() - .map(|&point| point.to_ark()) - .collect(); - let ark_scalars: Vec = scalars - .iter() - .map(|scalar| scalar.to_ark()) - .collect(); - - let ark_points_bls12377: Vec = points_bls12377 - .iter() - .map(|point| point.to_ark()) - .collect(); - let ark_scalars_bls12377: Vec = scalars_bls12377 - .iter() - .map(|scalar| scalar.to_ark()) - .collect(); - - #[cfg(feature = "profile")] - let start = Instant::now(); - let bn254_ark_msm_res = Bn254ArkG1Projective::msm(&ark_points, &ark_scalars).unwrap(); - println!("Arkworks Bn254 result: {:#?}", bn254_ark_msm_res); - #[cfg(feature = "profile")] - println!( - "Ark BN254 MSM on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); - - #[cfg(feature = "profile")] - let start = Instant::now(); - let bls12377_ark_msm_res = - Bls12377ArkG1Projective::msm(&ark_points_bls12377, &ark_scalars_bls12377).unwrap(); - println!("Arkworks Bls12377 result: {:#?}", bls12377_ark_msm_res); - #[cfg(feature = "profile")] - println!( - "Ark BLS12377 MSM on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); - - let bn254_icicle_msm_res_as_ark = msm_host_result[0].to_ark(); - let bls12377_icicle_msm_res_as_ark = msm_host_result_bls12377[0].to_ark(); - - println!( - "Bn254 MSM is correct: {}", - bn254_ark_msm_res.eq(&bn254_icicle_msm_res_as_ark) - ); - println!( - "Bls12377 MSM is correct: {}", - bls12377_ark_msm_res.eq(&bls12377_icicle_msm_res_as_ark) - ); - } - println!("Cleaning up bn254..."); stream .destroy() .unwrap(); + g2_stream + .destroy() + .unwrap(); + println!("Cleaning up bls12377..."); stream_bls12377 .destroy() diff --git a/examples/rust/ntt/Cargo.toml b/examples/rust/ntt/Cargo.toml index 551fcd6f9..081a581b4 100644 --- a/examples/rust/ntt/Cargo.toml +++ b/examples/rust/ntt/Cargo.toml @@ -4,17 +4,19 @@ version = "1.2.0" edition = "2018" [dependencies] -icicle-cuda-runtime = { path = "../../../wrappers/rust/icicle-cuda-runtime" } -icicle-core = { path = "../../../wrappers/rust/icicle-core", features = ["arkworks"] } -icicle-bn254 = { path = "../../../wrappers/rust/icicle-curves/icicle-bn254", features = ["arkworks"] } -icicle-bls12-377 = { path = "../../../wrappers/rust/icicle-curves/icicle-bls12-377", features = ["arkworks"] } +icicle-runtime = { path = "../../../wrappers/rust/icicle-runtime" } +icicle-core = { path = "../../../wrappers/rust/icicle-core" } +icicle-bn254 = { path = "../../../wrappers/rust/icicle-curves/icicle-bn254" } +icicle-bls12-377 = { path = "../../../wrappers/rust/icicle-curves/icicle-bls12-377" } -ark-ff = { version = "0.4.0" } -ark-poly = "0.4.0" -ark-std = "0.4.0" -ark-bn254 = { version = "0.4.0" } -ark-bls12-377 = { version = "0.4.0" } clap = { version = "<=4.4.12", features = ["derive"] } [features] -profile = [] +cuda = ["icicle-runtime/cuda_backend", + "icicle-bn254/cuda_backend", + "icicle-bn254/no_ecntt", + "icicle-bn254/no_g2", + "icicle-bls12-377/cuda_backend", + "icicle-bls12-377/no_ecntt", + "icicle-bls12-377/no_g2" +] diff --git a/examples/rust/ntt/README.md b/examples/rust/ntt/README.md index 223e91f3e..f7e6c6bcb 100644 --- a/examples/rust/ntt/README.md +++ b/examples/rust/ntt/README.md @@ -4,10 +4,6 @@ `ICICLE` provides Rust bindings to CUDA-accelerated C++ implementation of [Number Theoretic Transform](https://github.com/ingonyama-zk/ingopedia/blob/master/src/fft.md). -## Best Practices - -In order to save time and setting up prerequisites manually, we recommend running this example in our [ZKContainer](../../ZKContainer.md). - ## Usage ```rust @@ -32,34 +28,9 @@ In this example we use the `BN254` and `BLS12377` fields. 7. Compare results with arkworks Running the example: - -```sh -cargo run --release -``` - -You can add the `--feature profile` flag to measure times of both ICICLE and arkworks. - -> [!NOTE] -> The default size is 2^20. You can change this by passing the `--size ` option. To change the size to 2^23, run the example like this: - ```sh -cargo run --release -- -s 23 +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir ``` - -## Benchmarks - -These benchmarks were run on a 16 core 24 thread i9-12900k CPU and an RTX 3090 Ti GPU - -### Single BN254 NTT - -| Library\Size | 2^19 | 2^20 | 2^21 | 2^22 | 2^23 | -|--------------|------|------|------|------|------| -| ICICLE | 1.263 ms | 2.986 ms | 4.651 ms | 9.308 ms | 18.618 ms | -| Arkworks | 138 ms | 290 ms | 611 ms | 1,295 ms | 2,715 ms | - -### Single BLS12377 NTT - -| Library\Size | 2^19 | 2^20 | 2^21 | 2^22 | 2^23 | -|--------------|------|------|------|------|------| -| ICICLE | 1.272 ms | 2.893 ms | 4.728 ms | 9.211 ms | 18.319 ms | -| Arkworks | 135 ms | 286 ms | 605 ms | 1,279 ms | 2,682 ms | diff --git a/examples/rust/ntt/run.sh b/examples/rust/ntt/run.sh new file mode 100755 index 000000000..4f987d96b --- /dev/null +++ b/examples/rust/ntt/run.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cargo build --release --features=cuda + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "./target/release/deps/icicle/lib/backend") + cargo run --release --features=cuda -- --device-type "${DEVICE_TYPE}" +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="$ICICLE_BACKEND_INSTALL_DIR"; + cargo run --release -- --device-type "${DEVICE_TYPE}" +fi diff --git a/examples/rust/ntt/src/main.rs b/examples/rust/ntt/src/main.rs index 9f560099f..ea6a303fd 100644 --- a/examples/rust/ntt/src/main.rs +++ b/examples/rust/ntt/src/main.rs @@ -1,93 +1,87 @@ -use icicle_bn254::curve::{ScalarCfg, ScalarField}; - use icicle_bls12_377::curve::{ScalarCfg as BLS12377ScalarCfg, ScalarField as BLS12377ScalarField}; +use icicle_bn254::curve::{ScalarCfg as Bn254ScalarCfg, ScalarField as Bn254ScalarField}; +use icicle_runtime::memory::{DeviceVec, HostSlice}; -use icicle_cuda_runtime::{ - device_context::DeviceContext, - memory::{DeviceVec, HostSlice}, - stream::CudaStream, -}; - +use clap::Parser; use icicle_core::{ ntt::{self, initialize_domain}, traits::{FieldImpl, GenerateRandom}, }; - -use icicle_core::traits::ArkConvertible; - -use ark_bls12_377::Fr as Bls12377Fr; -use ark_bn254::Fr as Bn254Fr; -use ark_ff::FftField; -use ark_poly::{EvaluationDomain, Radix2EvaluationDomain}; -use ark_std::cmp::{Ord, Ordering}; use std::convert::TryInto; - -#[cfg(feature = "profile")] use std::time::Instant; -use clap::Parser; - #[derive(Parser, Debug)] struct Args { /// Size of NTT to run (20 for 2^20) #[arg(short, long, default_value_t = 20)] size: u8, + + /// Device type (e.g., "CPU", "CUDA") + #[arg(short, long, default_value = "CPU")] + device_type: String, +} + +// Load backend and set device +fn try_load_and_set_backend_device(args: &Args) { + if args.device_type != "CPU" { + icicle_runtime::runtime::load_backend_from_env_or_default().unwrap(); + } + println!("Setting device {}", args.device_type); + let device = icicle_runtime::Device::new(&args.device_type, 0 /* =device_id*/); + icicle_runtime::set_device(&device).unwrap(); } fn main() { let args = Args::parse(); + println!("{:?}", args); + + try_load_and_set_backend_device(&args); + println!("Running Icicle Examples: Rust NTT"); let log_size = args.size; let size = 1 << log_size; println!( - "---------------------- NTT size 2^{}={} ------------------------", + "---------------------- NTT size 2^{} = {} ------------------------", log_size, size ); + // Setting Bn254 points and scalars println!("Generating random inputs on host for bn254..."); - let scalars = ScalarCfg::generate_random(size); - let mut ntt_results = DeviceVec::::cuda_malloc(size).unwrap(); + let scalars = Bn254ScalarCfg::generate_random(size); + let mut ntt_results = DeviceVec::::device_malloc(size).unwrap(); // Setting bls12377 points and scalars println!("Generating random inputs on host for bls12377..."); let scalars_bls12377 = BLS12377ScalarCfg::generate_random(size); - let mut ntt_results_bls12377 = DeviceVec::::cuda_malloc(size).unwrap(); + let mut ntt_results_bls12377 = DeviceVec::::device_malloc(size).unwrap(); println!("Setting up bn254 Domain..."); - let icicle_omega = ::get_root_of_unity( - size.try_into() - .unwrap(), + initialize_domain( + ntt::get_root_of_unity::( + size.try_into() + .unwrap(), + ), + &ntt::NTTInitDomainConfig::default(), ) .unwrap(); - let ctx = DeviceContext::default(); - initialize_domain(ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); println!("Configuring bn254 NTT..."); - let stream = CudaStream::create().unwrap(); - let mut cfg = ntt::NTTConfig::<'_, ScalarField>::default(); - cfg.ctx - .stream = &stream; - cfg.is_async = true; + let cfg = ntt::NTTConfig::::default(); println!("Setting up bls12377 Domain..."); - let icicle_omega = ::get_root_of_unity( - size.try_into() - .unwrap(), + initialize_domain( + ntt::get_root_of_unity::( + size.try_into() + .unwrap(), + ), + &ntt::NTTInitDomainConfig::default(), ) .unwrap(); - // reusing ctx from above - initialize_domain(BLS12377ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); println!("Configuring bls12377 NTT..."); - let stream_bls12377 = CudaStream::create().unwrap(); - let mut cfg_bls12377 = ntt::NTTConfig::<'_, BLS12377ScalarField>::default(); - cfg_bls12377 - .ctx - .stream = &stream_bls12377; - cfg_bls12377.is_async = true; + let cfg_bls12377 = ntt::NTTConfig::::default(); println!("Executing bn254 NTT on device..."); - #[cfg(feature = "profile")] let start = Instant::now(); ntt::ntt( HostSlice::from_slice(&scalars), @@ -96,7 +90,6 @@ fn main() { &mut ntt_results[..], ) .unwrap(); - #[cfg(feature = "profile")] println!( "ICICLE BN254 NTT on size 2^{log_size} took: {} μs", start @@ -105,7 +98,6 @@ fn main() { ); println!("Executing bls12377 NTT on device..."); - #[cfg(feature = "profile")] let start = Instant::now(); ntt::ntt( HostSlice::from_slice(&scalars_bls12377), @@ -114,7 +106,6 @@ fn main() { &mut ntt_results_bls12377[..], ) .unwrap(); - #[cfg(feature = "profile")] println!( "ICICLE BLS12377 NTT on size 2^{log_size} took: {} μs", start @@ -122,82 +113,14 @@ fn main() { .as_micros() ); - println!("Moving results to host.."); - stream - .synchronize() - .unwrap(); - let mut host_bn254_results = vec![ScalarField::zero(); size]; + println!("Moving results to host..."); + let mut host_bn254_results = vec![Bn254ScalarField::zero(); size]; ntt_results .copy_to_host(HostSlice::from_mut_slice(&mut host_bn254_results[..])) .unwrap(); - stream_bls12377 - .synchronize() - .unwrap(); let mut host_bls12377_results = vec![BLS12377ScalarField::zero(); size]; ntt_results_bls12377 .copy_to_host(HostSlice::from_mut_slice(&mut host_bls12377_results[..])) .unwrap(); - - println!("Checking against arkworks..."); - let mut ark_scalars: Vec = scalars - .iter() - .map(|scalar| scalar.to_ark()) - .collect(); - let bn254_domain = as EvaluationDomain>::new(size).unwrap(); - - let mut ark_scalars_bls12377: Vec = scalars_bls12377 - .iter() - .map(|scalar| scalar.to_ark()) - .collect(); - let bls12_377_domain = as EvaluationDomain>::new(size).unwrap(); - - #[cfg(feature = "profile")] - let start = Instant::now(); - bn254_domain.fft_in_place(&mut ark_scalars); - #[cfg(feature = "profile")] - println!( - "Ark BN254 NTT on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); - - #[cfg(feature = "profile")] - let start = Instant::now(); - bls12_377_domain.fft_in_place(&mut ark_scalars_bls12377); - #[cfg(feature = "profile")] - println!( - "Ark BLS12377 NTT on size 2^{log_size} took: {} ms", - start - .elapsed() - .as_millis() - ); - - host_bn254_results - .iter() - .zip(ark_scalars.iter()) - .for_each(|(icicle_scalar, &ark_scalar)| { - assert_eq!(ark_scalar.cmp(&icicle_scalar.to_ark()), Ordering::Equal); - }); - println!("Bn254 NTT is correct"); - - host_bls12377_results - .iter() - .zip(ark_scalars_bls12377.iter()) - .for_each(|(icicle_scalar, &ark_scalar)| { - assert_eq!(ark_scalar.cmp(&icicle_scalar.to_ark()), Ordering::Equal); - }); - - println!("Bls12377 NTT is correct"); - - println!("Cleaning up bn254..."); - stream - .destroy() - .unwrap(); - println!("Cleaning up bls12377..."); - stream_bls12377 - .destroy() - .unwrap(); - println!(""); } diff --git a/examples/rust/polynomials/Cargo.toml b/examples/rust/polynomials/Cargo.toml index 2e60481ac..057a3677c 100644 --- a/examples/rust/polynomials/Cargo.toml +++ b/examples/rust/polynomials/Cargo.toml @@ -4,11 +4,18 @@ version = "1.2.0" edition = "2018" [dependencies] -icicle-cuda-runtime = { path = "../../../wrappers/rust/icicle-cuda-runtime" } +icicle-runtime = { path = "../../../wrappers/rust/icicle-runtime" } icicle-core = { path = "../../../wrappers/rust/icicle-core" } icicle-bn254 = { path = "../../../wrappers/rust/icicle-curves/icicle-bn254" } icicle-babybear = { path = "../../../wrappers/rust/icicle-fields/icicle-babybear" } + clap = { version = "<=4.4.12", features = ["derive"] } [features] -profile = [] +cuda = ["icicle-runtime/cuda_backend", + "icicle-bn254/cuda_backend", + "icicle-babybear/cuda_backend", + "icicle-bn254/no_ecntt", + "icicle-bn254/no_g2", +] + diff --git a/examples/rust/polynomials/README.md b/examples/rust/polynomials/README.md new file mode 100644 index 000000000..b88422910 --- /dev/null +++ b/examples/rust/polynomials/README.md @@ -0,0 +1,15 @@ +# ICICLE example: Polynomial API + +## Key-Takeaway + +`ICICLE` provides Rust bindings to Polynomial API [https://dev.ingonyama.com/icicle/rust-bindings/polynomials] + +In this example we use the `BN254` and `babybear` fields to demonstrate how to compute on polynomials. + +Running the example: +```sh +# for CPU +./run.sh -d CPU +# for CUDA +./run.sh -d CUDA -b /path/to/cuda/backend/install/dir +``` diff --git a/examples/rust/polynomials/run.sh b/examples/rust/polynomials/run.sh new file mode 100755 index 000000000..4f987d96b --- /dev/null +++ b/examples/rust/polynomials/run.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# Exit immediately if a command exits with a non-zero status +set -e + +# Function to display usage information +show_help() { + echo "Usage: $0 [-d DEVICE_TYPE] [-b ICICLE_BACKEND_INSTALL_DIR]" + echo + echo "Options:" + echo " -d DEVICE_TYPE Specify the device type (default: CPU)" + echo " -b ICICLE_BACKEND_INSTALL_DIR Specify the backend installation directory (default: empty)" + echo " -h Show this help message" + exit 0 +} + +# Parse command line options +while getopts ":d:b:h" opt; do + case ${opt} in + d ) + DEVICE_TYPE=$OPTARG + ;; + b ) + ICICLE_BACKEND_INSTALL_DIR="$(realpath ${OPTARG})" + ;; + h ) + show_help + ;; + \? ) + echo "Invalid option: -$OPTARG" 1>&2 + show_help + ;; + : ) + echo "Invalid option: -$OPTARG requires an argument" 1>&2 + show_help + ;; + esac +done + +# Set default values if not provided +: "${DEVICE_TYPE:=CPU}" +: "${ICICLE_BACKEND_INSTALL_DIR:=}" + +# Create necessary directories +mkdir -p build/example +mkdir -p build/icicle + +ICILE_DIR=$(realpath "../../../icicle/") +ICICLE_CUDA_SOURCE_DIR="${ICILE_DIR}/backend/cuda" + +# Build Icicle and the example app that links to it +if [ "$DEVICE_TYPE" == "CUDA" ] && [ ! -d "${ICICLE_BACKEND_INSTALL_DIR}" ] && [ -d "${ICICLE_CUDA_SOURCE_DIR}" ]; then + echo "Building icicle with CUDA backend" + cargo build --release --features=cuda + export ICICLE_BACKEND_INSTALL_DIR=$(realpath "./target/release/deps/icicle/lib/backend") + cargo run --release --features=cuda -- --device-type "${DEVICE_TYPE}" +else + echo "Building icicle without CUDA backend, ICICLE_BACKEND_INSTALL_DIR=${ICICLE_BACKEND_INSTALL_DIR}" + export ICICLE_BACKEND_INSTALL_DIR="$ICICLE_BACKEND_INSTALL_DIR"; + cargo run --release -- --device-type "${DEVICE_TYPE}" +fi diff --git a/examples/rust/polynomials/src/main.rs b/examples/rust/polynomials/src/main.rs index 1e0e67723..377f4a7d4 100644 --- a/examples/rust/polynomials/src/main.rs +++ b/examples/rust/polynomials/src/main.rs @@ -3,21 +3,16 @@ use icicle_babybear::polynomials::DensePolynomial as PolynomialBabyBear; use icicle_bn254::curve::ScalarField as bn254Scalar; use icicle_bn254::polynomials::DensePolynomial as PolynomialBn254; -use icicle_cuda_runtime::{ - device_context::DeviceContext, - memory::{DeviceVec, HostSlice}, -}; +use icicle_runtime::memory::{DeviceVec, HostSlice}; use icicle_core::{ - ntt::{get_root_of_unity, initialize_domain}, + ntt::{get_root_of_unity, initialize_domain, NTTInitDomainConfig}, polynomials::UnivariatePolynomial, traits::{FieldImpl, GenerateRandom}, }; -#[cfg(feature = "profile")] -use std::time::Instant; - use clap::Parser; +use std::time::Instant; #[derive(Parser, Debug)] struct Args { @@ -26,21 +21,33 @@ struct Args { max_ntt_log_size: u8, #[arg(short, long, default_value_t = 15)] poly_log_size: u8, + + /// Device type (e.g., "CPU", "CUDA") + #[arg(short, long, default_value = "CPU")] + device_type: String, +} + +// Load backend and set device +fn try_load_and_set_backend_device(args: &Args) { + if args.device_type != "CPU" { + icicle_runtime::runtime::load_backend_from_env_or_default().unwrap(); + } + println!("Setting device {}", args.device_type); + let device = icicle_runtime::Device::new(&args.device_type, 0 /* =device_id*/); + icicle_runtime::set_device(&device).unwrap(); } -fn init(max_ntt_size: u64) { - // initialize NTT domain for all fields!. Polynomials ops relies on NTT. +fn init_ntt_domain(max_ntt_size: u64) { + // Initialize NTT domain for all fields. Polynomial operations rely on NTT. + println!( + "Initializing NTT domain for max size 2^{}", + max_ntt_size.trailing_zeros() + ); let rou_bn254: bn254Scalar = get_root_of_unity(max_ntt_size); - let ctx = DeviceContext::default(); - initialize_domain(rou_bn254, &ctx, false /*=fast twiddles mode*/).unwrap(); + initialize_domain(rou_bn254, &NTTInitDomainConfig::default()).unwrap(); let rou_babybear: babybearScalar = get_root_of_unity(max_ntt_size); - initialize_domain(rou_babybear, &ctx, false /*=fast twiddles mode*/).unwrap(); - - // initialize the cuda backend for polynomials - // make sure to initialize it per field - PolynomialBn254::init_cuda_backend(); - PolynomialBabyBear::init_cuda_backend(); + initialize_domain(rou_babybear, &NTTInitDomainConfig::default()).unwrap(); } fn randomize_poly

(size: usize, from_coeffs: bool) -> P @@ -49,6 +56,7 @@ where P::Field: FieldImpl, P::FieldConfig: GenerateRandom, { + println!("Randomizing polynomial of size {} (from_coeffs: {})", size, from_coeffs); let coeffs_or_evals = P::FieldConfig::generate_random(size); let p = if from_coeffs { P::from_coeffs(HostSlice::from_slice(&coeffs_or_evals), size) @@ -60,42 +68,61 @@ where fn main() { let args = Args::parse(); - init(1 << args.max_ntt_log_size); + println!("{:?}", args); + + try_load_and_set_backend_device(&args); + + init_ntt_domain(1 << args.max_ntt_log_size); - // randomize three polynomials f,g,h over bn254 scalar field let poly_size = 1 << args.poly_log_size; + + println!("Randomizing polynomials [f(x),g(x),h(x)] over bn254 scalar field..."); let f = randomize_poly::(poly_size, true /*from random coeffs*/); let g = randomize_poly::(poly_size / 2, true /*from random coeffs*/); let h = randomize_poly::(poly_size / 4, false /*from random evaluations on rou*/); - // randomize two polynomials over babybear field + println!("Randomizing polynomials [f_babybear(x), g_babyber(x)] over babybear field..."); let f_babybear = randomize_poly::(poly_size, true /*from random coeffs*/); let g_babybear = randomize_poly::(poly_size / 2, true /*from random coeffs*/); + let start = Instant::now(); // Arithmetic + println!("Computing t0(x) = f(x) + g(x)"); let t0 = &f + &g; + println!("Computing t1(x) f(x) * h(x)"); let t1 = &f * &h; - let (q, r) = t1.divide(&t0); // computes q,r for t1(x)=q(x)*t0(x)+r(x) + println!("Computing q(x),r(x) = t1(x)/t0(x) (where t1(x) = q(x) * t0(x) + r(x))"); + let (q, r) = t1.divide(&t0); + println!("Computing f_babybear(x) * g_babybear(x)"); let _r_babybear = &f_babybear * &g_babybear; - // check degree - let _r_degree = r.degree(); + // Check degree + println!("Degree of r(x): {}", r.degree()); - // evaluate in single domain point + // Evaluate in single domain point let five = bn254Scalar::from_u32(5); + println!("Evaluating q(5)"); let q_at_five = q.eval(&five); - // evaluate on domain. Note: domain and image can be either Host or Device slice. - // in this example domain in on host and evals on device. + // Evaluate on domain let host_domain = [five, bn254Scalar::from_u32(30)]; - let mut device_image = DeviceVec::::cuda_malloc(host_domain.len()).unwrap(); - t1.eval_on_domain(HostSlice::from_slice(&host_domain), &mut device_image[..]); + let mut device_image = DeviceVec::::device_malloc(host_domain.len()).unwrap(); + println!("Evaluating t1(x) on domain {:?}", host_domain); + t1.eval_on_domain(HostSlice::from_slice(&host_domain), &mut device_image[..]); // for NTT use eval_on_rou_domain() - // slicing + // Slicing + println!("Performing slicing operations on h"); let o = h.odd(); let e = h.even(); - let fold = &e + &(&o * &q_at_five); // e(x) + o(x)*scalar + let fold = &e + &(&o * &q_at_five); // e(x) + o(x) * scalar + + let _coeff = fold.get_coeff(2); // Coeff of x^2 - let _coeff = fold.get_coeff(2); // coeff of x^2 + println!( + "Polynomial computation on selected device took: {} ms", + start + .elapsed() + .as_millis() + ); } diff --git a/examples/rust/poseidon/.devcontainer/Dockerfile b/examples/rust/poseidon/.devcontainer/Dockerfile deleted file mode 100644 index 1af12c34a..000000000 --- a/examples/rust/poseidon/.devcontainer/Dockerfile +++ /dev/null @@ -1,27 +0,0 @@ -# Use the specified base image -#FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 -FROM nvidia/cuda:12.0.0-devel-ubuntu22.04 - -# Update and install dependencies -RUN apt-get update && apt-get install -y \ - cmake \ - protobuf-compiler \ - curl \ - build-essential \ - git \ - llvm \ - clang \ - && rm -rf /var/lib/apt/lists/* - -# Install Rust -RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y -ENV PATH="/root/.cargo/bin:${PATH}" - -# Set the working directory in the container -WORKDIR /icicle-example - -# Copy the content of the local directory to the working directory -COPY . . - -# Specify the default command for the container -CMD ["/bin/bash"] \ No newline at end of file diff --git a/examples/rust/poseidon/.devcontainer/devcontainer.json b/examples/rust/poseidon/.devcontainer/devcontainer.json deleted file mode 100644 index 63e34f9b9..000000000 --- a/examples/rust/poseidon/.devcontainer/devcontainer.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "name": "Icicle Examples: rust poseidon", - "build": { - "dockerfile": "Dockerfile" - }, - "runArgs": [ - "--gpus", - "all" - ], - "postCreateCommand": [ - "nvidia-smi" - ], - "customizations": { - "vscode": { - "extensions": [ - "ms-vscode.cmake-tools", - "ms-azuretools.vscode-docker", - "rust-lang.rust-analyzer", - "vadimcn.vscode-lldb" - ] - } - } -} diff --git a/examples/rust/poseidon/Cargo.toml b/examples/rust/poseidon/Cargo.toml deleted file mode 100644 index 1ae3bed8b..000000000 --- a/examples/rust/poseidon/Cargo.toml +++ /dev/null @@ -1,14 +0,0 @@ -[package] -name = "poseidon" -version = "1.4.0" -edition = "2018" - -[dependencies] -icicle-cuda-runtime = { path = "../../../wrappers/rust/icicle-cuda-runtime" } -icicle-core = { path = "../../../wrappers/rust/icicle-core" } -icicle-bls12-381 = { path = "../../../wrappers/rust/icicle-curves/icicle-bls12-381" } - -clap = { version = "<=4.4.12", features = ["derive"] } - -[features] -profile = [] diff --git a/examples/rust/poseidon/src/main.rs b/examples/rust/poseidon/src/main.rs deleted file mode 100644 index 17dd36f3d..000000000 --- a/examples/rust/poseidon/src/main.rs +++ /dev/null @@ -1,65 +0,0 @@ -use icicle_bls12_381::curve::ScalarField as F; - -use icicle_cuda_runtime::device_context::DeviceContext; - -use icicle_core::hash::{SpongeHash, HashConfig}; -use icicle_core::poseidon::Poseidon; -use icicle_core::traits::FieldImpl; -use icicle_cuda_runtime::memory::HostSlice; - -#[cfg(feature = "profile")] -use std::time::Instant; - -use clap::Parser; - -#[derive(Parser, Debug)] -struct Args { - /// Size of Poseidon input to run (20 for 2^20) - #[arg(short, long, default_value_t = 20)] - size: u8, -} - -fn main() { - let args = Args::parse(); - let size = args.size; - let test_size = 1 << size; - - println!("Running Icicle Examples: Rust Poseidon Hash"); - let arity = 2; - println!( - "---------------------- Loading optimized Poseidon constants for arity={} ------------------------", - arity - ); - let ctx = DeviceContext::default(); - let poseidon = Poseidon::load(arity, &ctx).unwrap(); - let config = HashConfig::default(); - - println!( - "---------------------- Input size 2^{}={} ------------------------", - size, test_size - ); - let mut inputs = vec![F::one(); test_size * arity as usize]; - let mut outputs = vec![F::zero(); test_size]; - let input_slice = HostSlice::from_mut_slice(&mut inputs); - let output_slice = HostSlice::from_mut_slice(&mut outputs); - - println!("Executing BLS12-381 Poseidon Hash on device..."); - #[cfg(feature = "profile")] - let start = Instant::now(); - poseidon.hash_many( - input_slice, - output_slice, - test_size, - arity, - 1, - &config, - ) - .unwrap(); - #[cfg(feature = "profile")] - println!( - "ICICLE BLS12-381 Poseidon Hash on size 2^{size} took: {} μs", - start - .elapsed() - .as_micros() - ); -} diff --git a/go.mod b/go.mod index 4aa8d85fe..bf11c0b30 100644 --- a/go.mod +++ b/go.mod @@ -1,4 +1,4 @@ -module github.com/ingonyama-zk/icicle/v2 +module github.com/ingonyama-zk/icicle/v3 go 1.20 diff --git a/icicle/CMakeLists.txt b/icicle/CMakeLists.txt index 8d240d4ac..4773ad7d3 100644 --- a/icicle/CMakeLists.txt +++ b/icicle/CMakeLists.txt @@ -1,62 +1,122 @@ cmake_minimum_required(VERSION 3.18) -project(icicle LANGUAGES CUDA CXX) +project(icicle) -include(cmake/Common.cmake) -include(cmake/FieldsCommon.cmake) -include(cmake/CurvesCommon.cmake) +# Specify the C++ standard +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED True) -set_env() -set_gpu_env() +include(cmake/field.cmake) +include(cmake/curve.cmake) -set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib) +# Set the default build type to Release if not specified +if(NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Choose the type of build: Debug, Release, RelWithDebInfo, MinSizeRel." FORCE) +endif() + +# Print the selected build type +message(STATUS "Build type: ${CMAKE_BUILD_TYPE}") + +# Find the ccache program +find_program(CCACHE_PROGRAM ccache) +# If ccache is found, use it as the compiler launcher +if(CCACHE_PROGRAM) + message(STATUS "ccache found: ${CCACHE_PROGRAM}") + + # Use ccache for C and C++ compilers + set(CMAKE_C_COMPILER_LAUNCHER ${CCACHE_PROGRAM}) + set(CMAKE_CXX_COMPILER_LAUNCHER ${CCACHE_PROGRAM}) + set(CMAKE_CUDA_COMPILER_LAUNCHER ${CCACHE_PROGRAM}) +else() + message(STATUS "ccache not found") +endif() -option(DEVMODE "Enable development mode" OFF) -option(EXT_FIELD "Build extension field" OFF) -option(G2 "Build G2" OFF) +set(CMAKE_POSITION_INDEPENDENT_CODE ON) + +# Build options +option(BUILD_TESTS "Build unit tests. Default=OFF" OFF) +option(CPU_BACKEND "Build CPU backend. Default=ON" ON) +option(CUDA_BACKEND "Branch/commit to pull for CUDA backend or `local` if under icicle/backend/cuda. Default=OFF" OFF) + +# features that some fields/curves have and some don't. +option(NTT "Build NTT" ON) +option(ECNTT "Build ECNTT" ON) option(MSM "Build MSM" ON) -option(ECNTT "Build ECNTT" OFF) -option(BUILD_HASH "Build hash functions" OFF) -option(BUILD_TESTS "Build unit tests" OFF) -option(BUILD_BENCHMARKS "Build benchmarks" OFF) -# add options here +option(G2 "Build G2 MSM" ON) +option(EXT_FIELD "Build extension field" ON) -if((DEFINED CURVE) AND (DEFINED FIELD)) - message( FATAL_ERROR "CURVE and FIELD cannot be defined at the same time" ) -endif () +# device API library +add_library(icicle_device SHARED + src/device_api.cpp + src/runtime.cpp + src/config_extension.cpp +) +target_link_libraries(icicle_device PUBLIC dl) +include_directories(include) + +# Define the install directory (default is /usr/local) +if(NOT DEFINED CMAKE_INSTALL_PREFIX) + set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Install path prefix") +endif() +message(STATUS "CMAKE_INSTALL_PREFIX=${CMAKE_INSTALL_PREFIX}") +set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/lib) +# set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE) + +# Specify the installation rules +install(TARGETS icicle_device + RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/lib + LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/lib + ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib) -if (DEVMODE) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O0 --ptxas-options=-O0 --ptxas-options=-allow-expensive-optimizations=false -DDEVMODE=ON") -endif () +if((DEFINED CURVE) AND (DEFINED FIELD)) + if(NOT ("${CURVE}" STREQUAL "${FIELD}")) + message(FATAL_ERROR "CURVE and FIELD should be defined at the same time unless they are equal") + endif() +endif() -if(DEFINED FIELD) - check_field() - add_subdirectory(src/fields) -endif () +# curve is building the scalar field too +if(CURVE) + set(CURVE_INDEX -1) + set(FEATURES_STRING "") + check_curve(${CURVE} CURVE_INDEX FEATURES_STRING) + setup_curve_target(${CURVE} ${CURVE_INDEX} ${FEATURES_STRING}) +elseif(FIELD) + set(FIELD_INDEX -1) + set(FEATURES_STRING "") + check_field(${FIELD} FIELD_INDEX FEATURES_STRING) + setup_field_target(${FIELD} ${FIELD_INDEX} ${FEATURES_STRING}) +endif() -if(DEFINED CURVE) - check_curve() - set(FIELD ${CURVE}) - add_subdirectory(src/fields) - add_subdirectory(src/curves) -endif () +if (CPU_BACKEND) + add_subdirectory(backend/cpu) +endif() -if (G2) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DG2") -endif () +if (CUDA_BACKEND) + string(TOLOWER "${CUDA_BACKEND}" CUDA_BACKEND_LOWER) + if (CUDA_BACKEND_LOWER STREQUAL "local") + # CUDA backend is local, no need to pull + message(STATUS "Adding CUDA backend from local path: icicle/backend/cuda") + add_subdirectory(backend/cuda) -if (EXT_FIELD) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DEXT_FIELD") -endif () + # Set the compile definition for the backend build directory + add_compile_definitions(BACKEND_BUILD_DIR="${CMAKE_BINARY_DIR}/backend") + else() + set(CUDA_BACKEND_URL "git@github.com:ingonyama-zk/icicle-cuda-backend.git") -if(BUILD_HASH) - add_subdirectory(src/hash) -endif () + include(FetchContent) + message(STATUS "Fetching cuda backend from ${CUDA_BACKEND_URL}:${CUDA_BACKEND}") + FetchContent_Declare( + cuda_backend + GIT_REPOSITORY ${CUDA_BACKEND_URL} + GIT_TAG ${CUDA_BACKEND} + ) + FetchContent_MakeAvailable(cuda_backend) + # Set the compile definition for the backend build directory + add_compile_definitions(BACKEND_BUILD_DIR="${CMAKE_BINARY_DIR}/_deps/cuda_backend-build") + endif() +endif() if (BUILD_TESTS) add_subdirectory(tests) endif() -if (BUILD_BENCHMARKS) - add_subdirectory(benchmarks) -endif() diff --git a/icicle/Doxyfile b/icicle/Doxyfile deleted file mode 100644 index 1d7ba732e..000000000 --- a/icicle/Doxyfile +++ /dev/null @@ -1,2585 +0,0 @@ -# Doxyfile 1.8.17 - -# This file describes the settings to be used by the documentation system -# doxygen (www.doxygen.org) for a project. -# -# All text after a double hash (##) is considered a comment and is placed in -# front of the TAG it is preceding. -# -# All text after a single hash (#) is considered a comment and will be ignored. -# The format is: -# TAG = value [value, ...] -# For lists, items can also be appended using: -# TAG += value [value, ...] -# Values that contain spaces should be placed between quotes (\" \"). - -#--------------------------------------------------------------------------- -# Project related configuration options -#--------------------------------------------------------------------------- - -# This tag specifies the encoding used for all characters in the configuration -# file that follow. 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If set to NO, the -# Javadoc-style will behave just like regular comments and it will not be -# interpreted by doxygen. -# The default value is: NO. - -JAVADOC_BANNER = NO - -# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first -# line (until the first dot) of a Qt-style comment as the brief description. If -# set to NO, the Qt-style will behave just like regular Qt-style comments (thus -# requiring an explicit \brief command for a brief description.) -# The default value is: NO. - -QT_AUTOBRIEF = NO - -# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a -# multi-line C++ special comment block (i.e. a block of //! or /// comments) as -# a brief description. This used to be the default behavior. The new default is -# to treat a multi-line C++ comment block as a detailed description. 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Doxygen -# uses this value to replace tabs by spaces in code fragments. -# Minimum value: 1, maximum value: 16, default value: 4. - -TAB_SIZE = 4 - -# This tag can be used to specify a number of aliases that act as commands in -# the documentation. An alias has the form: -# name=value -# For example adding -# "sideeffect=@par Side Effects:\n" -# will allow you to put the command \sideeffect (or @sideeffect) in the -# documentation, which will result in a user-defined paragraph with heading -# "Side Effects:". You can put \n's in the value part of an alias to insert -# newlines (in the resulting output). You can put ^^ in the value part of an -# alias to insert a newline as if a physical newline was in the original file. -# When you need a literal { or } or , in the value part of an alias you have to -# escape them by means of a backslash (\), this can lead to conflicts with the -# commands \{ and \} for these it is advised to use the version @{ and @} or use -# a double escape (\\{ and \\}) - -ALIASES = - -# This tag can be used to specify a number of word-keyword mappings (TCL only). -# A mapping has the form "name=value". For example adding "class=itcl::class" -# will allow you to use the command class in the itcl::class meaning. - -TCL_SUBST = - -# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources -# only. Doxygen will then generate output that is more tailored for C. For -# instance, some of the names that are used will be different. The list of all -# members will be omitted, etc. -# The default value is: NO. - -OPTIMIZE_OUTPUT_FOR_C = NO - -# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or -# Python sources only. Doxygen will then generate output that is more tailored -# for that language. For instance, namespaces will be presented as packages, -# qualified scopes will look different, etc. -# The default value is: NO. - -OPTIMIZE_OUTPUT_JAVA = NO - -# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran -# sources. Doxygen will then generate output that is tailored for Fortran. -# The default value is: NO. - -OPTIMIZE_FOR_FORTRAN = NO - -# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL -# sources. Doxygen will then generate output that is tailored for VHDL. -# The default value is: NO. - -OPTIMIZE_OUTPUT_VHDL = NO - -# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice -# sources only. Doxygen will then generate output that is more tailored for that -# language. For instance, namespaces will be presented as modules, types will be -# separated into more groups, etc. -# The default value is: NO. - -OPTIMIZE_OUTPUT_SLICE = NO - -# Doxygen selects the parser to use depending on the extension of the files it -# parses. With this tag you can assign which parser to use for a given -# extension. Doxygen has a built-in mapping, but you can override or extend it -# using this tag. The format is ext=language, where ext is a file extension, and -# language is one of the parsers supported by doxygen: IDL, Java, JavaScript, -# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice, -# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran: -# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser -# tries to guess whether the code is fixed or free formatted code, this is the -# default for Fortran type files), VHDL, tcl. For instance to make doxygen treat -# .inc files as Fortran files (default is PHP), and .f files as C (default is -# Fortran), use: inc=Fortran f=C. -# -# Note: For files without extension you can use no_extension as a placeholder. -# -# Note that for custom extensions you also need to set FILE_PATTERNS otherwise -# the files are not read by doxygen. - -EXTENSION_MAPPING = cu=c++ \ - cuh=c++ - -# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments -# according to the Markdown format, which allows for more readable -# documentation. See https://daringfireball.net/projects/markdown/ for details. -# The output of markdown processing is further processed by doxygen, so you can -# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in -# case of backward compatibilities issues. -# The default value is: YES. - -MARKDOWN_SUPPORT = YES - -# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up -# to that level are automatically included in the table of contents, even if -# they do not have an id attribute. -# Note: This feature currently applies only to Markdown headings. -# Minimum value: 0, maximum value: 99, default value: 5. -# This tag requires that the tag MARKDOWN_SUPPORT is set to YES. - -TOC_INCLUDE_HEADINGS = 5 - -# When enabled doxygen tries to link words that correspond to documented -# classes, or namespaces to their corresponding documentation. Such a link can -# be prevented in individual cases by putting a % sign in front of the word or -# globally by setting AUTOLINK_SUPPORT to NO. -# The default value is: YES. - -AUTOLINK_SUPPORT = YES - -# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want -# to include (a tag file for) the STL sources as input, then you should set this -# tag to YES in order to let doxygen match functions declarations and -# definitions whose arguments contain STL classes (e.g. func(std::string); -# versus func(std::string) {}). This also make the inheritance and collaboration -# diagrams that involve STL classes more complete and accurate. -# The default value is: NO. - -BUILTIN_STL_SUPPORT = NO - -# If you use Microsoft's C++/CLI language, you should set this option to YES to -# enable parsing support. -# The default value is: NO. - -CPP_CLI_SUPPORT = NO - -# Set the SIP_SUPPORT tag to YES if your project consists of sip (see: -# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen -# will parse them like normal C++ but will assume all classes use public instead -# of private inheritance when no explicit protection keyword is present. -# The default value is: NO. - -SIP_SUPPORT = NO - -# For Microsoft's IDL there are propget and propput attributes to indicate -# getter and setter methods for a property. Setting this option to YES will make -# doxygen to replace the get and set methods by a property in the documentation. -# This will only work if the methods are indeed getting or setting a simple -# type. If this is not the case, or you want to show the methods anyway, you -# should set this option to NO. -# The default value is: YES. - -IDL_PROPERTY_SUPPORT = YES - -# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC -# tag is set to YES then doxygen will reuse the documentation of the first -# member in the group (if any) for the other members of the group. By default -# all members of a group must be documented explicitly. -# The default value is: NO. - -DISTRIBUTE_GROUP_DOC = NO - -# If one adds a struct or class to a group and this option is enabled, then also -# any nested class or struct is added to the same group. By default this option -# is disabled and one has to add nested compounds explicitly via \ingroup. -# The default value is: NO. - -GROUP_NESTED_COMPOUNDS = NO - -# Set the SUBGROUPING tag to YES to allow class member groups of the same type -# (for instance a group of public functions) to be put as a subgroup of that -# type (e.g. under the Public Functions section). Set it to NO to prevent -# subgrouping. Alternatively, this can be done per class using the -# \nosubgrouping command. -# The default value is: YES. - -SUBGROUPING = YES - -# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions -# are shown inside the group in which they are included (e.g. using \ingroup) -# instead of on a separate page (for HTML and Man pages) or section (for LaTeX -# and RTF). -# -# Note that this feature does not work in combination with -# SEPARATE_MEMBER_PAGES. -# The default value is: NO. - -INLINE_GROUPED_CLASSES = NO - -# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions -# with only public data fields or simple typedef fields will be shown inline in -# the documentation of the scope in which they are defined (i.e. file, -# namespace, or group documentation), provided this scope is documented. If set -# to NO, structs, classes, and unions are shown on a separate page (for HTML and -# Man pages) or section (for LaTeX and RTF). -# The default value is: NO. - -INLINE_SIMPLE_STRUCTS = NO - -# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or -# enum is documented as struct, union, or enum with the name of the typedef. So -# typedef struct TypeS {} TypeT, will appear in the documentation as a struct -# with name TypeT. When disabled the typedef will appear as a member of a file, -# namespace, or class. And the struct will be named TypeS. This can typically be -# useful for C code in case the coding convention dictates that all compound -# types are typedef'ed and only the typedef is referenced, never the tag name. -# The default value is: NO. - -TYPEDEF_HIDES_STRUCT = NO - -# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This -# cache is used to resolve symbols given their name and scope. Since this can be -# an expensive process and often the same symbol appears multiple times in the -# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small -# doxygen will become slower. If the cache is too large, memory is wasted. The -# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range -# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536 -# symbols. At the end of a run doxygen will report the cache usage and suggest -# the optimal cache size from a speed point of view. -# Minimum value: 0, maximum value: 9, default value: 0. - -LOOKUP_CACHE_SIZE = 0 - -#--------------------------------------------------------------------------- -# Build related configuration options -#--------------------------------------------------------------------------- - -# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in -# documentation are documented, even if no documentation was available. Private -# class members and static file members will be hidden unless the -# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES. -# Note: This will also disable the warnings about undocumented members that are -# normally produced when WARNINGS is set to YES. -# The default value is: NO. - -EXTRACT_ALL = NO - -# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will -# be included in the documentation. -# The default value is: NO. - -EXTRACT_PRIVATE = NO - -# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual -# methods of a class will be included in the documentation. -# The default value is: NO. - -EXTRACT_PRIV_VIRTUAL = NO - -# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal -# scope will be included in the documentation. -# The default value is: NO. - -EXTRACT_PACKAGE = NO - -# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be -# included in the documentation. -# The default value is: NO. - -EXTRACT_STATIC = NO - -# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined -# locally in source files will be included in the documentation. If set to NO, -# only classes defined in header files are included. Does not have any effect -# for Java sources. -# The default value is: YES. - -EXTRACT_LOCAL_CLASSES = YES - -# This flag is only useful for Objective-C code. If set to YES, local methods, -# which are defined in the implementation section but not in the interface are -# included in the documentation. If set to NO, only methods in the interface are -# included. -# The default value is: NO. - -EXTRACT_LOCAL_METHODS = NO - -# If this flag is set to YES, the members of anonymous namespaces will be -# extracted and appear in the documentation as a namespace called -# 'anonymous_namespace{file}', where file will be replaced with the base name of -# the file that contains the anonymous namespace. By default anonymous namespace -# are hidden. -# The default value is: NO. - -EXTRACT_ANON_NSPACES = NO - -# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all -# undocumented members inside documented classes or files. If set to NO these -# members will be included in the various overviews, but no documentation -# section is generated. This option has no effect if EXTRACT_ALL is enabled. -# The default value is: NO. - -HIDE_UNDOC_MEMBERS = NO - -# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all -# undocumented classes that are normally visible in the class hierarchy. If set -# to NO, these classes will be included in the various overviews. This option -# has no effect if EXTRACT_ALL is enabled. -# The default value is: NO. - -HIDE_UNDOC_CLASSES = NO - -# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend -# declarations. If set to NO, these declarations will be included in the -# documentation. -# The default value is: NO. - -HIDE_FRIEND_COMPOUNDS = NO - -# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any -# documentation blocks found inside the body of a function. If set to NO, these -# blocks will be appended to the function's detailed documentation block. -# The default value is: NO. - -HIDE_IN_BODY_DOCS = NO - -# The INTERNAL_DOCS tag determines if documentation that is typed after a -# \internal command is included. If the tag is set to NO then the documentation -# will be excluded. Set it to YES to include the internal documentation. -# The default value is: NO. - -INTERNAL_DOCS = NO - -# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file -# names in lower-case letters. If set to YES, upper-case letters are also -# allowed. This is useful if you have classes or files whose names only differ -# in case and if your file system supports case sensitive file names. Windows -# (including Cygwin) ands Mac users are advised to set this option to NO. -# The default value is: system dependent. - -CASE_SENSE_NAMES = YES - -# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with -# their full class and namespace scopes in the documentation. If set to YES, the -# scope will be hidden. -# The default value is: NO. - -HIDE_SCOPE_NAMES = NO - -# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will -# append additional text to a page's title, such as Class Reference. If set to -# YES the compound reference will be hidden. -# The default value is: NO. - -HIDE_COMPOUND_REFERENCE= NO - -# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of -# the files that are included by a file in the documentation of that file. -# The default value is: YES. - -SHOW_INCLUDE_FILES = YES - -# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each -# grouped member an include statement to the documentation, telling the reader -# which file to include in order to use the member. -# The default value is: NO. - -SHOW_GROUPED_MEMB_INC = NO - -# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include -# files with double quotes in the documentation rather than with sharp brackets. -# The default value is: NO. - -FORCE_LOCAL_INCLUDES = NO - -# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the -# documentation for inline members. -# The default value is: YES. - -INLINE_INFO = YES - -# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the -# (detailed) documentation of file and class members alphabetically by member -# name. If set to NO, the members will appear in declaration order. -# The default value is: YES. - -SORT_MEMBER_DOCS = YES - -# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief -# descriptions of file, namespace and class members alphabetically by member -# name. If set to NO, the members will appear in declaration order. Note that -# this will also influence the order of the classes in the class list. -# The default value is: NO. - -SORT_BRIEF_DOCS = NO - -# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the -# (brief and detailed) documentation of class members so that constructors and -# destructors are listed first. If set to NO the constructors will appear in the -# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS. -# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief -# member documentation. -# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting -# detailed member documentation. -# The default value is: NO. - -SORT_MEMBERS_CTORS_1ST = NO - -# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy -# of group names into alphabetical order. If set to NO the group names will -# appear in their defined order. -# The default value is: NO. - -SORT_GROUP_NAMES = NO - -# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by -# fully-qualified names, including namespaces. If set to NO, the class list will -# be sorted only by class name, not including the namespace part. -# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. -# Note: This option applies only to the class list, not to the alphabetical -# list. -# The default value is: NO. - -SORT_BY_SCOPE_NAME = NO - -# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper -# type resolution of all parameters of a function it will reject a match between -# the prototype and the implementation of a member function even if there is -# only one candidate or it is obvious which candidate to choose by doing a -# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still -# accept a match between prototype and implementation in such cases. -# The default value is: NO. - -STRICT_PROTO_MATCHING = NO - -# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo -# list. This list is created by putting \todo commands in the documentation. -# The default value is: YES. - -GENERATE_TODOLIST = YES - -# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test -# list. This list is created by putting \test commands in the documentation. -# The default value is: YES. - -GENERATE_TESTLIST = YES - -# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug -# list. This list is created by putting \bug commands in the documentation. -# The default value is: YES. - -GENERATE_BUGLIST = YES - -# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO) -# the deprecated list. This list is created by putting \deprecated commands in -# the documentation. -# The default value is: YES. - -GENERATE_DEPRECATEDLIST= YES - -# The ENABLED_SECTIONS tag can be used to enable conditional documentation -# sections, marked by \if ... \endif and \cond -# ... \endcond blocks. - -ENABLED_SECTIONS = - -# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the -# initial value of a variable or macro / define can have for it to appear in the -# documentation. If the initializer consists of more lines than specified here -# it will be hidden. Use a value of 0 to hide initializers completely. The -# appearance of the value of individual variables and macros / defines can be -# controlled using \showinitializer or \hideinitializer command in the -# documentation regardless of this setting. -# Minimum value: 0, maximum value: 10000, default value: 30. - -MAX_INITIALIZER_LINES = 30 - -# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at -# the bottom of the documentation of classes and structs. If set to YES, the -# list will mention the files that were used to generate the documentation. -# The default value is: YES. - -SHOW_USED_FILES = YES - -# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This -# will remove the Files entry from the Quick Index and from the Folder Tree View -# (if specified). -# The default value is: YES. - -SHOW_FILES = YES - -# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces -# page. This will remove the Namespaces entry from the Quick Index and from the -# Folder Tree View (if specified). -# The default value is: YES. - -SHOW_NAMESPACES = YES - -# The FILE_VERSION_FILTER tag can be used to specify a program or script that -# doxygen should invoke to get the current version for each file (typically from -# the version control system). Doxygen will invoke the program by executing (via -# popen()) the command command input-file, where command is the value of the -# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided -# by doxygen. Whatever the program writes to standard output is used as the file -# version. For an example see the documentation. - -FILE_VERSION_FILTER = - -# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed -# by doxygen. The layout file controls the global structure of the generated -# output files in an output format independent way. To create the layout file -# that represents doxygen's defaults, run doxygen with the -l option. You can -# optionally specify a file name after the option, if omitted DoxygenLayout.xml -# will be used as the name of the layout file. -# -# Note that if you run doxygen from a directory containing a file called -# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE -# tag is left empty. - -LAYOUT_FILE = - -# The CITE_BIB_FILES tag can be used to specify one or more bib files containing -# the reference definitions. This must be a list of .bib files. The .bib -# extension is automatically appended if omitted. This requires the bibtex tool -# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info. -# For LaTeX the style of the bibliography can be controlled using -# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the -# search path. See also \cite for info how to create references. - -CITE_BIB_FILES = - -#--------------------------------------------------------------------------- -# Configuration options related to warning and progress messages -#--------------------------------------------------------------------------- - -# The QUIET tag can be used to turn on/off the messages that are generated to -# standard output by doxygen. If QUIET is set to YES this implies that the -# messages are off. -# The default value is: NO. - -QUIET = NO - -# The WARNINGS tag can be used to turn on/off the warning messages that are -# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES -# this implies that the warnings are on. -# -# Tip: Turn warnings on while writing the documentation. -# The default value is: YES. - -WARNINGS = YES - -# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate -# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag -# will automatically be disabled. -# The default value is: YES. - -WARN_IF_UNDOCUMENTED = YES - -# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for -# potential errors in the documentation, such as not documenting some parameters -# in a documented function, or documenting parameters that don't exist or using -# markup commands wrongly. -# The default value is: YES. - -WARN_IF_DOC_ERROR = YES - -# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that -# are documented, but have no documentation for their parameters or return -# value. If set to NO, doxygen will only warn about wrong or incomplete -# parameter documentation, but not about the absence of documentation. If -# EXTRACT_ALL is set to YES then this flag will automatically be disabled. -# The default value is: NO. - -WARN_NO_PARAMDOC = NO - -# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when -# a warning is encountered. -# The default value is: NO. - -WARN_AS_ERROR = NO - -# The WARN_FORMAT tag determines the format of the warning messages that doxygen -# can produce. The string should contain the $file, $line, and $text tags, which -# will be replaced by the file and line number from which the warning originated -# and the warning text. Optionally the format may contain $version, which will -# be replaced by the version of the file (if it could be obtained via -# FILE_VERSION_FILTER) -# The default value is: $file:$line: $text. - -WARN_FORMAT = "$file:$line: $text" - -# The WARN_LOGFILE tag can be used to specify a file to which warning and error -# messages should be written. If left blank the output is written to standard -# error (stderr). - -WARN_LOGFILE = - -#--------------------------------------------------------------------------- -# Configuration options related to the input files -#--------------------------------------------------------------------------- - -# The INPUT tag is used to specify the files and/or directories that contain -# documented source files. You may enter file names like myfile.cpp or -# directories like /usr/src/myproject. Separate the files or directories with -# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING -# Note: If this tag is empty the current directory is searched. - -INPUT = appUtils/msm \ - appUtils/ntt \ - appUtils/lde \ - utils/device_context.cuh - -# This tag can be used to specify the character encoding of the source files -# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses -# libiconv (or the iconv built into libc) for the transcoding. See the libiconv -# documentation (see: https://www.gnu.org/software/libiconv/) for the list of -# possible encodings. -# The default value is: UTF-8. - -INPUT_ENCODING = UTF-8 - -# If the value of the INPUT tag contains directories, you can use the -# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and -# *.h) to filter out the source-files in the directories. -# -# Note that for custom extensions or not directly supported extensions you also -# need to set EXTENSION_MAPPING for the extension otherwise the files are not -# read by doxygen. -# -# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, -# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, -# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, -# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment), -# *.doc (to be provided as doxygen C comment), *.txt (to be provided as doxygen -# C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f, *.for, *.tcl, *.vhd, -# *.vhdl, *.ucf, *.qsf and *.ice. - -FILE_PATTERNS = *.c \ - *.cc \ - *.cxx \ - *.cu \ - *.cuh \ - *.cpp \ - *.c++ \ - *.java \ - *.ii \ - *.ixx \ - *.ipp \ - *.i++ \ - *.inl \ - *.idl \ - *.ddl \ - *.odl \ - *.h \ - *.hh \ - *.hxx \ - *.hpp \ - *.h++ \ - *.cs \ - *.d \ - *.php \ - *.php4 \ - *.php5 \ - *.phtml \ - *.inc \ - *.m \ - *.markdown \ - *.md \ - *.mm \ - *.dox \ - *.doc \ - *.txt \ - *.py \ - *.pyw \ - *.f90 \ - *.f95 \ - *.f03 \ - *.f08 \ - *.f \ - *.for \ - *.tcl \ - *.vhd \ - *.vhdl \ - *.ucf \ - *.qsf \ - *.ice - -# The RECURSIVE tag can be used to specify whether or not subdirectories should -# be searched for input files as well. -# The default value is: NO. - -RECURSIVE = NO - -# The EXCLUDE tag can be used to specify files and/or directories that should be -# excluded from the INPUT source files. This way you can easily exclude a -# subdirectory from a directory tree whose root is specified with the INPUT tag. -# -# Note that relative paths are relative to the directory from which doxygen is -# run. - -EXCLUDE = - -# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or -# directories that are symbolic links (a Unix file system feature) are excluded -# from the input. -# The default value is: NO. - -EXCLUDE_SYMLINKS = NO - -# If the value of the INPUT tag contains directories, you can use the -# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude -# certain files from those directories. -# -# Note that the wildcards are matched against the file with absolute path, so to -# exclude all test directories for example use the pattern */test/* - -EXCLUDE_PATTERNS = - -# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names -# (namespaces, classes, functions, etc.) that should be excluded from the -# output. The symbol name can be a fully qualified name, a word, or if the -# wildcard * is used, a substring. Examples: ANamespace, AClass, -# AClass::ANamespace, ANamespace::*Test -# -# Note that the wildcards are matched against the file with absolute path, so to -# exclude all test directories use the pattern */test/* - -EXCLUDE_SYMBOLS = - -# The EXAMPLE_PATH tag can be used to specify one or more files or directories -# that contain example code fragments that are included (see the \include -# command). - -EXAMPLE_PATH = - -# If the value of the EXAMPLE_PATH tag contains directories, you can use the -# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and -# *.h) to filter out the source-files in the directories. If left blank all -# files are included. - -EXAMPLE_PATTERNS = * - -# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be -# searched for input files to be used with the \include or \dontinclude commands -# irrespective of the value of the RECURSIVE tag. -# The default value is: NO. - -EXAMPLE_RECURSIVE = NO - -# The IMAGE_PATH tag can be used to specify one or more files or directories -# that contain images that are to be included in the documentation (see the -# \image command). - -IMAGE_PATH = - -# The INPUT_FILTER tag can be used to specify a program that doxygen should -# invoke to filter for each input file. Doxygen will invoke the filter program -# by executing (via popen()) the command: -# -# -# -# where is the value of the INPUT_FILTER tag, and is the -# name of an input file. Doxygen will then use the output that the filter -# program writes to standard output. If FILTER_PATTERNS is specified, this tag -# will be ignored. -# -# Note that the filter must not add or remove lines; it is applied before the -# code is scanned, but not when the output code is generated. If lines are added -# or removed, the anchors will not be placed correctly. -# -# Note that for custom extensions or not directly supported extensions you also -# need to set EXTENSION_MAPPING for the extension otherwise the files are not -# properly processed by doxygen. - -INPUT_FILTER = - -# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern -# basis. Doxygen will compare the file name with each pattern and apply the -# filter if there is a match. The filters are a list of the form: pattern=filter -# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how -# filters are used. If the FILTER_PATTERNS tag is empty or if none of the -# patterns match the file name, INPUT_FILTER is applied. -# -# Note that for custom extensions or not directly supported extensions you also -# need to set EXTENSION_MAPPING for the extension otherwise the files are not -# properly processed by doxygen. - -FILTER_PATTERNS = - -# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using -# INPUT_FILTER) will also be used to filter the input files that are used for -# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES). -# The default value is: NO. - -FILTER_SOURCE_FILES = NO - -# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file -# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and -# it is also possible to disable source filtering for a specific pattern using -# *.ext= (so without naming a filter). -# This tag requires that the tag FILTER_SOURCE_FILES is set to YES. - -FILTER_SOURCE_PATTERNS = - -# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that -# is part of the input, its contents will be placed on the main page -# (index.html). This can be useful if you have a project on for instance GitHub -# and want to reuse the introduction page also for the doxygen output. - -USE_MDFILE_AS_MAINPAGE = - -#--------------------------------------------------------------------------- -# Configuration options related to source browsing -#--------------------------------------------------------------------------- - -# If the SOURCE_BROWSER tag is set to YES then a list of source files will be -# generated. Documented entities will be cross-referenced with these sources. -# -# Note: To get rid of all source code in the generated output, make sure that -# also VERBATIM_HEADERS is set to NO. -# The default value is: NO. - -SOURCE_BROWSER = NO - -# Setting the INLINE_SOURCES tag to YES will include the body of functions, -# classes and enums directly into the documentation. -# The default value is: NO. - -INLINE_SOURCES = NO - -# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any -# special comment blocks from generated source code fragments. Normal C, C++ and -# Fortran comments will always remain visible. -# The default value is: YES. - -STRIP_CODE_COMMENTS = YES - -# If the REFERENCED_BY_RELATION tag is set to YES then for each documented -# entity all documented functions referencing it will be listed. -# The default value is: NO. - -REFERENCED_BY_RELATION = NO - -# If the REFERENCES_RELATION tag is set to YES then for each documented function -# all documented entities called/used by that function will be listed. -# The default value is: NO. - -REFERENCES_RELATION = NO - -# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set -# to YES then the hyperlinks from functions in REFERENCES_RELATION and -# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will -# link to the documentation. -# The default value is: YES. - -REFERENCES_LINK_SOURCE = YES - -# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the -# source code will show a tooltip with additional information such as prototype, -# brief description and links to the definition and documentation. Since this -# will make the HTML file larger and loading of large files a bit slower, you -# can opt to disable this feature. -# The default value is: YES. -# This tag requires that the tag SOURCE_BROWSER is set to YES. - -SOURCE_TOOLTIPS = YES - -# If the USE_HTAGS tag is set to YES then the references to source code will -# point to the HTML generated by the htags(1) tool instead of doxygen built-in -# source browser. The htags tool is part of GNU's global source tagging system -# (see https://www.gnu.org/software/global/global.html). You will need version -# 4.8.6 or higher. -# -# To use it do the following: -# - Install the latest version of global -# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file -# - Make sure the INPUT points to the root of the source tree -# - Run doxygen as normal -# -# Doxygen will invoke htags (and that will in turn invoke gtags), so these -# tools must be available from the command line (i.e. in the search path). -# -# The result: instead of the source browser generated by doxygen, the links to -# source code will now point to the output of htags. -# The default value is: NO. -# This tag requires that the tag SOURCE_BROWSER is set to YES. - -USE_HTAGS = NO - -# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a -# verbatim copy of the header file for each class for which an include is -# specified. Set to NO to disable this. -# See also: Section \class. -# The default value is: YES. - -VERBATIM_HEADERS = YES - -# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the -# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the -# cost of reduced performance. This can be particularly helpful with template -# rich C++ code for which doxygen's built-in parser lacks the necessary type -# information. -# Note: The availability of this option depends on whether or not doxygen was -# generated with the -Duse_libclang=ON option for CMake. -# The default value is: NO. - -CLANG_ASSISTED_PARSING = NO - -# If clang assisted parsing is enabled you can provide the compiler with command -# line options that you would normally use when invoking the compiler. Note that -# the include paths will already be set by doxygen for the files and directories -# specified with INPUT and INCLUDE_PATH. -# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES. - -CLANG_OPTIONS = - -# If clang assisted parsing is enabled you can provide the clang parser with the -# path to the compilation database (see: -# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files -# were built. This is equivalent to specifying the "-p" option to a clang tool, -# such as clang-check. These options will then be passed to the parser. -# Note: The availability of this option depends on whether or not doxygen was -# generated with the -Duse_libclang=ON option for CMake. - -CLANG_DATABASE_PATH = - -#--------------------------------------------------------------------------- -# Configuration options related to the alphabetical class index -#--------------------------------------------------------------------------- - -# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all -# compounds will be generated. Enable this if the project contains a lot of -# classes, structs, unions or interfaces. -# The default value is: YES. - -ALPHABETICAL_INDEX = YES - -# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in -# which the alphabetical index list will be split. -# Minimum value: 1, maximum value: 20, default value: 5. -# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. - -COLS_IN_ALPHA_INDEX = 5 - -# In case all classes in a project start with a common prefix, all classes will -# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag -# can be used to specify a prefix (or a list of prefixes) that should be ignored -# while generating the index headers. -# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. - -IGNORE_PREFIX = - -#--------------------------------------------------------------------------- -# Configuration options related to the HTML output -#--------------------------------------------------------------------------- - -# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output -# The default value is: YES. - -GENERATE_HTML = YES - -# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a -# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of -# it. -# The default directory is: html. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_OUTPUT = html - -# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each -# generated HTML page (for example: .htm, .php, .asp). -# The default value is: .html. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_FILE_EXTENSION = .html - -# The HTML_HEADER tag can be used to specify a user-defined HTML header file for -# each generated HTML page. If the tag is left blank doxygen will generate a -# standard header. -# -# To get valid HTML the header file that includes any scripts and style sheets -# that doxygen needs, which is dependent on the configuration options used (e.g. -# the setting GENERATE_TREEVIEW). It is highly recommended to start with a -# default header using -# doxygen -w html new_header.html new_footer.html new_stylesheet.css -# YourConfigFile -# and then modify the file new_header.html. See also section "Doxygen usage" -# for information on how to generate the default header that doxygen normally -# uses. -# Note: The header is subject to change so you typically have to regenerate the -# default header when upgrading to a newer version of doxygen. For a description -# of the possible markers and block names see the documentation. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_HEADER = - -# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each -# generated HTML page. If the tag is left blank doxygen will generate a standard -# footer. See HTML_HEADER for more information on how to generate a default -# footer and what special commands can be used inside the footer. See also -# section "Doxygen usage" for information on how to generate the default footer -# that doxygen normally uses. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_FOOTER = - -# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style -# sheet that is used by each HTML page. It can be used to fine-tune the look of -# the HTML output. If left blank doxygen will generate a default style sheet. -# See also section "Doxygen usage" for information on how to generate the style -# sheet that doxygen normally uses. -# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as -# it is more robust and this tag (HTML_STYLESHEET) will in the future become -# obsolete. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_STYLESHEET = - -# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined -# cascading style sheets that are included after the standard style sheets -# created by doxygen. Using this option one can overrule certain style aspects. -# This is preferred over using HTML_STYLESHEET since it does not replace the -# standard style sheet and is therefore more robust against future updates. -# Doxygen will copy the style sheet files to the output directory. -# Note: The order of the extra style sheet files is of importance (e.g. the last -# style sheet in the list overrules the setting of the previous ones in the -# list). For an example see the documentation. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_EXTRA_STYLESHEET = - -# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or -# other source files which should be copied to the HTML output directory. Note -# that these files will be copied to the base HTML output directory. Use the -# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these -# files. In the HTML_STYLESHEET file, use the file name only. Also note that the -# files will be copied as-is; there are no commands or markers available. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_EXTRA_FILES = - -# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen -# will adjust the colors in the style sheet and background images according to -# this color. Hue is specified as an angle on a colorwheel, see -# https://en.wikipedia.org/wiki/Hue for more information. For instance the value -# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 -# purple, and 360 is red again. -# Minimum value: 0, maximum value: 359, default value: 220. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_HUE = 220 - -# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors -# in the HTML output. For a value of 0 the output will use grayscales only. A -# value of 255 will produce the most vivid colors. -# Minimum value: 0, maximum value: 255, default value: 100. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_SAT = 100 - -# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the -# luminance component of the colors in the HTML output. Values below 100 -# gradually make the output lighter, whereas values above 100 make the output -# darker. The value divided by 100 is the actual gamma applied, so 80 represents -# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not -# change the gamma. -# Minimum value: 40, maximum value: 240, default value: 80. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_GAMMA = 80 - -# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML -# page will contain the date and time when the page was generated. Setting this -# to YES can help to show when doxygen was last run and thus if the -# documentation is up to date. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_TIMESTAMP = NO - -# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML -# documentation will contain a main index with vertical navigation menus that -# are dynamically created via JavaScript. If disabled, the navigation index will -# consists of multiple levels of tabs that are statically embedded in every HTML -# page. Disable this option to support browsers that do not have JavaScript, -# like the Qt help browser. -# The default value is: YES. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_DYNAMIC_MENUS = YES - -# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML -# documentation will contain sections that can be hidden and shown after the -# page has loaded. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_DYNAMIC_SECTIONS = NO - -# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries -# shown in the various tree structured indices initially; the user can expand -# and collapse entries dynamically later on. Doxygen will expand the tree to -# such a level that at most the specified number of entries are visible (unless -# a fully collapsed tree already exceeds this amount). So setting the number of -# entries 1 will produce a full collapsed tree by default. 0 is a special value -# representing an infinite number of entries and will result in a full expanded -# tree by default. -# Minimum value: 0, maximum value: 9999, default value: 100. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_INDEX_NUM_ENTRIES = 100 - -# If the GENERATE_DOCSET tag is set to YES, additional index files will be -# generated that can be used as input for Apple's Xcode 3 integrated development -# environment (see: https://developer.apple.com/xcode/), introduced with OSX -# 10.5 (Leopard). To create a documentation set, doxygen will generate a -# Makefile in the HTML output directory. Running make will produce the docset in -# that directory and running make install will install the docset in -# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at -# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy -# genXcode/_index.html for more information. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_DOCSET = NO - -# This tag determines the name of the docset feed. A documentation feed provides -# an umbrella under which multiple documentation sets from a single provider -# (such as a company or product suite) can be grouped. -# The default value is: Doxygen generated docs. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_FEEDNAME = "Doxygen generated docs" - -# This tag specifies a string that should uniquely identify the documentation -# set bundle. This should be a reverse domain-name style string, e.g. -# com.mycompany.MyDocSet. Doxygen will append .docset to the name. -# The default value is: org.doxygen.Project. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_BUNDLE_ID = org.doxygen.Project - -# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify -# the documentation publisher. This should be a reverse domain-name style -# string, e.g. com.mycompany.MyDocSet.documentation. -# The default value is: org.doxygen.Publisher. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_PUBLISHER_ID = org.doxygen.Publisher - -# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher. -# The default value is: Publisher. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_PUBLISHER_NAME = Publisher - -# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three -# additional HTML index files: index.hhp, index.hhc, and index.hhk. The -# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop -# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on -# Windows. -# -# The HTML Help Workshop contains a compiler that can convert all HTML output -# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML -# files are now used as the Windows 98 help format, and will replace the old -# Windows help format (.hlp) on all Windows platforms in the future. Compressed -# HTML files also contain an index, a table of contents, and you can search for -# words in the documentation. The HTML workshop also contains a viewer for -# compressed HTML files. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_HTMLHELP = NO - -# The CHM_FILE tag can be used to specify the file name of the resulting .chm -# file. You can add a path in front of the file if the result should not be -# written to the html output directory. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -CHM_FILE = - -# The HHC_LOCATION tag can be used to specify the location (absolute path -# including file name) of the HTML help compiler (hhc.exe). If non-empty, -# doxygen will try to run the HTML help compiler on the generated index.hhp. -# The file has to be specified with full path. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -HHC_LOCATION = - -# The GENERATE_CHI flag controls if a separate .chi index file is generated -# (YES) or that it should be included in the master .chm file (NO). -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -GENERATE_CHI = NO - -# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc) -# and project file content. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -CHM_INDEX_ENCODING = - -# The BINARY_TOC flag controls whether a binary table of contents is generated -# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it -# enables the Previous and Next buttons. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -BINARY_TOC = NO - -# The TOC_EXPAND flag can be set to YES to add extra items for group members to -# the table of contents of the HTML help documentation and to the tree view. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -TOC_EXPAND = NO - -# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and -# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that -# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help -# (.qch) of the generated HTML documentation. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_QHP = NO - -# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify -# the file name of the resulting .qch file. The path specified is relative to -# the HTML output folder. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QCH_FILE = - -# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help -# Project output. For more information please see Qt Help Project / Namespace -# (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace). -# The default value is: org.doxygen.Project. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_NAMESPACE = org.doxygen.Project - -# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt -# Help Project output. For more information please see Qt Help Project / Virtual -# Folders (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual- -# folders). -# The default value is: doc. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_VIRTUAL_FOLDER = doc - -# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom -# filter to add. For more information please see Qt Help Project / Custom -# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom- -# filters). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_CUST_FILTER_NAME = - -# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the -# custom filter to add. For more information please see Qt Help Project / Custom -# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom- -# filters). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_CUST_FILTER_ATTRS = - -# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this -# project's filter section matches. Qt Help Project / Filter Attributes (see: -# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_SECT_FILTER_ATTRS = - -# The QHG_LOCATION tag can be used to specify the location of Qt's -# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the -# generated .qhp file. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHG_LOCATION = - -# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be -# generated, together with the HTML files, they form an Eclipse help plugin. To -# install this plugin and make it available under the help contents menu in -# Eclipse, the contents of the directory containing the HTML and XML files needs -# to be copied into the plugins directory of eclipse. The name of the directory -# within the plugins directory should be the same as the ECLIPSE_DOC_ID value. -# After copying Eclipse needs to be restarted before the help appears. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_ECLIPSEHELP = NO - -# A unique identifier for the Eclipse help plugin. 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m_buckets; // Vector of all buckets required for phase 1 (All bms in order) + std::vector m_bkts_occupancy; // Boolean vector indicating if the corresponding bucket is occupied + + // Phase 2 members + const int m_log_num_segments; + const int m_num_bm_segments; // Number of segments in a BM - to maximize parallel operation from the serial bm sums. + const int m_segment_size; + + /** + * @struct BmSumSegment + * @brief Struct bundling the required data members for each BM segment in phase 2. + */ + struct BmSumSegment { + // Imagining the required BM sum as a right angle triangle - Nth element is a N high column of the element - + // A method to summing the BM serially is starting from the top with a triangle sum up to the current line and a + // line sum which is calculated for each line then added to the triangle. + // Therefore both sums are need as a part of this struct. + P triangle_sum; + P line_sum; + + int m_nof_received_sums = 1; // Counter for numbers of sum received in this row idx - used to determine when a new + // row idx can be dispatched (Due to it depending on the two sums from the prev idx). + int m_idx_in_segment; // Counter counting down how far the current sum is through the segment. + int m_segment_mem_start; // Offset of segment start in memory / vector. + }; + + // Phase 3 members + std::vector m_phase3_threads; + + /** + * @brief Phase 1 (accumulation) of MSM - sorting input points to buckets according to corresponding input scalars. + * @param scalars - scalar input. + * @param bases - EC P input, affine representation (Regardless of the defined P type of the class). + * @param msm_size - Size of the above arrays, as they are given as Pers. + */ + void phase1_bucket_accumulator(const scalar_t* scalars, const A* bases, const unsigned int msm_size); + + /** + * @brief Push addition task during phase 1. + * Th function also handles completed addition results while attempting to insert the new addition task (including + * potential collision that requires more urgent ec-addition of the result and the current stored value). + * @param task_bkt_idx - address in m_buckets in which to store the result in the future. + * @param bkt - the P from m_buckets. + * @param base - the P from the input bases + * @param negate_base - flag to signal the task to subtract base instead of adding it. + */ + void phase1_push_addition(const unsigned int task_bkt_idx, const P bkt, const A base); + /** + * @brief Handles the final results of phase 1 (after no other planned additions are required). + * The function also handles the potential collision similarly to push_addition above. + */ + void phase1_wait_for_completion(); + + /** + * @brief Phase 2 of MSM. Function handling the initial parallel part of summing the bucket modules. It splits the + * BMs into segments, summing each separately and passing the segments sum to be handled by the final accumulator. + * @param segments vector to contain segments line and triangle sums for the final accumulator (output of function). + */ + void phase2_bm_sum(std::vector& segments); + + /** + * @brief Setting up phase 2 class members according to phase 1 results. + * NOTE: This setup function also potentially blocks until the previous MSM's phase 3 finishes (Given that in batched + * MSM, phase 3 is initiated on a separate thread while the next MSM begins phase 1). + */ + void phase2_setup(std::vector& segments); + + /** + * @brief Final accumulation required for MSM calculation. the function will either launch a thread to perform the + * calculation (`phase3_tread` function) or by the main thread, depending on the position in the batch (Last msm or + * not). + * @param segments_ptr - pointer to vector containing the segment calculations of phase 2 + * @param idx_in_batch - idx of the current MSM in the batch. + * @param result - output, Per to write the MSM result to. Memory for the Per has already been allocated by + * the user. + */ + void phase3_final_accumulator(std::vector& segments, int idx_in_batch, P* result); + + /** + * @brief Phase 3 function to be ran by a separate thread (or main in the end of the run) - i.e. without using the + * tasks manager. It performs the remaining serial addition in each BM and sums them to one final MSM result. + * @param result - Per to write the MSM result to. Memory for the Per has already been allocated by the user. + */ + void phase3_thread(std::vector segments, P* result); + + /** + * @brief Function for resetting class members between batch runs. + */ + void batch_run_reset() { std::fill(m_bkts_occupancy.begin(), m_bkts_occupancy.end(), false); } +}; + +template +Msm::Msm(const MSMConfig& config, const int c, const int nof_threads) + : manager(nof_threads), + + m_c(c), m_num_bkts(1 << (m_c - 1)), m_precompute_factor(config.precompute_factor), + m_num_bms(((scalar_t::NBITS) / (config.precompute_factor * m_c)) + 1), + m_are_scalars_mont(config.are_scalars_montgomery_form), m_are_points_mont(config.are_points_montgomery_form), + m_batch_size(config.batch_size), + + m_buckets(m_num_bms * m_num_bkts), m_bkts_occupancy(m_num_bms * m_num_bkts, false), + + m_log_num_segments(std::max( + (int)std::floor(std::log2((double)(nof_threads * TASKS_PER_THREAD - 1) / (double)(2 * m_num_bms))), 0)), + m_num_bm_segments(std::min((int)(1 << m_log_num_segments), (int)(m_num_bms * m_num_bkts))), + m_segment_size(std::max((int)(m_num_bkts >> m_log_num_segments), 1)), + + m_phase3_threads(m_batch_size - 1) +{ +} + +template +void Msm::run_msm( + const scalar_t* scalars, const A* bases, const unsigned int msm_size, const unsigned int batch_idx, P* results) +{ + phase1_bucket_accumulator(scalars, bases, msm_size); + auto segments = std::vector(m_num_bms * m_num_bm_segments); + phase2_bm_sum(segments); + phase3_final_accumulator(segments, batch_idx, results); + if (batch_idx < m_batch_size - 1) { batch_run_reset(); } +} + +template +void Msm::phase1_bucket_accumulator(const scalar_t* scalars, const A* bases, const unsigned int msm_size) +{ + const int coeff_bit_mask_no_sign_bit = m_num_bkts - 1; + const int coeff_bit_mask_with_sign_bit = (1 << m_c) - 1; + // NUmber of windows / additions per scalar in case num_bms * precompute_factor exceed scalar width + const int num_bms_before_precompute = ((scalar_t::NBITS - 1) / m_c) + 1; // +1 for ceiling + + int carry = 0; + for (int i = 0; i < msm_size; i++) { + carry = 0; + // Handle required preprocess of scalar + scalar_t scalar = m_are_scalars_mont ? scalar_t::from_montgomery(scalars[i]) : scalars[i]; + bool negate_p_and_s = scalar.get_scalar_digit(scalar_t::NBITS - 1, 1) > 0; + if (negate_p_and_s) { scalar = scalar_t::neg(scalar); } + for (int j = 0; j < m_precompute_factor; j++) { + // Handle required preprocess of base P + A base = + m_are_points_mont ? A::from_montgomery(bases[m_precompute_factor * i + j]) : bases[m_precompute_factor * i + j]; + if (base == A::zero()) { continue; } + if (negate_p_and_s) { base = A::neg(base); } + + for (int k = 0; k < m_num_bms; k++) { + // Avoid seg fault in case precompute_factor*c exceeds the scalar width by comparing index with num additions + if (m_num_bms * j + k >= num_bms_before_precompute) { break; } + + uint32_t curr_coeff = scalar.get_scalar_digit(m_num_bms * j + k, m_c) + carry; + int bkt_idx = 0; + if ((curr_coeff & coeff_bit_mask_with_sign_bit) != 0) { + // Remove sign to infer the bkt idx. + carry = curr_coeff >= m_num_bkts; + if (curr_coeff < m_num_bkts) { + bkt_idx = m_num_bkts * k + curr_coeff; + } else { + bkt_idx = m_num_bkts * k + ((-curr_coeff) & coeff_bit_mask_no_sign_bit); + } + + // Check for collision in that bucket and either dispatch an addition or store the P accordingly. + if (m_bkts_occupancy[bkt_idx]) { + m_bkts_occupancy[bkt_idx] = false; + phase1_push_addition(bkt_idx, m_buckets[bkt_idx], carry > 0 ? A::neg(base) : base); + } else { + m_bkts_occupancy[bkt_idx] = true; + m_buckets[bkt_idx] = carry > 0 ? P::neg(P::from_affine(base)) : P::from_affine(base); + } + } else { + // Handle edge case where coeff = 1 << c due to carry overflow which means: + // coeff & coeff_mask == 0 but there is a carry to propagate to the next segment + carry = curr_coeff >> m_c; + } + } + } + } + phase1_wait_for_completion(); +} + +template +void Msm::phase1_push_addition(const unsigned int task_bkt_idx, const P bkt, const A base) +{ + EcAddTask* task = nullptr; + while (task == nullptr) { + // Use the search for an available (idle or completed) task as an opportunity to handle the existing results. + task = manager.get_idle_or_completed_task(); + if (task->is_completed()) { + // Check for collision in the destination bucket, and chain and addition / store result accordingly. + if (m_bkts_occupancy[task->m_return_idx]) { + m_bkts_occupancy[task->m_return_idx] = false; + task->set_phase1_collision_task(m_buckets[task->m_return_idx]); + task = nullptr; + } else { + m_buckets[task->m_return_idx] = task->m_point1; + m_bkts_occupancy[task->m_return_idx] = true; + } + } + } + // After handling the result a new one can be set. + task->set_phase1_addition_with_affine(bkt, base, task_bkt_idx); +} + +template +void Msm::phase1_wait_for_completion() +{ + EcAddTask* task = manager.get_completed_task(); + while (task != nullptr) { + // Check for collision in the destination bucket, and chain and addition / store result accordingly. + if (m_bkts_occupancy[task->m_return_idx]) { + m_bkts_occupancy[task->m_return_idx] = false; + task->set_phase1_collision_task(m_buckets[task->m_return_idx]); + } else { + m_buckets[task->m_return_idx] = task->m_point1; + m_bkts_occupancy[task->m_return_idx] = true; + task->set_idle(); + } + task = manager.get_completed_task(); + } +} + +template +void Msm::phase2_bm_sum(std::vector& segments) +{ + phase2_setup(segments); + if (m_segment_size > 1) { + // Send first additions - line additions. + for (int i = 0; i < m_num_bms * m_num_bm_segments; i++) { + EcAddTask* task = manager.get_idle_task(); + BmSumSegment& curr_segment = segments[i]; // For readability + + int bkt_idx = curr_segment.m_segment_mem_start + curr_segment.m_idx_in_segment; + P bucket = m_bkts_occupancy[bkt_idx] ? m_buckets[bkt_idx] : P::zero(); + task->set_phase2_addition_by_value(curr_segment.line_sum, bucket, i); + } + + // Loop until all line/tri sums are done. + int done_segments = 0; + while (done_segments < m_num_bms * m_num_bm_segments) { + EcAddTask* task = manager.get_completed_task(); + BmSumSegment& curr_segment = segments[task->m_return_idx]; // For readability + + if (task->m_is_line) { + curr_segment.line_sum = task->m_point1; + } else { + curr_segment.triangle_sum = task->m_point1; + } + curr_segment.m_nof_received_sums++; + + // Check if this was the last addition in the segment + if (curr_segment.m_idx_in_segment < 0) { + done_segments++; + task->set_idle(); + continue; + } + // Otherwise check if it is possible to assign new additions: + // Triangle sum is dependent on the 2 previous sums (line and triangle) - so check if 2 sums were received. + if (curr_segment.m_nof_received_sums == 2) { + curr_segment.m_nof_received_sums = 0; + task->set_phase2_triangle_addition(curr_segment.line_sum, &(curr_segment.triangle_sum)); + curr_segment.m_idx_in_segment--; + + // Line sum (if not the last one in the segment) + if (curr_segment.m_idx_in_segment >= 0) { + int bkt_idx = curr_segment.m_segment_mem_start + curr_segment.m_idx_in_segment; + if (m_bkts_occupancy[bkt_idx]) { + int return_idx = task->m_return_idx; + // Due to the choice of num segments being less than half of total tasks there ought to be an idle task for + // the line sum + task = manager.get_idle_task(); + task->set_phase2_line_addition(curr_segment.line_sum, &m_buckets[bkt_idx], return_idx); + } else { + curr_segment.m_nof_received_sums++; + } // No need to add a zero - just increase nof_received_sums + } + } else { + task->set_idle(); + } // Handling Completed task without dispatching a new one + } + } +} + +template +void Msm::phase2_setup(std::vector& segments) +{ + // Init values of partial (line) and total (triangle) sums + for (int i = 0; i < m_num_bms; i++) { + for (int j = 0; j < m_num_bm_segments - 1; j++) { + BmSumSegment& segment = segments[m_num_bm_segments * i + j]; + int bkt_idx = m_num_bkts * i + m_segment_size * (j + 1); + if (m_bkts_occupancy[bkt_idx]) { + segment.triangle_sum = m_buckets[bkt_idx]; + segment.line_sum = m_buckets[bkt_idx]; + } else { + segment.triangle_sum = P::zero(); + segment.line_sum = P::zero(); + } + segment.m_idx_in_segment = m_segment_size - 2; + segment.m_segment_mem_start = m_num_bkts * i + m_segment_size * j + 1; + } + + // The most significant bucket of every bm is stored in address 0 - + // so the last tri/line sums will be initialized to bucket[0] + BmSumSegment& segment = segments[m_num_bm_segments * (i + 1) - 1]; + int bkt_idx = m_num_bkts * i; + if (m_bkts_occupancy[bkt_idx]) { + segment.triangle_sum = m_buckets[bkt_idx]; + segment.line_sum = m_buckets[bkt_idx]; + } else { + segment.triangle_sum = P::zero(); + segment.line_sum = P::zero(); + } + segment.m_idx_in_segment = m_segment_size - 2; + segment.m_segment_mem_start = m_num_bkts * (i + 1) - m_segment_size + 1; + } +} + +template +void Msm::phase3_final_accumulator(std::vector& segments, int idx_in_batch, P* result) +{ + // If it isn't the last MSM in the batch - run phase 3 on a separate thread to start utilizing the tasks manager on + // the next phase 1. + if (idx_in_batch == m_batch_size - 1) { + phase3_thread(segments, result); + } else { + m_phase3_threads[idx_in_batch] = std::thread(&Msm::phase3_thread, this, std::move(segments), result); + } +} + +template +void Msm::phase3_thread(std::vector segments, P* result) +{ + for (int i = 0; i < m_num_bms; i++) { + // Weighted sum of all the lines for each bm - summed in a similar fashion of the triangle sum of phase 2 + P partial_sum = segments[m_num_bm_segments * (i + 1) - 1].line_sum; + P total_sum = segments[m_num_bm_segments * (i + 1) - 1].line_sum; + for (int j = m_num_bm_segments - 2; j > 0; j--) { + partial_sum = partial_sum + segments[m_num_bm_segments * i + j].line_sum; + total_sum = total_sum + partial_sum; + } + segments[m_num_bm_segments * i].line_sum = total_sum; + + // Convert weighted lines sum to rectangles sum by doubling + int num_doubles = m_c - 1 - m_log_num_segments; + for (int k = 0; k < num_doubles; k++) { + segments[m_num_bm_segments * i].line_sum = P::dbl(segments[m_num_bm_segments * i].line_sum); + } + + // Sum triangles within bm linearly + for (int j = 1; j < m_num_bm_segments; j++) { + segments[m_num_bm_segments * i].triangle_sum = + segments[m_num_bm_segments * i].triangle_sum + segments[m_num_bm_segments * i + j].triangle_sum; + } + + // After which add the lines and triangle sums to one sum of the entire BM + if (m_num_bm_segments > 1) { + segments[m_num_bm_segments * i].triangle_sum = + segments[m_num_bm_segments * i].triangle_sum + segments[m_num_bm_segments * i].line_sum; + } + } + + // Sum BM sums together + P final_sum = segments[(m_num_bms - 1) * m_num_bm_segments].triangle_sum; + for (int i = m_num_bms - 2; i >= 0; i--) { + // Multiply by the BM digit factor 2^c - i.e. c doublings + for (int j = 0; j < m_c; j++) { + final_sum = P::dbl(final_sum); + } + final_sum = final_sum + segments[m_num_bm_segments * i].triangle_sum; + } + *result = final_sum; +} + +// None class functions below: + +/** + * @brief Super function that handles the Msm class to calculate a MSM. + * @param device - Icicle API parameter stating the device being ran on. In this case - CPU. + * @param scalars - Input scalars for MSM. + * @param bases - EC P input, affine representation. + * @param msm_size - Size of the above arrays, as they are given as Pers. + * @param config - configuration containing parameters for the MSM. + * @param results - Per to P array in which to store the results. NOTE: the user is expected to preallocate the + * results array. + */ +template +eIcicleError cpu_msm( + const Device& device, const scalar_t* scalars, const A* bases, int msm_size, const MSMConfig& config, P* results) +{ + int c = config.c; + if (c < 1) { c = std::max((int)std::log2(msm_size) - 1, 8); } + if (scalar_t::NBITS % c == 0) { + ICICLE_LOG_DEBUG << "Currerntly c (" << c << ") mustn't divide scalar width (" << scalar_t::NBITS + << ") without remainder.\n"; + } + while (scalar_t::NBITS % c == 0) { + c++; + } + + int nof_threads = std::thread::hardware_concurrency(); + if (config.ext && config.ext->has(CpuBackendConfig::CPU_NOF_THREADS)) { + nof_threads = config.ext->get(CpuBackendConfig::CPU_NOF_THREADS); + } + if (nof_threads <= 0) { + ICICLE_LOG_WARNING << "Unable to detect number of hardware supported threads - fixing it to 1\n"; + nof_threads = 1; + } + + auto msm = Msm{config, c, nof_threads}; + + for (int i = 0; i < config.batch_size; i++) { + int batch_start_idx = msm_size * i; + int bases_start_idx = config.are_points_shared_in_batch ? 0 : batch_start_idx; + msm.run_msm(&scalars[batch_start_idx], &bases[bases_start_idx], msm_size, i, &results[i]); + } + return eIcicleError::SUCCESS; +} + +/** + * @brief Function to precompute basess multiplications - trading memory for MSM performance. + * @param device - Icicle API parameter stating the device being ran on. In this case - CPU. + * @param input_bases - bases (EC points) to precompute. + * @param nof_bases - Size of the above array, as it is given as a Per. + * @param config - configuration containing parameters for the MSM. In this case, the config implicitly determines the + * multiplication factor(s) of the input bases. + * @param output_bases - Per to P array in which to store the results. NOTE: the user is expected to + * preallocate the results array. + */ +template +eIcicleError cpu_msm_precompute_bases( + const Device& device, + const A* input_bases, + int nof_bases, + const MSMConfig& config, + A* output_bases) // Pre assigned? +{ + int c = config.c; + if (c < 1) { c = std::max((int)std::log2(nof_bases) - 1, 8); } + if (scalar_t::NBITS % c == 0) { + ICICLE_LOG_ERROR << "Currerntly c (" << c << ") mustn't divide scalar width (" << scalar_t::NBITS + << ") without remainder.\n"; + } + while (scalar_t::NBITS % c == 0) { + c++; + } + int precompute_factor = config.precompute_factor; + bool is_mont = config.are_points_montgomery_form; + const unsigned int num_bms_no_precomp = (scalar_t::NBITS - 1) / c + 1; + const unsigned int shift = c * ((num_bms_no_precomp - 1) / precompute_factor + 1); + for (int i = 0; i < nof_bases; i++) { + output_bases[precompute_factor * i] = input_bases[i]; + P point = P::from_affine(is_mont ? A::from_montgomery(input_bases[i]) : input_bases[i]); + for (int j = 1; j < precompute_factor; j++) { + for (int k = 0; k < shift; k++) { + point = P::dbl(point); + } + output_bases[precompute_factor * i + j] = is_mont ? A::to_montgomery(P::to_affine(point)) : P::to_affine(point); + } + } + return eIcicleError::SUCCESS; +} \ No newline at end of file diff --git a/icicle/backend/cpu/src/curve/test/Makefile b/icicle/backend/cpu/src/curve/test/Makefile new file mode 100644 index 000000000..53590138e --- /dev/null +++ b/icicle/backend/cpu/src/curve/test/Makefile @@ -0,0 +1,32 @@ +curve=4 + +build_msm_test: + mkdir -p build + mkdir -p build/generated_data + g++ -O3 -o build/msm_test msm_test.cpp -I.. -I../../../../../include -I../../../include -I../../../../../include/icicle/utils/ -std=c++17 -DCURVE_ID=$(curve) + +run_memory_check: + mkdir -p build + mkdir -p build/generated_data + g++ -O3 -fsanitize=address -fno-omit-frame-pointer -g -o build/msm_test msm_test.cpp -I.. -I../../../../../include -I../../../include -I../../../../../include/icicle/utils/ -std=c++17 -DCURVE_ID=$(curve) + ./build/msm_test + +build_debug: + mkdir -p build + mkdir -p build/generated_data + g++ -g -o build/msm_test msm_test.cpp -I.. -I../../../../../include -I../../../include -I../../../../../include/icicle/utils/ -std=c++17 -DCURVE_ID=$(curve) + +diff_bkts: + diff buckets_multi.txt buckets_single.txt + +trace_idx: + grep "#$(idx):" trace_bucket_multi.txt > single_trace_multithread.txt + grep "#$(idx):" trace_bucket_single.txt > single_trace_singlethread.txt + +clean_logs: + rm -rf *.txt + rm -rf ./build/*.txt + +clean_data: + rm -rf ./build/generated_data/* + \ No newline at end of file diff --git a/icicle/backend/cpu/src/curve/test/msm_test.cpp b/icicle/backend/cpu/src/curve/test/msm_test.cpp new file mode 100644 index 000000000..b16fafc2a --- /dev/null +++ b/icicle/backend/cpu/src/curve/test/msm_test.cpp @@ -0,0 +1,481 @@ +#include "icicle/errors.h" +#include "icicle/device.h" +#include "icicle/config_extension.h" +#include "icicle/curves/curve_config.h" +#include +#include +#include "timer.hpp" + +// #define DUMMY_TYPES +#define DEBUG_PRINTS +#define P_MACRO 1000 + +class DummyScalar +{ +public: + static constexpr unsigned NBITS = 10; + + unsigned x; + unsigned p = P_MACRO; + // unsigned p = 1<<30; + + static DummyScalar zero() { return {0}; } + + static DummyScalar one() { return {1}; } + + static DummyScalar to_montgomery(const DummyScalar& s) { return {s.x}; } + + static DummyScalar from_montgomery(const DummyScalar& s) { return {s.x}; } + + friend std::ostream& operator<<(std::ostream& os, const DummyScalar& scalar) + { + os << scalar.x; + return os; + } + + unsigned get_scalar_digit(unsigned digit_num, unsigned digit_width) const + { + // if (digit_num * digit_width > 10) { std::cout << "Overflow output(" << digit_num << '*' << digit_width << "):\t" + // << ((x >> (digit_num * digit_width)) & ((1 << digit_width) - 1)) << "\t(" << this << ")\n";} else { std::cout << + // "Reg output(" << digit_num << "):\t" << ((x >> (digit_num * digit_width)) & ((1 << digit_width) - 1)) << '\n';} + return ((x % p) >> (digit_num * digit_width)) & ((1 << digit_width) - 1); + } + + friend DummyScalar operator+(DummyScalar p1, const DummyScalar& p2) { return {(p1.x + p2.x) % p1.p}; } + + friend DummyScalar operator-(DummyScalar p1, const DummyScalar& p2) { return p1 + neg(p2); } + + friend bool operator==(const DummyScalar& p1, const DummyScalar& p2) { return (p1.x == p2.x); } + + friend bool operator==(const DummyScalar& p1, const unsigned p2) { return (p1.x == p2); } + + static DummyScalar neg(const DummyScalar& scalar) { return {scalar.p - scalar.x}; } + static DummyScalar rand_host() { return {(unsigned)rand() % P_MACRO}; } + + static void rand_host_many(DummyScalar* out, int size) + { + for (int i = 0; i < size; i++) + // out[i] = (i % size < 100) ? rand_host(rand_generator) : out[i - 100]; + out[i] = rand_host(); + } +}; + +class DummyPoint +{ +public: + DummyScalar x; + + static DummyPoint zero() { return {0}; } + + static DummyPoint one() { return {1}; } + + static DummyPoint to_affine(const DummyPoint& point) { return {point.x}; } + + static DummyPoint from_affine(const DummyPoint& point) { return {point.x}; } + + static DummyPoint to_montgomery(const DummyPoint& point) { return {point.x}; } + + static DummyPoint from_montgomery(const DummyPoint& point) { return {point.x}; } + + static DummyPoint neg(const DummyPoint& point) { return {DummyScalar::neg(point.x)}; } + + static DummyPoint copy(const DummyPoint& point) { return {point.x}; } + + friend DummyPoint operator+(DummyPoint p1, const DummyPoint& p2) { return {p1.x + p2.x}; } + + friend DummyPoint operator-(DummyPoint p1, const DummyPoint& p2) { return {p1.x - p2.x}; } + + static DummyPoint dbl(const DummyPoint& point) { return {point.x + point.x}; } + + // friend DummyPoint operator-(DummyPoint p1, const DummyPoint& p2) { + // return p1 + neg(p2); + // } + + friend std::ostream& operator<<(std::ostream& os, const DummyPoint& point) + { + os << point.x; + return os; + } + + friend DummyPoint operator*(DummyScalar scalar, const DummyPoint& point) + { + DummyPoint res = zero(); +#ifdef CUDA_ARCH + UNROLL +#endif + for (int i = 0; i < DummyScalar::NBITS; i++) { + if (i > 0) { res = res + res; } + if (scalar.get_scalar_digit(DummyScalar::NBITS - i - 1, 1)) { res = res + point; } + } + return res; + } + + friend bool operator==(const DummyPoint& p1, const DummyPoint& p2) { return (p1.x == p2.x); } + + static bool is_zero(const DummyPoint& point) { return point.x == 0; } + + static DummyPoint rand_host() { return {(unsigned)rand() % P_MACRO}; } + + static void rand_host_many(DummyPoint* out, int size) + { + for (int i = 0; i < size; i++) + // out[i] = (i % size < 100) ? to_affine(rand_host()) : out[i - 100]; + out[i] = to_affine(rand_host()); + } +}; + +#ifdef DUMMY_TYPES // for testing +using A = DummyPoint; +using P = DummyPoint; +using scalar_t = DummyScalar; +#else +using A = curve_config::g2_affine_t; +using P = curve_config::g2_projective_t; +using scalar_t = curve_config::scalar_t; +#endif + +#include "cpu_msm.hpp" +using namespace icicle; + +template +std::vector msm_bucket_accumulator( + const scalar_t* scalars, + const A* bases, + const unsigned int c, + const unsigned int num_bms, + const unsigned int msm_size, + const unsigned int precompute_factor, + const bool is_s_mont, + const bool is_b_mont) +{ + /** + * Accumulate into the different bkts + * @param scalars - original scalars given from the msm result + * @param bases - point bases to add + * @param c - address width of bucket modules to split scalars above + * @param msm_size - number of scalars to add + * @param is_s_mont - flag indicating input scalars are in Montgomery form + * @param is_b_mont - flag indicating input bases are in Montgomery form + * @return bkts - points array containing all bkts + */ + auto t = Timer("P1:bucket-accumulator"); + uint32_t num_bkts = 1 << (c - 1); + std::vector bkts(num_bms * num_bkts, Point::zero()); + uint32_t coeff_bit_mask = num_bkts - 1; + const int num_windows_m1 = (scalar_t::NBITS - 1) / c; + int carry; + +#ifdef DEBUG_PRINTS + std::string trace_fname = "trace_bucket_single.txt"; + std::ofstream trace_f(trace_fname); + if (!trace_f.good()) { std::cout << "ERROR: can't open file:\t" << trace_fname << std::endl; } +#endif + for (int i = 0; i < msm_size; i++) { + carry = 0; + scalar_t scalar = is_s_mont ? scalar_t::from_montgomery(scalars[i]) : scalars[i]; + bool negate_p_and_s = scalar.get_scalar_digit(scalar_t::NBITS - 1, 1) > 0; + if (negate_p_and_s) scalar = scalar_t::neg(scalar); + for (int j = 0; j < precompute_factor; j++) { + A point = is_b_mont ? A::from_montgomery(bases[precompute_factor * i + j]) : bases[precompute_factor * i + j]; + if (negate_p_and_s) point = A::neg(point); + for (int k = 0; k < num_bms; k++) { + // In case precompute_factor*c exceeds the scalar width + if (num_bms * j + k > num_windows_m1) { break; } + + uint32_t curr_coeff = scalar.get_scalar_digit(num_bms * j + k, c) + carry; + if ((curr_coeff & ((1 << c) - 1)) != 0) { + if (curr_coeff < num_bkts) { +#ifdef DEBUG_PRINTS + int bkt_idx = num_bkts * k + curr_coeff; + if (Point::is_zero(bkts[bkt_idx])) { + trace_f << '#' << bkt_idx << ":\tWrite free cell:\t" << point.x << '\n'; + } else { + trace_f << '#' << bkt_idx << ":\tRead for addition:\t" << Point::to_affine(bkts[bkt_idx]).x + << "\t(With new point:\t" << point.x << " = " << Point::to_affine(bkts[bkt_idx] + point).x + << ")\n"; + trace_f << '#' << bkt_idx << ":\tWrite (res) free cell:\t" << Point::to_affine(bkts[bkt_idx] + point).x + << '\n'; + } +#endif + + bkts[num_bkts * k + curr_coeff] = Point::is_zero(bkts[num_bkts * k + curr_coeff]) + ? Point::from_affine(point) + : bkts[num_bkts * k + curr_coeff] + point; + carry = 0; + } else { +#ifdef DEBUG_PRINTS + int bkt_idx = num_bkts * k + ((-curr_coeff) & coeff_bit_mask); + if (Point::is_zero(bkts[bkt_idx])) { + trace_f << '#' << bkt_idx << ":\tWrite free cell:\t" << A::neg(point).x << '\n'; + } else { + trace_f << '#' << bkt_idx << ":\tRead for subtraction:\t" << Point::to_affine(bkts[bkt_idx]).x + << "\t(With new point:\t" << point.x << " = " << Point::to_affine(bkts[bkt_idx] - point).x + << ")\n"; + trace_f << '#' << bkt_idx << ":\tWrite (res) free cell:\t" << Point::to_affine(bkts[bkt_idx] - point).x + << '\n'; + } +#endif + + bkts[num_bkts * k + ((-curr_coeff) & coeff_bit_mask)] = + Point::is_zero(bkts[num_bkts * k + ((-curr_coeff) & coeff_bit_mask)]) + ? Point::neg(Point::from_affine(point)) + : bkts[num_bkts * k + ((-curr_coeff) & coeff_bit_mask)] - point; + carry = 1; + } + } else { + carry = curr_coeff >> c; // Edge case for coeff = 1 << c + } + } + } + } +#ifdef DEBUG_PRINTS + trace_f.close(); + std::string b_fname = "buckets_single.txt"; + std::ofstream bkts_f_single(b_fname); + if (!bkts_f_single.good()) { std::cout << "ERROR: can't open file:\t" << b_fname << std::endl; } + for (int i = 0; i < num_bms; i++) + for (int j = 0; j < num_bkts; j++) + bkts_f_single << '(' << i << ',' << j << "):\t" << Point::to_affine(bkts[num_bkts * i + j]).x << '\n'; + bkts_f_single.close(); +#endif + return bkts; +} + +// They will only be kept in a test file when phase 2 and 3 sums are implemented in msm class +/** + * @brief Single threaded implementation of BM sum (phase 2) of MSM. + * @param bkts - vector of buckets given from the previous phase 1 function. + * @param c - Pipenger's constant. Used to calculate the bucket size of a BM. + * @param num_bms - number of BMs. + * @return - vector of size , containing each of the bms sums. + */ +template +std::vector msm_bm_sum(std::vector& bkts, const unsigned int c, const unsigned int num_bms) +{ + auto t = Timer("P2:bucket-module-sum"); + uint32_t num_bkts = 1 << (c - 1); + + std::vector bm_sums(num_bms); + + // Calculate the weighted "triangle" sum by using two sums in series: + // A partial sum holding the current line sum, and a total sum holding the sums of the lines (The triangle). + for (int k = 0; k < num_bms; k++) { + bm_sums[k] = bkts[num_bkts * k]; // Start with bucket zero that holds the weight @num_bkts + Point partial_sum = bkts[num_bkts * k]; // And the triangle and the line start with the same value + + for (int i = num_bkts - 1; i > 0; i--) { + partial_sum = partial_sum + bkts[num_bkts * k + i]; + bm_sums[k] = bm_sums[k] + partial_sum; + } + } + + return bm_sums; +} + +/** + * @brief Single threaded implementation of the final accumulator (phase 3) of MSM. + * @param bm_sums - vector of Points which are the BM sums from the previous phase 2 function. + * @param c - Pipenger's constant. Used to calculate the bucket size of a BM. + * @param num_bms - number of BMs. + * @return - Point, MSM result. + */ +template +Point msm_final_sum(std::vector& bm_sums, const unsigned int c, const unsigned int num_bms) +{ + /** + * Sum the bucket module sums to get the final result. + * @param bm_sums - point array containing bucket module sums. + * @param c - bucket module width / shift between subsequent bkts. + * @param is_b_mont - flag indicating input bases are in Montgomery form. + * @return result - MSM calculation. + */ + auto t = Timer("P3:final-accumulator"); + Point result = bm_sums[num_bms - 1]; + for (int k = num_bms - 2; k >= 0; k--) { + // Check if the current value is not zero before doing the c doubling below. + if (Point::is_zero(result)) { + result = bm_sums[k]; + } else { + // Every bm sum is 2^c times larger than the subsequent less significant bm-sum. + // Double the current result c times before adding the next bm-sum. + for (int dbl = 0; dbl < c; dbl++) { + result = Point::dbl(result); + } + result = result + bm_sums[k]; + } + } + return result; +} + +// Pipenger +template +eIcicleError cpu_msm_single_thread( + const Device& device, const scalar_t* scalars, const A* bases, int msm_size, const MSMConfig& config, Point* results) +{ + auto t = Timer("total-msm-single-threaded"); + + const unsigned int c = config.ext->get("c"); + const unsigned int precompute_factor = config.precompute_factor; + const int num_bms = ((scalar_t::NBITS - 1) / (precompute_factor * c)) + 1; + std::cout << "\n\nnum_bms = " << num_bms << ", c=" << c << ", precomp=" << precompute_factor << "\n\n\n"; + + for (int i = 0; i < config.batch_size; i++) { + std::vector bkts = msm_bucket_accumulator( + &scalars[msm_size * i], bases, c, num_bms, msm_size, precompute_factor, config.are_scalars_montgomery_form, + config.are_points_montgomery_form); + std::vector bm_sums = msm_bm_sum(bkts, c, num_bms); + Point res = msm_final_sum(bm_sums, c, num_bms); + results[i] = res; + } + + return eIcicleError::SUCCESS; +} + +// Most naive implementation as backup +template +eIcicleError cpu_msm_ref( + const Device& device, const scalar_t* scalars, const A* bases, int msm_size, const MSMConfig& config, Point* results) +{ + const unsigned int precompute_factor = config.precompute_factor; + Point res = Point::zero(); + for (auto i = 0; i < msm_size; ++i) { + scalar_t scalar = config.are_scalars_montgomery_form ? scalar_t::from_montgomery(scalars[i]) : scalars[i]; + A point = config.are_points_montgomery_form ? A::from_montgomery(bases[precompute_factor * i]) + : bases[precompute_factor * i]; + res = res + scalar * Point::from_affine(point); + } + // results[0] = config.are_points_montgomery_form? Point::to_montgomery(res) : res; + results[0] = res; + return eIcicleError::SUCCESS; +} + +template +bool read_inputs(T* arr, const int arr_size, const std::string fname) +{ + std::ifstream in_file(fname); + bool status = in_file.is_open(); + if (status) { + for (int i = 0; i < arr_size; i++) { + in_file.read(reinterpret_cast(&arr[i]), sizeof(T)); + } + in_file.close(); + } + return status; +} + +template +void store_inputs(T* arr, const int arr_size, const std::string fname) +{ + std::ofstream out_file(fname); + if (!out_file.is_open()) { + std::cerr << "Failed to open " << fname << " for writing.\n"; + return; + } + for (int i = 0; i < arr_size; i++) { + out_file.write(reinterpret_cast(&arr[i]), sizeof(T)); + } + out_file.close(); +} + +void get_inputs(A* bases, scalar_t* scalars, const int n, const int batch_size) +{ + // Scalars + std::string scalar_file = "build/generated_data/scalars_N" + std::to_string(n * batch_size) + ".dat"; + if (!read_inputs(scalars, n * batch_size, scalar_file)) { + std::cout << "Generating scalars.\n"; + scalar_t::rand_host_many(scalars, n * batch_size); + store_inputs(scalars, n * batch_size, scalar_file); + } + // Bases + std::string base_file = "build/generated_data/bases_N" + std::to_string(n) + ".dat"; + if (!read_inputs(bases, n, base_file)) { + std::cout << "Generating bases.\n"; + P::rand_host_many(bases, n); + store_inputs(bases, n, base_file); + } +} + +int main() +{ + while (true) { + // MSM config + const int logn = 17; + const int N = 1 << logn; + const int log_p = 2; + const int batch_size = 3; + bool conv_mont = true; + + auto scalars = std::make_unique(N * batch_size); + auto bases = std::make_unique(N); + get_inputs(bases.get(), scalars.get(), N, batch_size); + + if (conv_mont) { + std::cout << "Convertiting inputs to Montgomery form\n"; + for (int i = 0; i < N; i++) + bases[i] = A::to_montgomery(bases[i]); + } + P* result_cpu = new P[batch_size]; + P* result_cpu_ref = new P[batch_size]; + std::fill_n(result_cpu, batch_size, P::zero()); + std::fill_n(result_cpu_ref, batch_size, P::zero()); + + auto run = [&](const char* dev_type, P* result, const char* msg, bool measure, int iters, auto msm_func) { + const int c = std::max(logn, 8) - 1; + std::cout << "c:\t" << c << '\n'; + const int pcf = 1 << log_p; + + int hw_threads = std::thread::hardware_concurrency(); + if (hw_threads <= 0) { std::cout << "Unable to detect number of hardware supported threads - fixing it to 1\n"; } + const int n_threads = (hw_threads > 1) ? hw_threads - 1 : 1; + // const int n_threads = 1; + std::cout << "Num threads: " << n_threads << '\n'; + // const int n_threads = 8; + + const int tasks_per_thread = 4; + + MSMConfig config = default_msm_config(); + ConfigExtension ext; + ext.set("c", c); + ext.set("n_threads", n_threads); + + config.c = c; + config.ext = &ext; + config.precompute_factor = pcf; + config.are_scalars_montgomery_form = false; + config.are_points_montgomery_form = conv_mont; + config.batch_size = batch_size; + + auto precomp_bases = std::make_unique(N * pcf); + std::string precomp_fname = + "build/generated_data/precomp_N" + std::to_string(N) + "_pcf" + std::to_string(pcf) + ".dat"; + if (!read_inputs(precomp_bases.get(), N * pcf, precomp_fname)) { + std::cout << "Precomputing bases."; + cpu_msm_precompute_bases("CPU", bases.get(), N, config, precomp_bases.get()); + std::cout << " Storing.\n"; + store_inputs(precomp_bases.get(), N * pcf, precomp_fname); + } + // START_TIMER(MSM_sync) + std::cout << "Starting msm (N=" << N << ", pcf=" << pcf << ")\n"; + for (int i = 0; i < iters; ++i) { + msm_func("CPU", scalars.get(), precomp_bases.get(), N, config, result); + } + // END_TIMER(MSM_sync, msg, measure); + }; + + // run("CPU", &result_cpu_dbl_n_add, "CPU msm", false /*=measure*/, 1 /*=iters*/); // warmup + run("CPU_REF", result_cpu_ref, "CPU_REF msm", true /*=measure*/, 1 /*=iters*/, cpu_msm_single_thread

); + run("CPU", result_cpu, "CPU msm", true /*=measure*/, 1 /*=iters*/, cpu_msm); + + for (int i = 0; i < batch_size; i++) { + std::cout << "Batch no. " << i << ":\n"; + std::cout << "CPU:\t\t" << P::to_affine(result_cpu[i]) << std::endl; + std::cout << "CPU REF:\t" << P::to_affine(result_cpu_ref[i]) << std::endl; + assert(result_cpu[i] == result_cpu_ref[i]); + } + + delete[] result_cpu; + delete[] result_cpu_ref; + } + + return 0; +} diff --git a/icicle/backend/cpu/src/field/cpu_ntt.cpp b/icicle/backend/cpu/src/field/cpu_ntt.cpp new file mode 100644 index 000000000..fb4656504 --- /dev/null +++ b/icicle/backend/cpu/src/field/cpu_ntt.cpp @@ -0,0 +1,50 @@ +#include "cpu_ntt.h" + +using namespace field_config; +using namespace icicle; + +eIcicleError +cpu_ntt_init_domain(const Device& device, const scalar_t& primitive_root, const NTTInitDomainConfig& config) +{ + auto err = ntt_cpu::CpuNttDomain::cpu_ntt_init_domain(device, primitive_root, config); + return err; +} + +template +eIcicleError cpu_ntt_release_domain(const Device& device, const S& dummy) +{ + auto err = ntt_cpu::CpuNttDomain::cpu_ntt_release_domain(device); + return err; +} + +template +eIcicleError cpu_get_root_of_unity_from_domain(const Device& device, uint64_t logn, S* rou) +{ + auto err = ntt_cpu::CpuNttDomain::get_root_of_unity_from_domain(device, logn, rou); + return err; +} + +template +eIcicleError +cpu_ntt(const Device& device, const E* input, uint64_t size, NTTDir dir, const NTTConfig& config, E* output) +{ + auto err = ntt_cpu::cpu_ntt(device, input, size, dir, config, output); + return err; +} + +// template +// eIcicleError +// cpu_ntt_ref(const Device& device, const E* input, uint64_t size, NTTDir dir, const NTTConfig& config, E* output) +// { +// auto err = ntt_cpu::cpu_ntt_ref(device, input, size, dir, config, output); +// return err; +// } + +REGISTER_NTT_INIT_DOMAIN_BACKEND("CPU", (cpu_ntt_init_domain)); +REGISTER_NTT_RELEASE_DOMAIN_BACKEND("CPU", cpu_ntt_release_domain); +REGISTER_NTT_GET_ROU_FROM_DOMAIN_BACKEND("CPU", cpu_get_root_of_unity_from_domain); +REGISTER_NTT_BACKEND("CPU", (cpu_ntt)); + +#ifdef EXT_FIELD +REGISTER_NTT_EXT_FIELD_BACKEND("CPU", (cpu_ntt)); +#endif // EXT_FIELD diff --git a/icicle/backend/cpu/src/field/cpu_vec_ops.cpp b/icicle/backend/cpu/src/field/cpu_vec_ops.cpp new file mode 100644 index 000000000..3a2156d60 --- /dev/null +++ b/icicle/backend/cpu/src/field/cpu_vec_ops.cpp @@ -0,0 +1,630 @@ + +#include "icicle/backend/vec_ops_backend.h" +#include "icicle/errors.h" +#include "icicle/runtime.h" +#include "icicle/utils/log.h" + +#include "icicle/fields/field_config.h" +#include "tasks_manager.h" + +using namespace field_config; +using namespace icicle; + +/* Enumeration for the selected operation to execute. + * The worker task is templated by this enum and based on that the functionality is selected. */ +enum VecOperation { + VECTOR_ADD, + VECTOR_SUB, + VECTOR_MUL, + VECTOR_DIV, + VECTOR_SUM, + VECTOR_PRODUCT, + SCALAR_ADD_VEC, + SCALAR_SUB_VEC, + SCALAR_MUL_VEC, + CONVERT_TO_MONTGOMERY, + CONVERT_FROM_MONTGOMERY, + BIT_REVERSE, + SLICE, + + NOF_OPERATIONS +}; + +/** + * @class VectorOpTask + * @brief Contains all the functionality that a single worker can execute for any vector operation. + * + * The enum VecOperation defines which functionality to execute. + * Based on the enum value, the functionality is selected and the worker execute that function for every task that + * dispatched by the manager. + */ +template +class VectorOpTask : public TaskBase +{ +public: + // Constructor + VectorOpTask() : TaskBase() {} + + // Set the operands to execute a task of 2 operands and 1 output and dispatch the task + void send_2ops_task(VecOperation operation, const int nof_operations, const T* op_a, const T* op_b, T* output) + { + m_operation = operation; + m_nof_operations = nof_operations; + m_op_a = op_a; + m_op_b = op_b; + m_output = output; + dispatch(); + } + + // Set the operands to execute a task of 1 operand and 1 output and dispatch the task + void send_1op_task(VecOperation operation, const int nof_operations, const T* op_a, T* output) + { + m_operation = operation; + m_nof_operations = nof_operations; + m_op_a = op_a; + m_output = output; + dispatch(); + } + // Set the operands to execute a task of 1 operand and dispatch the task + void send_intermidiate_res_task(VecOperation operation, const int nof_operations, const T* op_a) + { + m_operation = operation; + m_nof_operations = nof_operations; + m_op_a = op_a; + dispatch(); + } + + // Set the operands to bitrev operation dispatch the task + void send_bitrev_task( + VecOperation operation, int bit_size, uint64_t start_index, const int nof_operations, const T* op_a, T* output) + { + m_operation = operation; + m_nof_operations = nof_operations; + m_op_a = op_a; + m_output = output; + m_bit_size = bit_size, m_start_index = start_index; + dispatch(); + } + + // Set the operands to slice operation dispatch the task + void send_slice_task(VecOperation operation, uint64_t stride, const int nof_operations, const T* op_a, T* output) + { + m_operation = operation; + m_nof_operations = nof_operations; + m_op_a = op_a; + m_output = output; + m_stride = stride; + dispatch(); + } + + // Execute the selected function based on m_operation + virtual void execute() { (this->*functionPtrs[static_cast(m_operation)])(); } + +private: + // Single worker functionality to execute vector add (+) + void vector_add() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = m_op_a[i] + m_op_b[i]; + } + } + + // Single worker functionality to execute vector add (+) + void vector_sub() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = m_op_a[i] - m_op_b[i]; + } + } + // Single worker functionality to execute vector mul (*) + void vector_mul() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = m_op_a[i] * m_op_b[i]; + } + } + // Single worker functionality to execute vector div (/) + void vector_div() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = m_op_a[i] * T::inverse(m_op_b[i]); + } + } + // Single worker functionality to execute scalar + vector + void scalar_add_vec() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = *m_op_a + m_op_b[i]; + } + } + // Single worker functionality to execute scalar - vector + void scalar_sub_vec() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = *m_op_a + m_op_b[i]; + } + } + // Single worker functionality to execute scalar * vector + void scalar_mul_vec() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = *m_op_a * m_op_b[i]; + } + } + // Single worker functionality to execute sum(vector) + void vector_sum() + { + *m_output = m_op_a[0]; + for (uint64_t i = 1; i < m_nof_operations; ++i) { + *m_output = *m_output + m_op_a[i]; + } + } + // Single worker functionality to execute product(vector) + void vector_product() + { + *m_output = m_op_a[0]; + for (uint64_t i = 1; i < m_nof_operations; ++i) { + *m_output = *m_output * m_op_a[i]; + } + } + // Single worker functionality to execute conversion from barret to montgomery + void convert_to_montgomery() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = T::to_montgomery(m_op_a[i]); + } + } + + // Single worker functionality to execute conversion from montgomery to barret + void convert_from_montgomery() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = T::from_montgomery(m_op_a[i]); + } + } + // Single worker functionality to execute bit reverse reorder + void bit_reverse() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + uint64_t idx = m_start_index + i; // original index + uint64_t rev_idx = m_start_index + i; // reverse index + // Bit reverse the iundex for 64 bits + rev_idx = ((rev_idx >> 1) & 0x5555555555555555) | ((rev_idx & 0x5555555555555555) << 1); // bit rev single bits + rev_idx = ((rev_idx >> 2) & 0x3333333333333333) | ((rev_idx & 0x3333333333333333) << 2); // bit rev 2 bits chunk + rev_idx = ((rev_idx >> 4) & 0x0F0F0F0F0F0F0F0F) | ((rev_idx & 0x0F0F0F0F0F0F0F0F) << 4); // bit rev 4 bits chunk + rev_idx = ((rev_idx >> 8) & 0x00FF00FF00FF00FF) | ((rev_idx & 0x00FF00FF00FF00FF) << 8); // bit rev 8 bits chunk + rev_idx = + ((rev_idx >> 16) & 0x0000FFFF0000FFFF) | ((rev_idx & 0x0000FFFF0000FFFF) << 16); // bit rev 16 bits chunk + rev_idx = (rev_idx >> 32) | (rev_idx << 32); // bit rev 32 bits chunk + rev_idx = rev_idx >> (64 - m_bit_size); // Align rev_idx to the LSB + + if (m_output == m_op_a) { // inplace calculation + if (rev_idx < idx) { // only on of the threads need to work + std::swap(m_output[idx], m_output[rev_idx]); + } + } else { // out of place calculation + m_output[idx] = m_op_a[rev_idx]; // set index value + } + } + } + + // Single worker functionality to execute slice + void slice() + { + for (uint64_t i = 0; i < m_nof_operations; ++i) { + m_output[i] = m_op_a[i * m_stride]; + } + } + + // An array of available function pointers arranged according to the VecOperation enum + using FunctionPtr = void (VectorOpTask::*)(); + static constexpr std::array(NOF_OPERATIONS)> functionPtrs = { + &VectorOpTask::vector_add, // VECTOR_ADD, + &VectorOpTask::vector_sub, // VECTOR_SUB, + &VectorOpTask::vector_mul, // VECTOR_MUL, + &VectorOpTask::vector_div, // VECTOR_DIV, + &VectorOpTask::vector_sum, // VECTOR_SUM + &VectorOpTask::vector_product, // VECTOR_PRODUCT + &VectorOpTask::scalar_add_vec, // SCALAR_ADD_VEC, + &VectorOpTask::scalar_sub_vec, // SCALAR_SUB_VEC, + &VectorOpTask::scalar_mul_vec, // SCALAR_MUL_VEC, + &VectorOpTask::convert_to_montgomery, // CONVERT_TO_MONTGOMERY, + &VectorOpTask::convert_from_montgomery, // CONVERT_FROM_MONTGOMERY, + &VectorOpTask::bit_reverse, // BIT_REVERSE + &VectorOpTask::slice // SLICE + }; + + VecOperation m_operation; // the operation to execute + int m_nof_operations; // number of operations to execute for this task + const T* m_op_a; // pointer to operand A. Operand A is a vector. + const T* m_op_b; // pointer to operand B. Operand B is a vector or scalar + uint64_t m_start_index; // index used in bitreverse + int m_bit_size; // use in bitrev operation + uint64_t m_stride; // used in slice operation + T* m_output; // pointer to the output. Can be a vector or scalar pointer + T m_intermidiate_res; // pointer to the output. Can be a vector or scalar pointer +}; + +#define NOF_OPERATIONS_PER_TASK 512 +#define CONFIG_NOF_THREADS_KEY "n_threads" + +// extract the number of threads to run from config +int get_nof_workers(const VecOpsConfig& config) +{ + if (config.ext && config.ext->has(CONFIG_NOF_THREADS_KEY)) { return config.ext->get(CONFIG_NOF_THREADS_KEY); } + + int hw_threads = std::thread::hardware_concurrency(); + return ((hw_threads > 1) ? hw_threads - 1 : 1); // reduce 1 for the main +} + +// Execute a full task from the type vector = vector (op) vector +template +eIcicleError +cpu_2vectors_op(VecOperation op, const T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + TasksManager> task_manager(get_nof_workers(config)); + for (uint64_t i = 0; i < n; i += NOF_OPERATIONS_PER_TASK) { + VectorOpTask* task_p = task_manager.get_idle_or_completed_task(); + task_p->send_2ops_task(op, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, n - i), vec_a + i, vec_b + i, output + i); + } + task_manager.wait_done(); + return eIcicleError::SUCCESS; +} + +// Execute a full task from the type vector = scalar (op) vector +template +eIcicleError cpu_scalar_vector_op( + VecOperation op, const T* scalar_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + TasksManager> task_manager(get_nof_workers(config)); + for (uint64_t i = 0; i < n; i += NOF_OPERATIONS_PER_TASK) { + VectorOpTask* task_p = task_manager.get_idle_or_completed_task(); + task_p->send_2ops_task(op, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, n - i), scalar_a, vec_b + i, output + i); + } + task_manager.wait_done(); + return eIcicleError::SUCCESS; +} + +/////////////////////////////////////////////////////// +// Functions to register at the CPU backend +template +eIcicleError +cpu_vector_add(const Device& device, const T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_2vectors_op(VecOperation::VECTOR_ADD, vec_a, vec_b, n, config, output); +} + +REGISTER_VECTOR_ADD_BACKEND("CPU", cpu_vector_add); + +/*********************************** ACCUMULATE ***********************************/ +template +eIcicleError +cpu_vector_accumulate(const Device& device, T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config) +{ + for (uint64_t i = 0; i < n; ++i) { + vec_a[i] = vec_a[i] + vec_b[i]; + } + return eIcicleError::SUCCESS; +} + +REGISTER_VECTOR_ACCUMULATE_BACKEND("CPU", cpu_vector_accumulate); + +/*********************************** SUB ***********************************/ +template +eIcicleError +cpu_vector_sub(const Device& device, const T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_2vectors_op(VecOperation::VECTOR_SUB, vec_a, vec_b, n, config, output); +} + +REGISTER_VECTOR_SUB_BACKEND("CPU", cpu_vector_sub); + +/*********************************** MUL ***********************************/ +template +eIcicleError +cpu_vector_mul(const Device& device, const T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_2vectors_op(VecOperation::VECTOR_MUL, vec_a, vec_b, n, config, output); +} + +REGISTER_VECTOR_MUL_BACKEND("CPU", cpu_vector_mul); + +/*********************************** DIV ***********************************/ +template +eIcicleError +cpu_vector_div(const Device& device, const T* vec_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_2vectors_op(VecOperation::VECTOR_DIV, vec_a, vec_b, n, config, output); +} + +REGISTER_VECTOR_DIV_BACKEND("CPU", cpu_vector_div); + +/*********************************** SUM ***********************************/ +template +eIcicleError cpu_vector_sum(const Device& device, const T* vec_a, uint64_t n, const VecOpsConfig& config, T* output) +{ + TasksManager> task_manager(get_nof_workers(config)); + bool output_initialized = false; + uint64_t vec_s_offset = 0; + VectorOpTask* task_p; + // run until all vector deployed and all tasks completed + do { + task_p = vec_s_offset < n ? task_manager.get_idle_or_completed_task() : task_manager.get_completed_task(); + if (task_p->is_completed()) { + *output = output_initialized ? task_p->m_intermidiate_res : *output + task_p->m_intermidiate_res; + } + if (vec_s_offset < n) { + task_p->send_intermidiate_res_task( + VecOperation::VECTOR_SUM, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, n - vec_s_offset), vec_a + vec_s_offset); + vec_s_offset += NOF_OPERATIONS_PER_TASK; + } + } while (task_p != nullptr); + return eIcicleError::SUCCESS; +} + +// Once backend will support - uncomment the following line +// REGISTER_VECTOR_SUM_BACKEND("CPU", cpu_vector_sum); +/*********************************** SUM ***********************************/ +template +eIcicleError cpu_vector_product(const Device& device, const T* vec_a, uint64_t n, const VecOpsConfig& config, T* output) +{ + TasksManager> task_manager(get_nof_workers(config)); + bool output_initialized = false; + uint64_t vec_s_offset = 0; + VectorOpTask* task_p; + // run until all vector deployed and all tasks completed + do { + task_p = vec_s_offset < n ? task_manager.get_idle_or_completed_task() : task_manager.get_completed_task(); + if (task_p->is_completed()) { + *output = output_initialized ? task_p->m_intermidiate_res : *output * task_p->m_intermidiate_res; + } + if (vec_s_offset < n) { + task_p->send_intermidiate_res_task( + VecOperation::VECTOR_SUM, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, n - vec_s_offset), vec_a + vec_s_offset); + vec_s_offset += NOF_OPERATIONS_PER_TASK; + } + } while (task_p != nullptr); + return eIcicleError::SUCCESS; +} + +// Once backend will support - uncomment the following line +// REGISTER_VECTOR_SUM_BACKEND("CPU", cpu_vector_sum); + +/*********************************** MUL BY SCALAR***********************************/ +template +eIcicleError cpu_scalar_mul( + const Device& device, const T* scalar_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_scalar_vector_op(VecOperation::SCALAR_MUL_VEC, scalar_a, vec_b, n, config, output); +} + +REGISTER_SCALAR_MUL_VEC_BACKEND("CPU", cpu_scalar_mul); + +/*********************************** Scalar + Vector***********************************/ +template +eIcicleError cpu_scalar_add( + const Device& device, const T* scalar_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_scalar_vector_op(VecOperation::SCALAR_ADD_VEC, scalar_a, vec_b, n, config, output); +} + +REGISTER_SCALAR_ADD_VEC_BACKEND("CPU", cpu_scalar_add); + +/*********************************** Scalar - Vector***********************************/ +template +eIcicleError cpu_scalar_sub( + const Device& device, const T* scalar_a, const T* vec_b, uint64_t n, const VecOpsConfig& config, T* output) +{ + return cpu_scalar_vector_op(VecOperation::SCALAR_SUB_VEC, scalar_a, vec_b, n, config, output); +} + +REGISTER_SCALAR_SUB_VEC_BACKEND("CPU", cpu_scalar_sub); + +/*********************************** CONVERT MONTGOMERY ***********************************/ +template +eIcicleError cpu_convert_montgomery( + const Device& device, const T* input, uint64_t n, bool is_into, const VecOpsConfig& config, T* output) +{ + TasksManager> task_manager(get_nof_workers(config)); + for (uint64_t i = 0; i < n; i += NOF_OPERATIONS_PER_TASK) { + VectorOpTask* task_p = task_manager.get_idle_or_completed_task(); + task_p->send_1op_task( + is_into ? CONVERT_TO_MONTGOMERY : CONVERT_FROM_MONTGOMERY, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, n - i), + input + i, output + i); + } + task_manager.wait_done(); + return eIcicleError::SUCCESS; +} + +REGISTER_CONVERT_MONTGOMERY_BACKEND("CPU", cpu_convert_montgomery); + +#ifdef EXT_FIELD +REGISTER_VECTOR_ADD_EXT_FIELD_BACKEND("CPU", cpu_vector_add); +REGISTER_VECTOR_ACCUMULATE_EXT_FIELD_BACKEND("CPU", cpu_vector_accumulate); +REGISTER_VECTOR_SUB_EXT_FIELD_BACKEND("CPU", cpu_vector_sub); +REGISTER_VECTOR_MUL_EXT_FIELD_BACKEND("CPU", cpu_vector_mul); +REGISTER_CONVERT_MONTGOMERY_EXT_FIELD_BACKEND("CPU", cpu_convert_montgomery); +#endif // EXT_FIELD + +/*********************************** TRANSPOSE ***********************************/ +template +eIcicleError cpu_matrix_transpose( + const Device& device, const T* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, T* mat_out) +{ + // Check for invalid arguments + if (!mat_in || !mat_out || nof_rows == 0 || nof_cols == 0) { return eIcicleError::INVALID_ARGUMENT; } + + // Perform the matrix transpose + for (uint32_t i = 0; i < nof_rows; ++i) { + for (uint32_t j = 0; j < nof_cols; ++j) { + mat_out[j * nof_rows + i] = mat_in[i * nof_cols + j]; + } + } + + return eIcicleError::SUCCESS; +} + +REGISTER_MATRIX_TRANSPOSE_BACKEND("CPU", cpu_matrix_transpose); +#ifdef EXT_FIELD +REGISTER_MATRIX_TRANSPOSE_EXT_FIELD_BACKEND("CPU", cpu_matrix_transpose); +#endif // EXT_FIELD + +/*********************************** BIT REVERSE ***********************************/ +template +eIcicleError +cpu_bit_reverse(const Device& device, const T* vec_in, uint64_t size, const VecOpsConfig& config, T* vec_out) +{ + // Check for invalid arguments + if (!vec_in || !vec_out || size == 0) { return eIcicleError::INVALID_ARGUMENT; } + + // Calculate log2(size) + int logn = static_cast(std::floor(std::log2(size))); + if ((1ULL << logn) != size) { + return eIcicleError::INVALID_ARGUMENT; // Ensure size is a power of 2 + } + + // Perform the bit reverse + TasksManager> task_manager(get_nof_workers(config)); + for (uint64_t i = 0; i < size; i += NOF_OPERATIONS_PER_TASK) { + VectorOpTask* task_p = task_manager.get_idle_or_completed_task(); + task_p->send_bitrev_task( + BIT_REVERSE, logn, i, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, size - i), vec_in, vec_out); + } + task_manager.wait_done(); + return eIcicleError::SUCCESS; +} + +REGISTER_BIT_REVERSE_BACKEND("CPU", cpu_bit_reverse); +#ifdef EXT_FIELD +REGISTER_BIT_REVERSE_EXT_FIELD_BACKEND("CPU", cpu_bit_reverse); +#endif // EXT_FIELD + +/*********************************** SLICE ***********************************/ + +template +eIcicleError cpu_slice( + const Device& device, + const T* vec_in, + uint64_t offset, + uint64_t stride, + uint64_t size, + const VecOpsConfig& config, + T* vec_out) +{ + if (vec_in == nullptr || vec_out == nullptr) { + ICICLE_LOG_ERROR << "Error: Invalid argument - input or output vector is null"; + return eIcicleError::INVALID_ARGUMENT; + } + + TasksManager> task_manager(get_nof_workers(config)); + for (uint64_t i = 0; i < size; i += NOF_OPERATIONS_PER_TASK) { + VectorOpTask* task_p = task_manager.get_idle_or_completed_task(); + task_p->send_slice_task( + SLICE, stride, std::min((uint64_t)NOF_OPERATIONS_PER_TASK, size - i), vec_in + offset + i * stride, vec_out + i); + } + task_manager.wait_done(); + return eIcicleError::SUCCESS; +} + +REGISTER_SLICE_BACKEND("CPU", cpu_slice); +#ifdef EXT_FIELD +REGISTER_SLICE_EXT_FIELD_BACKEND("CPU", cpu_slice); +#endif // EXT_FIELD + +/*********************************** Polynomial evaluation ***********************************/ + +template +eIcicleError cpu_poly_eval( + const Device& device, + const T* coeffs, + uint64_t coeffs_size, + const T* domain, + uint64_t domain_size, + const VecOpsConfig& config, + T* evals /*OUT*/) +{ + // using Horner's method + // example: ax^2+bx+c is computed as (1) r=a, (2) r=r*x+b, (3) r=r*x+c + for (uint64_t eval_idx = 0; eval_idx < domain_size; ++eval_idx) { + evals[eval_idx] = coeffs[coeffs_size - 1]; + for (int64_t coeff_idx = coeffs_size - 2; coeff_idx >= 0; --coeff_idx) { + evals[eval_idx] = evals[eval_idx] * domain[eval_idx] + coeffs[coeff_idx]; + } + } + return eIcicleError::SUCCESS; +} + +REGISTER_POLYNOMIAL_EVAL("CPU", cpu_poly_eval); + +/*********************************** Highest non-zero idx ***********************************/ +template +eIcicleError cpu_highest_non_zero_idx( + const Device& device, const T* input, uint64_t size, const VecOpsConfig& config, int64_t* out_idx /*OUT*/) +{ + *out_idx = -1; // zero vector is considered '-1' since 0 would be zero in vec[0] + for (int64_t i = size - 1; i >= 0; --i) { + if (input[i] != T::zero()) { + *out_idx = i; + break; + } + } + return eIcicleError::SUCCESS; +} + +REGISTER_HIGHEST_NON_ZERO_IDX_BACKEND("CPU", cpu_highest_non_zero_idx); + +/*============================== polynomial division ==============================*/ +template +void school_book_division_step_cpu(T* r, T* q, const T* b, int deg_r, int deg_b, const T& lc_b_inv) +{ + int64_t monomial = deg_r - deg_b; // monomial=1 is 'x', monomial=2 is x^2 etc. + + T lc_r = r[deg_r]; + T monomial_coeff = lc_r * lc_b_inv; // lc_r / lc_b + + // adding monomial s to q (q=q+s) + q[monomial] = monomial_coeff; + + for (int i = monomial; i <= deg_r; ++i) { + T b_coeff = b[i - monomial]; + r[i] = r[i] - monomial_coeff * b_coeff; + } +} + +template +eIcicleError cpu_poly_divide( + const Device& device, + const T* numerator, + int64_t numerator_deg, + const T* denumerator, + int64_t denumerator_deg, + const VecOpsConfig& config, + T* q_out /*OUT*/, + uint64_t q_size, + T* r_out /*OUT*/, + uint64_t r_size) +{ + ICICLE_ASSERT(r_size >= numerator_deg) + << "polynomial division expects r(x) size to be similar to numerator size and higher than numerator degree(x)"; + ICICLE_ASSERT(q_size >= (numerator_deg - denumerator_deg + 1)) + << "polynomial division expects q(x) size to be at least deg(numerator)-deg(denumerator)+1"; + + ICICLE_CHECK(icicle_copy_async(r_out, numerator, r_size * sizeof(T), config.stream)); + + // invert largest coeff of b + const T& lc_b_inv = T::inverse(denumerator[denumerator_deg]); + + int64_t deg_r = numerator_deg; + while (deg_r >= denumerator_deg) { + // each iteration is removing the largest monomial in r until deg(r)); \ No newline at end of file diff --git a/icicle/backend/cpu/src/polynomials/cpu_polynomial_backend.cpp b/icicle/backend/cpu/src/polynomials/cpu_polynomial_backend.cpp new file mode 100644 index 000000000..4d3bb1604 --- /dev/null +++ b/icicle/backend/cpu/src/polynomials/cpu_polynomial_backend.cpp @@ -0,0 +1,39 @@ + +#include "icicle/backend/polynomial_backend.h" +#include "icicle/polynomials/default_backend/default_poly_context.h" +#include "icicle/polynomials/default_backend/default_poly_backend.h" + +namespace polynomials { + + using icicle::DefaultPolynomialBackend; + using icicle::DefaultPolynomialContext; + + /*============================== Polynomial CPU-factory ==============================*/ + + template + class CPUPolynomialFactory : public AbstractPolynomialFactory + { + public: + CPUPolynomialFactory() {} + ~CPUPolynomialFactory() {} + std::shared_ptr> create_context() override; + std::shared_ptr> create_backend() override; + }; + + template + std::shared_ptr> CPUPolynomialFactory::create_context() + { + return std::make_shared>(nullptr); + } + + template + std::shared_ptr> CPUPolynomialFactory::create_backend() + { + return std::make_shared>(nullptr); + } + + /************************************** BACKEND REGISTRATION **************************************/ + + REGISTER_SCALAR_POLYNOMIAL_FACTORY_BACKEND("CPU", CPUPolynomialFactory) + +} // namespace polynomials \ No newline at end of file diff --git a/icicle/benchmarks/CMakeLists.txt b/icicle/benchmarks/CMakeLists.txt deleted file mode 100644 index 2f8b8a496..000000000 --- a/icicle/benchmarks/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ - -add_executable(benches benches.cu) -target_link_libraries(benches benchmark::benchmark) -target_include_directories(benches PUBLIC ${CMAKE_SOURCE_DIR}/include/) -find_package(benchmark REQUIRED) diff --git a/icicle/benchmarks/README.md b/icicle/benchmarks/README.md deleted file mode 100644 index 0e56cc8cb..000000000 --- a/icicle/benchmarks/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# How to use benchmarks - -ICICLE uses [google benchmarks](https://github.com/google/benchmark) to measure the performance of primitives. - -To run benchmarks, make sure you have everything installed to run ICICLE (see top-level README for that). Next, you need to install google benchmarks library as described in their [documentation](https://github.com/google/benchmark?tab=readme-ov-file#installation). When running benchmarks, export the path to this installation: - -``` -export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH: -``` - -Then to benchmark field arithmetic, say, on `babybear` field, run: - -``` -cmake -UCURVE -UFIELD -UG2 -UEXT_FIELD -DFIELD=babybear -DEXT_FIELD=ON -S . -B build; -cmake --build build; -build/benches --benchmark_counters_tabular=true -``` - -`-U` parameters are needed to clear variables from previous runs and `EXT_FIELD` can be disabled if benhcmarking the extension field is not needed. To benchmark a curve, say, `bn254`, change the first `cmake` call to: - -``` -cmake -UCURVE -UFIELD -UG2 -UEXT_FIELD -DCURVE=bn254 -S . -B build; -``` - -Benchmarks measure throughput of very cheap operations like field multiplication or EC addition by repeating them very many times in parallel, so throughput is the main metric to look at. \ No newline at end of file diff --git a/icicle/benchmarks/benches.cu b/icicle/benchmarks/benches.cu deleted file mode 100644 index f7ff86357..000000000 --- a/icicle/benchmarks/benches.cu +++ /dev/null @@ -1,6 +0,0 @@ -#include "field_benchmarks.cu" -#ifdef CURVE_ID -#include "curve_benchmarks.cu" -#endif - -BENCHMARK_MAIN(); \ No newline at end of file diff --git a/icicle/benchmarks/curve_benchmarks.cu b/icicle/benchmarks/curve_benchmarks.cu deleted file mode 100644 index 379793bac..000000000 --- a/icicle/benchmarks/curve_benchmarks.cu +++ /dev/null @@ -1,79 +0,0 @@ -#include -#include "utils/test_functions.cuh" -#include "curves/curve_config.cuh" - -using namespace curve_config; -using namespace benchmark; - -static void BM_MixedECAdd(State& state) -{ - constexpr int N = 128; - int n = state.range(0) / N; - projective_t* points1; - affine_t* points2; - assert(!cudaMalloc(&points1, n * sizeof(projective_t))); - assert(!cudaMalloc(&points2, n * sizeof(affine_t))); - - projective_t* h_points1 = (projective_t*)malloc(n * sizeof(projective_t)); - affine_t* h_points2 = (affine_t*)malloc(n * sizeof(affine_t)); - projective_t::rand_host_many(h_points1, n); - projective_t::rand_host_many_affine(h_points2, n); - cudaMemcpy(points1, h_points1, sizeof(projective_t) * n, cudaMemcpyHostToDevice); - cudaMemcpy(points2, h_points2, sizeof(affine_t) * n, cudaMemcpyHostToDevice); - - for (auto _ : state) { - cudaEvent_t start, stop; - cudaEventCreate(&start); - cudaEventCreate(&stop); - cudaEventRecord(start); - assert((vec_add(points1, points2, points1, n)) == cudaSuccess); - assert(cudaStreamSynchronize(0) == cudaSuccess); - cudaEventRecord(stop); - - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, start, stop); - - state.SetIterationTime((double)(milliseconds / 1000)); - } - state.counters["Throughput"] = Counter(state.range(0), Counter::kIsRate | Counter::kIsIterationInvariant); - cudaFree(points1); - cudaFree(points2); -} - -static void BM_FullECAdd(benchmark::State& state) -{ - constexpr int N = 128; - int n = state.range(0) / N; - projective_t* points1; - projective_t* points2; - assert(!cudaMalloc(&points1, n * sizeof(projective_t))); - assert(!cudaMalloc(&points2, n * sizeof(projective_t))); - - projective_t* h_points1 = (projective_t*)malloc(n * sizeof(projective_t)); - projective_t* h_points2 = (projective_t*)malloc(n * sizeof(projective_t)); - projective_t::rand_host_many(h_points1, n); - projective_t::rand_host_many(h_points2, n); - cudaMemcpy(points1, h_points1, sizeof(projective_t) * n, cudaMemcpyHostToDevice); - cudaMemcpy(points2, h_points2, sizeof(projective_t) * n, cudaMemcpyHostToDevice); - - for (auto _ : state) { - cudaEvent_t start, stop; - cudaEventCreate(&start); - cudaEventCreate(&stop); - cudaEventRecord(start); - assert((vec_add(points1, points2, points1, n)) == cudaSuccess); - assert(cudaStreamSynchronize(0) == cudaSuccess); - cudaEventRecord(stop); - - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, start, stop); - - state.SetIterationTime((double)(milliseconds / 1000)); - } - state.counters["Throughput"] = Counter(state.range(0), Counter::kIsRate | Counter::kIsIterationInvariant); - cudaFree(points1); - cudaFree(points2); -} - -BENCHMARK(BM_FullECAdd)->Range(1 << 27, 1 << 27)->Unit(benchmark::kMillisecond); -BENCHMARK(BM_MixedECAdd)->Range(1 << 27, 1 << 27)->Unit(benchmark::kMillisecond); diff --git a/icicle/benchmarks/field_benchmarks.cu b/icicle/benchmarks/field_benchmarks.cu deleted file mode 100644 index cf5f2a24c..000000000 --- a/icicle/benchmarks/field_benchmarks.cu +++ /dev/null @@ -1,108 +0,0 @@ -#include -#include "utils/test_functions.cuh" -#include "fields/field_config.cuh" - -using namespace field_config; -using namespace benchmark; - -template -static void BM_FieldAdd(State& state) -{ - constexpr int N = 256; - int n = state.range(0) / N; - T* scalars1; - T* scalars2; - assert(!cudaMalloc(&scalars1, n * sizeof(T))); - assert(!cudaMalloc(&scalars2, n * sizeof(T))); - - assert(device_populate_random(scalars1, n) == cudaSuccess); - assert(device_populate_random(scalars2, n) == cudaSuccess); - - for (auto _ : state) { - cudaEvent_t start, stop; - cudaEventCreate(&start); - cudaEventCreate(&stop); - cudaEventRecord(start); - assert((vec_add(scalars1, scalars2, scalars1, n)) == cudaSuccess); - assert(cudaStreamSynchronize(0) == cudaSuccess); - cudaEventRecord(stop); - - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, start, stop); - - state.SetIterationTime((double)(milliseconds / 1000)); - } - state.counters["Throughput"] = Counter(state.range(0), Counter::kIsRate | Counter::kIsIterationInvariant); - cudaFree(scalars1); - cudaFree(scalars2); -} - -template -static void BM_FieldMul(State& state) -{ - constexpr int N = 128; - int n = state.range(0) / N; - T* scalars1; - T* scalars2; - assert(!cudaMalloc(&scalars1, n * sizeof(T))); - assert(!cudaMalloc(&scalars2, n * sizeof(T))); - - assert(device_populate_random(scalars1, n) == cudaSuccess); - assert(device_populate_random(scalars2, n) == cudaSuccess); - - for (auto _ : state) { - cudaEvent_t start, stop; - cudaEventCreate(&start); - cudaEventCreate(&stop); - cudaEventRecord(start); - assert((vec_mul(scalars1, scalars2, scalars1, n)) == cudaSuccess); - assert(cudaStreamSynchronize(0) == cudaSuccess); - cudaEventRecord(stop); - - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, start, stop); - - state.SetIterationTime((double)(milliseconds / 1000)); - } - state.counters["Throughput"] = Counter(state.range(0), Counter::kIsRate | Counter::kIsIterationInvariant); - cudaFree(scalars1); - cudaFree(scalars2); -} - -template -static void BM_FieldSqr(State& state) -{ - constexpr int N = 128; - int n = state.range(0) / N; - T* scalars; - assert(!cudaMalloc(&scalars, n * sizeof(T))); - - assert(device_populate_random(scalars, n) == cudaSuccess); - - for (auto _ : state) { - cudaEvent_t start, stop; - cudaEventCreate(&start); - cudaEventCreate(&stop); - cudaEventRecord(start); - assert((field_vec_sqr(scalars, scalars, n)) == cudaSuccess); - assert(cudaStreamSynchronize(0) == cudaSuccess); - cudaEventRecord(stop); - - float milliseconds = 0; - cudaEventElapsedTime(&milliseconds, start, stop); - - state.SetIterationTime((double)(milliseconds / 1000)); - } - state.counters["Throughput"] = Counter(state.range(0), Counter::kIsRate | Counter::kIsIterationInvariant); - cudaFree(scalars); -} - -BENCHMARK(BM_FieldAdd)->Range(1 << 28, 1 << 28)->Unit(kMicrosecond); -BENCHMARK(BM_FieldMul)->Range(1 << 27, 1 << 27)->Unit(kMicrosecond); -BENCHMARK(BM_FieldSqr)->Range(1 << 27, 1 << 27)->Unit(kMicrosecond); - -#ifdef EXT_FIELD -BENCHMARK(BM_FieldAdd)->Range(1 << 28, 1 << 28)->Unit(kMicrosecond); -BENCHMARK(BM_FieldMul)->Range(1 << 27, 1 << 27)->Unit(kMicrosecond); -BENCHMARK(BM_FieldSqr)->Range(1 << 27, 1 << 27)->Unit(kMicrosecond); -#endif diff --git a/icicle/cmake/Common.cmake b/icicle/cmake/Common.cmake deleted file mode 100644 index 49624f3ee..000000000 --- a/icicle/cmake/Common.cmake +++ /dev/null @@ -1,63 +0,0 @@ -function(set_env) - set(CMAKE_CXX_STANDARD 17 PARENT_SCOPE) - set(CMAKE_CUDA_STANDARD 17 PARENT_SCOPE) - set(CMAKE_CUDA_STANDARD_REQUIRED TRUE PARENT_SCOPE) - set(CMAKE_CXX_STANDARD_REQUIRED TRUE PARENT_SCOPE) - - if("$ENV{ICICLE_PIC}" STREQUAL "OFF" OR ICICLE_PIC STREQUAL "OFF") - message(WARNING "Note that PIC (position-independent code) is disabled.") - else() - set(CMAKE_POSITION_INDEPENDENT_CODE ON) - endif() -endfunction() - -function(set_gpu_env) - # add the target cuda architectures - # each additional architecture increases the compilation time and output file size - if(DEFINED CUDA_ARCH) # user defined arch takes priority - set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH} PARENT_SCOPE) - elseif(${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.24.0") # otherwise, use native to detect GPU arch - set(CMAKE_CUDA_ARCHITECTURES native PARENT_SCOPE) - else() - find_program(_nvidia_smi "nvidia-smi") - - if(_nvidia_smi) - execute_process( - COMMAND ${_nvidia_smi} --query-gpu=compute_cap --format=csv,noheader - OUTPUT_VARIABLE GPU_COMPUTE_CAPABILITIES - OUTPUT_STRIP_TRAILING_WHITESPACE - ) - # Process the output to form the CUDA architectures string - string(REPLACE "\n" ";" GPU_COMPUTE_CAPABILITIES_LIST "${GPU_COMPUTE_CAPABILITIES}") - - set(CUDA_ARCHITECTURES "") - foreach(CAPABILITY ${GPU_COMPUTE_CAPABILITIES_LIST}) - # Remove the dot in compute capability to match CMake format - string(REPLACE "." "" CAPABILITY "${CAPABILITY}") - if(CUDA_ARCHITECTURES) - set(CUDA_ARCHITECTURES "${CUDA_ARCHITECTURES};${CAPABILITY}") - else() - set(CUDA_ARCHITECTURES "${CAPABILITY}") - endif() - endforeach() - - message("Setting CMAKE_CUDA_ARCHITECTURES to: ${CUDA_ARCHITECTURES}") - set(CMAKE_CUDA_ARCHITECTURES "${CUDA_ARCHITECTURES}" PARENT_SCOPE) - else() - # no GPUs found, like on Github CI runners - message("Setting CMAKE_CUDA_ARCHITECTURES to: 50") - set(CMAKE_CUDA_ARCHITECTURES 50 PARENT_SCOPE) # some safe value - endif() - endif() - - # Check CUDA version and, if possible, enable multi-threaded compilation - if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL "12.2") - message(STATUS "Using multi-threaded CUDA compilation.") - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --split-compile 0" PARENT_SCOPE) - else() - message(STATUS "Can't use multi-threaded CUDA compilation.") - endif() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr" PARENT_SCOPE) - set(CMAKE_CUDA_FLAGS_RELEASE "" PARENT_SCOPE) - set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -lineinfo" PARENT_SCOPE) -endfunction() diff --git a/icicle/cmake/CurvesCommon.cmake b/icicle/cmake/CurvesCommon.cmake deleted file mode 100644 index 952b47a4d..000000000 --- a/icicle/cmake/CurvesCommon.cmake +++ /dev/null @@ -1,17 +0,0 @@ -function(check_curve) - set(SUPPORTED_CURVES bn254;bls12_381;bls12_377;bw6_761;grumpkin) - - set(IS_CURVE_SUPPORTED FALSE) - set(I 0) - foreach (SUPPORTED_CURVE ${SUPPORTED_CURVES}) - math(EXPR I "${I} + 1") - if (CURVE STREQUAL SUPPORTED_CURVE) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DCURVE_ID=${I} -DFIELD_ID=${I}" PARENT_SCOPE) - set(IS_CURVE_SUPPORTED TRUE) - endif () - endforeach() - - if (NOT IS_CURVE_SUPPORTED) - message( FATAL_ERROR "The value of CURVE variable: ${CURVE} is not one of the supported curves: ${SUPPORTED_CURVES}" ) - endif () -endfunction() diff --git a/icicle/cmake/FieldsCommon.cmake b/icicle/cmake/FieldsCommon.cmake deleted file mode 100644 index e38f420c5..000000000 --- a/icicle/cmake/FieldsCommon.cmake +++ /dev/null @@ -1,17 +0,0 @@ -function(check_field) - set(SUPPORTED_FIELDS babybear;stark252;m31) - - set(IS_FIELD_SUPPORTED FALSE) - set(I 1000) - foreach (SUPPORTED_FIELD ${SUPPORTED_FIELDS}) - math(EXPR I "${I} + 1") - if (FIELD STREQUAL SUPPORTED_FIELD) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DFIELD_ID=${I}" PARENT_SCOPE) - set(IS_FIELD_SUPPORTED TRUE) - endif () - endforeach() - - if (NOT IS_FIELD_SUPPORTED) - message( FATAL_ERROR "The value of FIELD variable: ${FIELD} is not one of the supported fields: ${SUPPORTED_FIELDS}" ) - endif () -endfunction() \ No newline at end of file diff --git a/icicle/cmake/curve.cmake b/icicle/cmake/curve.cmake new file mode 100644 index 000000000..c82d1b90b --- /dev/null +++ b/icicle/cmake/curve.cmake @@ -0,0 +1,71 @@ +include(cmake/fields_and_curves.cmake) +include(cmake/target_editor.cmake) + +function(extract_curve_names CURVE_NAMES_OUT) + set(CURVE_NAMES "") + + foreach (ITEM ${ICICLE_CURVES}) + string(REPLACE ":" ";" ITEM_SPLIT ${ITEM}) + list(GET ITEM_SPLIT 1 CURVE_NAME) + list(APPEND CURVE_NAMES ${CURVE_NAME}) + endforeach() + + # Output the list of curve names + set(${CURVE_NAMES_OUT} "${CURVE_NAMES}" PARENT_SCOPE) +endfunction() + +function(check_curve CURVE CURVE_INDEX_OUT FEATURES_STRING_OUT) + set(IS_CURVE_SUPPORTED FALSE) + foreach (ITEM ${ICICLE_CURVES}) + string(REPLACE ":" ";" ITEM_SPLIT ${ITEM}) + list(GET ITEM_SPLIT 0 CURVE_INDEX) + list(GET ITEM_SPLIT 1 CURVE_NAME) + list(GET ITEM_SPLIT 2 FEATURES_STRING) + + if (CURVE STREQUAL CURVE_NAME) + set(IS_CURVE_SUPPORTED TRUE) + message(STATUS "building CURVE_NAME=${CURVE_NAME} ; CURVE_INDEX=${CURVE_INDEX} ; SUPPORTED_FEATURES=${FEATURES_STRING}") + # Output the CURVE_INDEX and FEATURES_STRING + set(${CURVE_INDEX_OUT} "${CURVE_INDEX}" PARENT_SCOPE) + set(${FEATURES_STRING_OUT} "${FEATURES_STRING}" PARENT_SCOPE) + break() + endif () + endforeach() + + if (NOT IS_CURVE_SUPPORTED) + set(ALL_CURVES "") + extract_curve_names(ALL_CURVES) + message(FATAL_ERROR "The value of CURVE variable: ${CURVE} is not supported: choose from [${ALL_CURVES}]") + endif () +endfunction() + + +function(setup_curve_target CURVE CURVE_INDEX FEATURES_STRING) + # the scalar field of the curve is built to a field library (like babybear is built) + setup_field_target(${CURVE} ${CURVE_INDEX} ${FEATURES_STRING}) + + add_library(icicle_curve SHARED) + + # Split FEATURES_STRING into a list using "," as the separator + string(REPLACE "," ";" FEATURES_LIST ${FEATURES_STRING}) + + # customize the curve lib to choose what to include + handle_curve(icicle_curve) # basic curve and field methods, including vec ops + # Handle features + handle_msm(icicle_curve "${FEATURES_LIST}") + handle_g2(icicle_curve "${FEATURES_LIST}") + handle_ecntt(icicle_curve "${FEATURES_LIST}") + # Add additional feature handling calls here + + set_target_properties(icicle_curve PROPERTIES OUTPUT_NAME "icicle_curve_${CURVE}") + target_link_libraries(icicle_curve PUBLIC icicle_device icicle_field pthread) + + # Ensure CURVE is defined in the cache for backends to see + set(CURVE "${CURVE}" CACHE STRING "") + target_compile_definitions(icicle_curve PUBLIC CURVE=${CURVE} CURVE_ID=${CURVE_INDEX}) + + install(TARGETS icicle_curve + RUNTIME DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/" + LIBRARY DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/" + ARCHIVE DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/") +endfunction() \ No newline at end of file diff --git a/icicle/cmake/field.cmake b/icicle/cmake/field.cmake new file mode 100644 index 000000000..92f668539 --- /dev/null +++ b/icicle/cmake/field.cmake @@ -0,0 +1,67 @@ +include(cmake/fields_and_curves.cmake) +include(cmake/target_editor.cmake) + +function(extract_field_names FIELD_NAMES_OUT) + set(FIELD_NAMES "") + + foreach (ITEM ${ICICLE_FIELDS}) + string(REPLACE ":" ";" ITEM_SPLIT ${ITEM}) + list(GET ITEM_SPLIT 1 FIELD_NAME) + list(APPEND FIELD_NAMES ${FIELD_NAME}) + endforeach() + + # Output the list of field names + set(${FIELD_NAMES_OUT} "${FIELD_NAMES}" PARENT_SCOPE) +endfunction() + +function(check_field FIELD FIELD_INDEX_OUT FEATURES_STRING_OUT) + set(IS_FIELD_SUPPORTED FALSE) + foreach (ITEM ${ICICLE_FIELDS}) + string(REPLACE ":" ";" ITEM_SPLIT ${ITEM}) + list(GET ITEM_SPLIT 0 FIELD_INDEX) + list(GET ITEM_SPLIT 1 FIELD_NAME) + list(GET ITEM_SPLIT 2 FEATURES_STRING) + + if (FIELD STREQUAL FIELD_NAME) + set(IS_FIELD_SUPPORTED TRUE) + message(STATUS "building FIELD_NAME=${FIELD_NAME} ; FIELD_INDEX=${FIELD_INDEX} ; SUPPORTED_FEATURES=${FEATURES_STRING}") + # Output the FIELD_INDEX and FEATURES_STRING + set(${FIELD_INDEX_OUT} "${FIELD_INDEX}" PARENT_SCOPE) + set(${FEATURES_STRING_OUT} "${FEATURES_STRING}" PARENT_SCOPE) + break() + endif () + endforeach() + + if (NOT IS_FIELD_SUPPORTED) + set(ALL_FIELDS "") + extract_field_names(ALL_FIELDS) + message(FATAL_ERROR "The value of FIELD variable: ${FIELD} is not supported: choose from [${ALL_FIELDS}]") + endif () +endfunction() + +function(setup_field_target FIELD FIELD_INDEX FEATURES_STRING) + add_library(icicle_field SHARED) + + # Split FEATURES_STRING into a list using "," as the separator + string(REPLACE "," ";" FEATURES_LIST ${FEATURES_STRING}) + + # customize the field lib to choose what to include + handle_field(icicle_field) # basic field methods, including vec ops + # Handle features + handle_ntt(icicle_field "${FEATURES_LIST}") + handle_ext_field(icicle_field "${FEATURES_LIST}") + # Add additional feature handling calls here + + set_target_properties(icicle_field PROPERTIES OUTPUT_NAME "icicle_field_${FIELD}") + target_link_libraries(icicle_field PUBLIC icicle_device pthread) + + # Ensure FIELD is defined in the cache for backends to see + set(FIELD "${FIELD}" CACHE STRING "") + add_compile_definitions(FIELD=${FIELD} FIELD_ID=${FIELD_INDEX}) + + install(TARGETS icicle_field + RUNTIME DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/" + LIBRARY DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/" + ARCHIVE DESTINATION "${CMAKE_INSTALL_PREFIX}/lib/") +endfunction() + diff --git a/icicle/cmake/fields_and_curves.cmake b/icicle/cmake/fields_and_curves.cmake new file mode 100644 index 000000000..e1e5a3b05 --- /dev/null +++ b/icicle/cmake/fields_and_curves.cmake @@ -0,0 +1,18 @@ + +# Define available fields with an index and their supported features +# Format: index:field:features +set(ICICLE_FIELDS + 1001:babybear:NTT,EXT_FIELD + 1002:stark252:NTT + 1003:m31:EXT_FIELD +) + +# Define available curves with an index and their supported features +# Format: index:curve:features +set(ICICLE_CURVES + 1:bn254:NTT,MSM,G2,ECNTT + 2:bls12_381:NTT,MSM,G2,ECNTT + 3:bls12_377:NTT,MSM,G2,ECNTT + 4:bw6_761:NTT,MSM,G2,ECNTT + 5:grumpkin:MSM +) \ No newline at end of file diff --git a/icicle/cmake/target_editor.cmake b/icicle/cmake/target_editor.cmake new file mode 100644 index 000000000..414570372 --- /dev/null +++ b/icicle/cmake/target_editor.cmake @@ -0,0 +1,72 @@ + +# The following functions, each adds a function to a target. +# In addition, some can check feature is enabled for target, given FEATURE_LIST. + +function(handle_field TARGET) + target_sources(${TARGET} PRIVATE + src/fields/ffi_extern.cpp + src/vec_ops.cpp + src/matrix_ops.cpp + ) +endfunction() + +function(handle_curve TARGET) + target_sources(${TARGET} PRIVATE + src/curves/ffi_extern.cpp + src/curves/montgomery_conversion.cpp + ) +endfunction() + +function(handle_ntt TARGET FEATURE_LIST) + if(NTT AND "NTT" IN_LIST FEATURE_LIST) + target_compile_definitions(${TARGET} PUBLIC NTT=${NTT}) + target_sources(${TARGET} PRIVATE + src/ntt.cpp + src/polynomials/polynomials.cpp + src/polynomials/polynomials_c_api.cpp + src/polynomials/polynomials_abstract_factory.cpp + ) + set(NTT ON CACHE BOOL "Enable NTT feature" FORCE) + else() + set(NTT OFF CACHE BOOL "NTT not available for this field" FORCE) + endif() +endfunction() + +function(handle_ext_field TARGET FEATURE_LIST) + if(EXT_FIELD AND "EXT_FIELD" IN_LIST FEATURE_LIST) + target_compile_definitions(${TARGET} PUBLIC EXT_FIELD=${EXT_FIELD}) + set(EXT_FIELD ON CACHE BOOL "Enable EXT_FIELD feature" FORCE) + else() + set(EXT_FIELD OFF CACHE BOOL "EXT_FIELD not available for this field" FORCE) + endif() +endfunction() + +function(handle_msm TARGET FEATURE_LIST) + if(MSM AND "MSM" IN_LIST FEATURE_LIST) + target_compile_definitions(${TARGET} PUBLIC MSM=${MSM}) + target_sources(${TARGET} PRIVATE src/msm.cpp) + set(MSM ON CACHE BOOL "Enable MSM feature" FORCE) + else() + set(MSM OFF CACHE BOOL "MSM not available for this curve" FORCE) + endif() +endfunction() + +function(handle_g2 TARGET FEATURE_LIST) + if(G2 AND "G2" IN_LIST FEATURE_LIST) + target_compile_definitions(${TARGET} PUBLIC G2=${G2}) + set(G2 "G2" CACHE BOOL "Enable G2 feature" FORCE) + else() + set(G2 OFF CACHE BOOL "G2 not available for this curve" FORCE) + endif() +endfunction() + +function(handle_ecntt TARGET FEATURE_LIST) + if(ECNTT AND "ECNTT" IN_LIST FEATURE_LIST) + target_compile_definitions(${TARGET} PUBLIC ECNTT=${ECNTT}) + target_sources(icicle_curve PRIVATE src/ecntt.cpp) + set(ECNTT ON CACHE BOOL "Enable ECNTT feature" FORCE) + else() + set(ECNTT OFF CACHE BOOL "ECNTT not available for this curve" FORCE) + endif() +endfunction() + diff --git a/icicle/cuda-shell.nix b/icicle/cuda-shell.nix deleted file mode 100644 index e7f8963e6..000000000 --- a/icicle/cuda-shell.nix +++ /dev/null @@ -1,28 +0,0 @@ -# Run with `nix-shell cuda-shell.nix` -{ pkgs ? import {} }: -let - gcc10 = pkgs.overrideCC pkgs.stdenv pkgs.gcc10; -in -pkgs.mkShell { - name = "cuda-env-shell"; - buildInputs = [ gcc10 ] ++ (with pkgs; [ - git gitRepo gnupg autoconf curl - procps gnumake util-linux m4 gperf unzip - cudatoolkit linuxPackages.nvidia_x11 - libGLU libGL - xorg.libXi xorg.libXmu freeglut - xorg.libXext xorg.libX11 xorg.libXv xorg.libXrandr zlib - ncurses5 - cmake - ]); - cc = gcc10; - shellHook = '' - export PATH="$PATH:${pkgs.cudatoolkit}/bin:${pkgs.cudatoolkit}/nvvm/bin" - export LD_LIBRARY_PATH=${pkgs.cudatoolkit}/lib:${pkgs.linuxPackages.nvidia_x11}/lib - export CUDA_PATH=${pkgs.cudatoolkit} - export CPATH="${pkgs.cudatoolkit}/include" - export LIBRARY_PATH="$LIBRARY_PATH:/lib:${pkgs.linuxPackages.nvidia_x11}/lib" - export CMAKE_CUDA_COMPILER=$CUDA_PATH/bin/nvcc - export CXX="${pkgs.gcc10}/bin/c++" - ''; -} diff --git a/icicle/include/api/babybear.h b/icicle/include/api/babybear.h deleted file mode 100644 index 64815ceb5..000000000 --- a/icicle/include/api/babybear.h +++ /dev/null @@ -1,142 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef BABYBEAR_API_H -#define BABYBEAR_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "fields/stark_fields/babybear.cuh" -#include "ntt/ntt.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon2/poseidon2.cuh" - -extern "C" cudaError_t babybear_extension_ntt_cuda( - const babybear::extension_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, babybear::extension_t* output); - -extern "C" cudaError_t babybear_poseidon2_create_cuda( - poseidon2::Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - unsigned int alpha, - unsigned int internal_rounds, - unsigned int external_rounds, - const babybear::scalar_t* round_constants, - const babybear::scalar_t* internal_matrix_diag, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t babybear_poseidon2_load_cuda( - poseidon2::Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t babybear_poseidon2_hash_many_cuda( - const poseidon2::Poseidon2* poseidon, - const babybear::scalar_t* inputs, - babybear::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - babybear_poseidon2_delete_cuda(poseidon2::Poseidon2* poseidon, device_context::DeviceContext& ctx); - -extern "C" cudaError_t babybear_build_merkle_tree( - const babybear::scalar_t* leaves, - babybear::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t babybear_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - babybear::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t babybear_mul_cuda( - babybear::scalar_t* vec_a, babybear::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::scalar_t* result); - -extern "C" cudaError_t babybear_add_cuda( - babybear::scalar_t* vec_a, babybear::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::scalar_t* result); - -extern "C" cudaError_t babybear_accumulate_cuda( - babybear::scalar_t* vec_a, babybear::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t babybear_sub_cuda( - babybear::scalar_t* vec_a, babybear::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::scalar_t* result); - -extern "C" cudaError_t babybear_transpose_matrix_cuda( - const babybear::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - babybear::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t babybear_bit_reverse_cuda( - const babybear::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, babybear::scalar_t* output); - - -extern "C" void babybear_generate_scalars(babybear::scalar_t* scalars, int size); - -extern "C" cudaError_t babybear_scalar_convert_montgomery( - babybear::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t babybear_initialize_domain( - babybear::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t babybear_ntt_cuda( - const babybear::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, babybear::scalar_t* output); - -extern "C" cudaError_t babybear_release_domain(device_context::DeviceContext& ctx); - -extern "C" void babybear_extension_generate_scalars(babybear::extension_t* scalars, int size); - -extern "C" cudaError_t babybear_extension_scalar_convert_montgomery( - babybear::extension_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t babybear_extension_mul_cuda( - babybear::extension_t* vec_a, babybear::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::extension_t* result); - -extern "C" cudaError_t babybear_extension_add_cuda( - babybear::extension_t* vec_a, babybear::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::extension_t* result); - -extern "C" cudaError_t babybear_extension_accumulate_cuda( - babybear::extension_t* vec_a, babybear::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t babybear_extension_sub_cuda( - babybear::extension_t* vec_a, babybear::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, babybear::extension_t* result); - -extern "C" cudaError_t babybear_extension_transpose_matrix_cuda( - const babybear::extension_t* input, - uint32_t row_size, - uint32_t column_size, - babybear::extension_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t babybear_extension_bit_reverse_cuda( - const babybear::extension_t* input, uint64_t n, vec_ops::BitReverseConfig& config, babybear::extension_t* output); - - -#endif \ No newline at end of file diff --git a/icicle/include/api/bls12_377.h b/icicle/include/api/bls12_377.h deleted file mode 100644 index 75a113d28..000000000 --- a/icicle/include/api/bls12_377.h +++ /dev/null @@ -1,154 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef BLS12_377_API_H -#define BLS12_377_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "curves/params/bls12_377.cuh" -#include "ntt/ntt.cuh" -#include "msm/msm.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon/poseidon.cuh" - -extern "C" cudaError_t bls12_377_g2_precompute_msm_bases_cuda( - bls12_377::g2_affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bls12_377::g2_affine_t* output_bases); - -extern "C" cudaError_t bls12_377_g2_msm_cuda( - const bls12_377::scalar_t* scalars, const bls12_377::g2_affine_t* points, int msm_size, msm::MSMConfig& config, bls12_377::g2_projective_t* out); - -extern "C" cudaError_t bls12_377_precompute_msm_bases_cuda( - bls12_377::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bls12_377::affine_t* output_bases); - -extern "C" cudaError_t bls12_377_msm_cuda( - const bls12_377::scalar_t* scalars, const bls12_377::affine_t* points, int msm_size, msm::MSMConfig& config, bls12_377::projective_t* out); - -extern "C" bool bls12_377_g2_eq(bls12_377::g2_projective_t* point1, bls12_377::g2_projective_t* point2); - -extern "C" void bls12_377_g2_to_affine(bls12_377::g2_projective_t* point, bls12_377::g2_affine_t* point_out); - -extern "C" void bls12_377_g2_generate_projective_points(bls12_377::g2_projective_t* points, int size); - -extern "C" void bls12_377_g2_generate_affine_points(bls12_377::g2_affine_t* points, int size); - -extern "C" cudaError_t bls12_377_g2_affine_convert_montgomery( - bls12_377::g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_g2_projective_convert_montgomery( - bls12_377::g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_ecntt_cuda( - const bls12_377::projective_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bls12_377::projective_t* output); - -extern "C" bool bls12_377_eq(bls12_377::projective_t* point1, bls12_377::projective_t* point2); - -extern "C" void bls12_377_to_affine(bls12_377::projective_t* point, bls12_377::affine_t* point_out); - -extern "C" void bls12_377_generate_projective_points(bls12_377::projective_t* points, int size); - -extern "C" void bls12_377_generate_affine_points(bls12_377::affine_t* points, int size); - -extern "C" cudaError_t bls12_377_affine_convert_montgomery( - bls12_377::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_projective_convert_montgomery( - bls12_377::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_build_merkle_tree( - const bls12_377::scalar_t* leaves, - bls12_377::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t bls12_377_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - bls12_377::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t bls12_377_poseidon_create_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const bls12_377::scalar_t* round_constants, - const bls12_377::scalar_t* mds_matrix, - const bls12_377::scalar_t* non_sparse_matrix, - const bls12_377::scalar_t* sparse_matrices, - const bls12_377::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_poseidon_load_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_poseidon_hash_many_cuda( - const poseidon::Poseidon* poseidon, - const bls12_377::scalar_t* inputs, - bls12_377::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - bls12_377_poseidon_delete_cuda(poseidon::Poseidon* poseidon); - -extern "C" cudaError_t bls12_377_mul_cuda( - bls12_377::scalar_t* vec_a, bls12_377::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_377::scalar_t* result); - -extern "C" cudaError_t bls12_377_add_cuda( - bls12_377::scalar_t* vec_a, bls12_377::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_377::scalar_t* result); - -extern "C" cudaError_t bls12_377_accumulate_cuda( - bls12_377::scalar_t* vec_a, bls12_377::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t bls12_377_sub_cuda( - bls12_377::scalar_t* vec_a, bls12_377::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_377::scalar_t* result); - -extern "C" cudaError_t bls12_377_transpose_matrix_cuda( - const bls12_377::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - bls12_377::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t bls12_377_bit_reverse_cuda( - const bls12_377::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, bls12_377::scalar_t* output); - - -extern "C" void bls12_377_generate_scalars(bls12_377::scalar_t* scalars, int size); - -extern "C" cudaError_t bls12_377_scalar_convert_montgomery( - bls12_377::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_377_initialize_domain( - bls12_377::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t bls12_377_ntt_cuda( - const bls12_377::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bls12_377::scalar_t* output); - -extern "C" cudaError_t bls12_377_release_domain(device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/bls12_381.h b/icicle/include/api/bls12_381.h deleted file mode 100644 index 9f1a49cd0..000000000 --- a/icicle/include/api/bls12_381.h +++ /dev/null @@ -1,154 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef BLS12_381_API_H -#define BLS12_381_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "curves/params/bls12_381.cuh" -#include "ntt/ntt.cuh" -#include "msm/msm.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon/poseidon.cuh" - -extern "C" cudaError_t bls12_381_g2_precompute_msm_bases_cuda( - bls12_381::g2_affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bls12_381::g2_affine_t* output_bases); - -extern "C" cudaError_t bls12_381_g2_msm_cuda( - const bls12_381::scalar_t* scalars, const bls12_381::g2_affine_t* points, int msm_size, msm::MSMConfig& config, bls12_381::g2_projective_t* out); - -extern "C" cudaError_t bls12_381_precompute_msm_bases_cuda( - bls12_381::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bls12_381::affine_t* output_bases); - -extern "C" cudaError_t bls12_381_msm_cuda( - const bls12_381::scalar_t* scalars, const bls12_381::affine_t* points, int msm_size, msm::MSMConfig& config, bls12_381::projective_t* out); - -extern "C" bool bls12_381_g2_eq(bls12_381::g2_projective_t* point1, bls12_381::g2_projective_t* point2); - -extern "C" void bls12_381_g2_to_affine(bls12_381::g2_projective_t* point, bls12_381::g2_affine_t* point_out); - -extern "C" void bls12_381_g2_generate_projective_points(bls12_381::g2_projective_t* points, int size); - -extern "C" void bls12_381_g2_generate_affine_points(bls12_381::g2_affine_t* points, int size); - -extern "C" cudaError_t bls12_381_g2_affine_convert_montgomery( - bls12_381::g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_g2_projective_convert_montgomery( - bls12_381::g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_ecntt_cuda( - const bls12_381::projective_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bls12_381::projective_t* output); - -extern "C" bool bls12_381_eq(bls12_381::projective_t* point1, bls12_381::projective_t* point2); - -extern "C" void bls12_381_to_affine(bls12_381::projective_t* point, bls12_381::affine_t* point_out); - -extern "C" void bls12_381_generate_projective_points(bls12_381::projective_t* points, int size); - -extern "C" void bls12_381_generate_affine_points(bls12_381::affine_t* points, int size); - -extern "C" cudaError_t bls12_381_affine_convert_montgomery( - bls12_381::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_projective_convert_montgomery( - bls12_381::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_build_merkle_tree( - const bls12_381::scalar_t* leaves, - bls12_381::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t bls12_381_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - bls12_381::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t bls12_381_poseidon_create_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const bls12_381::scalar_t* round_constants, - const bls12_381::scalar_t* mds_matrix, - const bls12_381::scalar_t* non_sparse_matrix, - const bls12_381::scalar_t* sparse_matrices, - const bls12_381::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_poseidon_load_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_poseidon_hash_many_cuda( - const poseidon::Poseidon* poseidon, - const bls12_381::scalar_t* inputs, - bls12_381::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - bls12_381_poseidon_delete_cuda(poseidon::Poseidon* poseidon); - -extern "C" cudaError_t bls12_381_mul_cuda( - bls12_381::scalar_t* vec_a, bls12_381::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_381::scalar_t* result); - -extern "C" cudaError_t bls12_381_add_cuda( - bls12_381::scalar_t* vec_a, bls12_381::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_381::scalar_t* result); - -extern "C" cudaError_t bls12_381_accumulate_cuda( - bls12_381::scalar_t* vec_a, bls12_381::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t bls12_381_sub_cuda( - bls12_381::scalar_t* vec_a, bls12_381::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bls12_381::scalar_t* result); - -extern "C" cudaError_t bls12_381_transpose_matrix_cuda( - const bls12_381::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - bls12_381::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t bls12_381_bit_reverse_cuda( - const bls12_381::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, bls12_381::scalar_t* output); - - -extern "C" void bls12_381_generate_scalars(bls12_381::scalar_t* scalars, int size); - -extern "C" cudaError_t bls12_381_scalar_convert_montgomery( - bls12_381::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bls12_381_initialize_domain( - bls12_381::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t bls12_381_ntt_cuda( - const bls12_381::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bls12_381::scalar_t* output); - -extern "C" cudaError_t bls12_381_release_domain(device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/bn254.h b/icicle/include/api/bn254.h deleted file mode 100644 index 1a9a46b5b..000000000 --- a/icicle/include/api/bn254.h +++ /dev/null @@ -1,190 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef BN254_API_H -#define BN254_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "curves/params/bn254.cuh" -#include "ntt/ntt.cuh" -#include "msm/msm.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon/poseidon.cuh" -#include "poseidon2/poseidon2.cuh" - -extern "C" cudaError_t bn254_g2_precompute_msm_bases_cuda( - bn254::g2_affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bn254::g2_affine_t* output_bases); - -extern "C" cudaError_t bn254_g2_msm_cuda( - const bn254::scalar_t* scalars, const bn254::g2_affine_t* points, int msm_size, msm::MSMConfig& config, bn254::g2_projective_t* out); - -extern "C" cudaError_t bn254_precompute_msm_bases_cuda( - bn254::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bn254::affine_t* output_bases); - -extern "C" cudaError_t bn254_msm_cuda( - const bn254::scalar_t* scalars, const bn254::affine_t* points, int msm_size, msm::MSMConfig& config, bn254::projective_t* out); - -extern "C" bool bn254_g2_eq(bn254::g2_projective_t* point1, bn254::g2_projective_t* point2); - -extern "C" void bn254_g2_to_affine(bn254::g2_projective_t* point, bn254::g2_affine_t* point_out); - -extern "C" void bn254_g2_generate_projective_points(bn254::g2_projective_t* points, int size); - -extern "C" void bn254_g2_generate_affine_points(bn254::g2_affine_t* points, int size); - -extern "C" cudaError_t bn254_g2_affine_convert_montgomery( - bn254::g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_g2_projective_convert_montgomery( - bn254::g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_ecntt_cuda( - const bn254::projective_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bn254::projective_t* output); - -extern "C" bool bn254_eq(bn254::projective_t* point1, bn254::projective_t* point2); - -extern "C" void bn254_to_affine(bn254::projective_t* point, bn254::affine_t* point_out); - -extern "C" void bn254_generate_projective_points(bn254::projective_t* points, int size); - -extern "C" void bn254_generate_affine_points(bn254::affine_t* points, int size); - -extern "C" cudaError_t bn254_affine_convert_montgomery( - bn254::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_projective_convert_montgomery( - bn254::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_poseidon2_create_cuda( - poseidon2::Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - unsigned int alpha, - unsigned int internal_rounds, - unsigned int external_rounds, - const bn254::scalar_t* round_constants, - const bn254::scalar_t* internal_matrix_diag, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t bn254_poseidon2_load_cuda( - poseidon2::Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t bn254_poseidon2_hash_many_cuda( - const poseidon2::Poseidon2* poseidon, - const bn254::scalar_t* inputs, - bn254::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - bn254_poseidon2_delete_cuda(poseidon2::Poseidon2* poseidon, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_build_merkle_tree( - const bn254::scalar_t* leaves, - bn254::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t bn254_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - bn254::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t bn254_poseidon_create_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const bn254::scalar_t* round_constants, - const bn254::scalar_t* mds_matrix, - const bn254::scalar_t* non_sparse_matrix, - const bn254::scalar_t* sparse_matrices, - const bn254::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_poseidon_load_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_poseidon_hash_many_cuda( - const poseidon::Poseidon* poseidon, - const bn254::scalar_t* inputs, - bn254::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - bn254_poseidon_delete_cuda(poseidon::Poseidon* poseidon); - -extern "C" cudaError_t bn254_mul_cuda( - bn254::scalar_t* vec_a, bn254::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bn254::scalar_t* result); - -extern "C" cudaError_t bn254_add_cuda( - bn254::scalar_t* vec_a, bn254::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bn254::scalar_t* result); - -extern "C" cudaError_t bn254_accumulate_cuda( - bn254::scalar_t* vec_a, bn254::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t bn254_sub_cuda( - bn254::scalar_t* vec_a, bn254::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bn254::scalar_t* result); - -extern "C" cudaError_t bn254_transpose_matrix_cuda( - const bn254::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - bn254::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t bn254_bit_reverse_cuda( - const bn254::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, bn254::scalar_t* output); - - -extern "C" void bn254_generate_scalars(bn254::scalar_t* scalars, int size); - -extern "C" cudaError_t bn254_scalar_convert_montgomery( - bn254::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bn254_initialize_domain( - bn254::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t bn254_ntt_cuda( - const bn254::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bn254::scalar_t* output); - -extern "C" cudaError_t bn254_release_domain(device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/bw6_761.h b/icicle/include/api/bw6_761.h deleted file mode 100644 index 531e4514f..000000000 --- a/icicle/include/api/bw6_761.h +++ /dev/null @@ -1,154 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef BW6_761_API_H -#define BW6_761_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "curves/params/bw6_761.cuh" -#include "ntt/ntt.cuh" -#include "msm/msm.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon/poseidon.cuh" - -extern "C" cudaError_t bw6_761_g2_precompute_msm_bases_cuda( - bw6_761::g2_affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bw6_761::g2_affine_t* output_bases); - -extern "C" cudaError_t bw6_761_g2_msm_cuda( - const bw6_761::scalar_t* scalars, const bw6_761::g2_affine_t* points, int msm_size, msm::MSMConfig& config, bw6_761::g2_projective_t* out); - -extern "C" cudaError_t bw6_761_precompute_msm_bases_cuda( - bw6_761::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - bw6_761::affine_t* output_bases); - -extern "C" cudaError_t bw6_761_msm_cuda( - const bw6_761::scalar_t* scalars, const bw6_761::affine_t* points, int msm_size, msm::MSMConfig& config, bw6_761::projective_t* out); - -extern "C" bool bw6_761_g2_eq(bw6_761::g2_projective_t* point1, bw6_761::g2_projective_t* point2); - -extern "C" void bw6_761_g2_to_affine(bw6_761::g2_projective_t* point, bw6_761::g2_affine_t* point_out); - -extern "C" void bw6_761_g2_generate_projective_points(bw6_761::g2_projective_t* points, int size); - -extern "C" void bw6_761_g2_generate_affine_points(bw6_761::g2_affine_t* points, int size); - -extern "C" cudaError_t bw6_761_g2_affine_convert_montgomery( - bw6_761::g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_g2_projective_convert_montgomery( - bw6_761::g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_ecntt_cuda( - const bw6_761::projective_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bw6_761::projective_t* output); - -extern "C" bool bw6_761_eq(bw6_761::projective_t* point1, bw6_761::projective_t* point2); - -extern "C" void bw6_761_to_affine(bw6_761::projective_t* point, bw6_761::affine_t* point_out); - -extern "C" void bw6_761_generate_projective_points(bw6_761::projective_t* points, int size); - -extern "C" void bw6_761_generate_affine_points(bw6_761::affine_t* points, int size); - -extern "C" cudaError_t bw6_761_affine_convert_montgomery( - bw6_761::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_projective_convert_montgomery( - bw6_761::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_build_merkle_tree( - const bw6_761::scalar_t* leaves, - bw6_761::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t bw6_761_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - bw6_761::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t bw6_761_poseidon_create_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const bw6_761::scalar_t* round_constants, - const bw6_761::scalar_t* mds_matrix, - const bw6_761::scalar_t* non_sparse_matrix, - const bw6_761::scalar_t* sparse_matrices, - const bw6_761::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_poseidon_load_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_poseidon_hash_many_cuda( - const poseidon::Poseidon* poseidon, - const bw6_761::scalar_t* inputs, - bw6_761::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - bw6_761_poseidon_delete_cuda(poseidon::Poseidon* poseidon); - -extern "C" cudaError_t bw6_761_mul_cuda( - bw6_761::scalar_t* vec_a, bw6_761::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bw6_761::scalar_t* result); - -extern "C" cudaError_t bw6_761_add_cuda( - bw6_761::scalar_t* vec_a, bw6_761::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bw6_761::scalar_t* result); - -extern "C" cudaError_t bw6_761_accumulate_cuda( - bw6_761::scalar_t* vec_a, bw6_761::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t bw6_761_sub_cuda( - bw6_761::scalar_t* vec_a, bw6_761::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, bw6_761::scalar_t* result); - -extern "C" cudaError_t bw6_761_transpose_matrix_cuda( - const bw6_761::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - bw6_761::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t bw6_761_bit_reverse_cuda( - const bw6_761::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, bw6_761::scalar_t* output); - - -extern "C" void bw6_761_generate_scalars(bw6_761::scalar_t* scalars, int size); - -extern "C" cudaError_t bw6_761_scalar_convert_montgomery( - bw6_761::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t bw6_761_initialize_domain( - bw6_761::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t bw6_761_ntt_cuda( - const bw6_761::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, bw6_761::scalar_t* output); - -extern "C" cudaError_t bw6_761_release_domain(device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/grumpkin.h b/icicle/include/api/grumpkin.h deleted file mode 100644 index 40241e591..000000000 --- a/icicle/include/api/grumpkin.h +++ /dev/null @@ -1,119 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef GRUMPKIN_API_H -#define GRUMPKIN_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "curves/params/grumpkin.cuh" -#include "msm/msm.cuh" -#include "vec_ops/vec_ops.cuh" -#include "poseidon/poseidon.cuh" - -extern "C" cudaError_t grumpkin_precompute_msm_bases_cuda( - grumpkin::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - grumpkin::affine_t* output_bases); - -extern "C" cudaError_t grumpkin_msm_cuda( - const grumpkin::scalar_t* scalars, const grumpkin::affine_t* points, int msm_size, msm::MSMConfig& config, grumpkin::projective_t* out); - -extern "C" bool grumpkin_eq(grumpkin::projective_t* point1, grumpkin::projective_t* point2); - -extern "C" void grumpkin_to_affine(grumpkin::projective_t* point, grumpkin::affine_t* point_out); - -extern "C" void grumpkin_generate_projective_points(grumpkin::projective_t* points, int size); - -extern "C" void grumpkin_generate_affine_points(grumpkin::affine_t* points, int size); - -extern "C" cudaError_t grumpkin_affine_convert_montgomery( - grumpkin::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t grumpkin_projective_convert_montgomery( - grumpkin::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t grumpkin_build_merkle_tree( - const grumpkin::scalar_t* leaves, - grumpkin::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t grumpkin_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - grumpkin::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t grumpkin_poseidon_create_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const grumpkin::scalar_t* round_constants, - const grumpkin::scalar_t* mds_matrix, - const grumpkin::scalar_t* non_sparse_matrix, - const grumpkin::scalar_t* sparse_matrices, - const grumpkin::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t grumpkin_poseidon_load_cuda( - poseidon::Poseidon** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t grumpkin_poseidon_hash_many_cuda( - const poseidon::Poseidon* poseidon, - const grumpkin::scalar_t* inputs, - grumpkin::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - grumpkin_poseidon_delete_cuda(poseidon::Poseidon* poseidon); - -extern "C" cudaError_t grumpkin_mul_cuda( - grumpkin::scalar_t* vec_a, grumpkin::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, grumpkin::scalar_t* result); - -extern "C" cudaError_t grumpkin_add_cuda( - grumpkin::scalar_t* vec_a, grumpkin::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, grumpkin::scalar_t* result); - -extern "C" cudaError_t grumpkin_accumulate_cuda( - grumpkin::scalar_t* vec_a, grumpkin::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t grumpkin_sub_cuda( - grumpkin::scalar_t* vec_a, grumpkin::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, grumpkin::scalar_t* result); - -extern "C" cudaError_t grumpkin_transpose_matrix_cuda( - const grumpkin::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - grumpkin::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t grumpkin_bit_reverse_cuda( - const grumpkin::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, grumpkin::scalar_t* output); - - -extern "C" void grumpkin_generate_scalars(grumpkin::scalar_t* scalars, int size); - -extern "C" cudaError_t grumpkin_scalar_convert_montgomery( - grumpkin::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/hash.h b/icicle/include/api/hash.h deleted file mode 100644 index ffa40d812..000000000 --- a/icicle/include/api/hash.h +++ /dev/null @@ -1,30 +0,0 @@ -#pragma once - -#ifndef HASH_API_H -#define HASH_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "hash/keccak/keccak.cuh" -#include "merkle-tree/merkle.cuh" - -extern "C" cudaError_t - keccak256_cuda(uint8_t* input, int input_block_size, int number_of_blocks, uint8_t* output, keccak::HashConfig& config); - -extern "C" cudaError_t - keccak512_cuda(uint8_t* input, int input_block_size, int number_of_blocks, uint8_t* output, keccak::HashConfig& config); - -extern "C" cudaError_t build_keccak256_merkle_tree_cuda( - const uint8_t* leaves, - uint64_t* digests, - unsigned int height, - unsigned int input_block_len, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t build_keccak512_merkle_tree_cuda( - const uint8_t* leaves, - uint64_t* digests, - unsigned int height, - unsigned int input_block_len, - const merkle_tree::TreeBuilderConfig& tree_config); -#endif \ No newline at end of file diff --git a/icicle/include/api/m31.h b/icicle/include/api/m31.h deleted file mode 100644 index 277268a1e..000000000 --- a/icicle/include/api/m31.h +++ /dev/null @@ -1,94 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef M31_API_H -#define M31_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "fields/stark_fields/m31.cuh" -#include "vec_ops/vec_ops.cuh" - -extern "C" cudaError_t m31_build_merkle_tree( - const m31::scalar_t* leaves, - m31::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t m31_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - m31::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t m31_mul_cuda( - m31::scalar_t* vec_a, m31::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::scalar_t* result); - -extern "C" cudaError_t m31_add_cuda( - m31::scalar_t* vec_a, m31::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::scalar_t* result); - -extern "C" cudaError_t m31_accumulate_cuda( - m31::scalar_t* vec_a, m31::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t m31_sub_cuda( - m31::scalar_t* vec_a, m31::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::scalar_t* result); - -extern "C" cudaError_t m31_transpose_matrix_cuda( - const m31::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - m31::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t m31_bit_reverse_cuda( - const m31::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, m31::scalar_t* output); - - -extern "C" void m31_generate_scalars(m31::scalar_t* scalars, int size); - -extern "C" cudaError_t m31_scalar_convert_montgomery( - m31::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" void m31_extension_generate_scalars(m31::extension_t* scalars, int size); - -extern "C" cudaError_t m31_extension_scalar_convert_montgomery( - m31::extension_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t m31_extension_mul_cuda( - m31::extension_t* vec_a, m31::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::extension_t* result); - -extern "C" cudaError_t m31_extension_add_cuda( - m31::extension_t* vec_a, m31::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::extension_t* result); - -extern "C" cudaError_t m31_extension_accumulate_cuda( - m31::extension_t* vec_a, m31::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t m31_extension_sub_cuda( - m31::extension_t* vec_a, m31::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, m31::extension_t* result); - -extern "C" cudaError_t m31_extension_transpose_matrix_cuda( - const m31::extension_t* input, - uint32_t row_size, - uint32_t column_size, - m31::extension_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t m31_extension_bit_reverse_cuda( - const m31::extension_t* input, uint64_t n, vec_ops::BitReverseConfig& config, m31::extension_t* output); - - -#endif \ No newline at end of file diff --git a/icicle/include/api/stark252.h b/icicle/include/api/stark252.h deleted file mode 100644 index 867f98e69..000000000 --- a/icicle/include/api/stark252.h +++ /dev/null @@ -1,73 +0,0 @@ -// WARNING: This file is auto-generated by a script. -// Any changes made to this file may be overwritten. -// Please modify the code generation script instead. -// Path to the code generation script: scripts/gen_c_api.py - -#pragma once -#ifndef STARK252_API_H -#define STARK252_API_H - -#include -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "fields/stark_fields/stark252.cuh" -#include "ntt/ntt.cuh" -#include "vec_ops/vec_ops.cuh" - -extern "C" cudaError_t stark252_build_merkle_tree( - const stark252::scalar_t* leaves, - stark252::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t stark252_mmcs_commit_cuda( - const matrix::Matrix* leaves, - unsigned int number_of_inputs, - stark252::scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const merkle_tree::TreeBuilderConfig& tree_config); - -extern "C" cudaError_t stark252_mul_cuda( - stark252::scalar_t* vec_a, stark252::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, stark252::scalar_t* result); - -extern "C" cudaError_t stark252_add_cuda( - stark252::scalar_t* vec_a, stark252::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, stark252::scalar_t* result); - -extern "C" cudaError_t stark252_accumulate_cuda( - stark252::scalar_t* vec_a, stark252::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t stark252_sub_cuda( - stark252::scalar_t* vec_a, stark252::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, stark252::scalar_t* result); - -extern "C" cudaError_t stark252_transpose_matrix_cuda( - const stark252::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - stark252::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t stark252_bit_reverse_cuda( - const stark252::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, stark252::scalar_t* output); - - -extern "C" void stark252_generate_scalars(stark252::scalar_t* scalars, int size); - -extern "C" cudaError_t stark252_scalar_convert_montgomery( - stark252::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); - -extern "C" cudaError_t stark252_initialize_domain( - stark252::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t stark252_ntt_cuda( - const stark252::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig& config, stark252::scalar_t* output); - -extern "C" cudaError_t stark252_release_domain(device_context::DeviceContext& ctx); - -#endif \ No newline at end of file diff --git a/icicle/include/api/templates/curves/ecntt.h b/icicle/include/api/templates/curves/ecntt.h deleted file mode 100644 index d155d7eae..000000000 --- a/icicle/include/api/templates/curves/ecntt.h +++ /dev/null @@ -1,2 +0,0 @@ -extern "C" cudaError_t ${CURVE}_ecntt_cuda( - const ${CURVE}::projective_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig<${CURVE}::scalar_t>& config, ${CURVE}::projective_t* output); \ No newline at end of file diff --git a/icicle/include/api/templates/curves/msm.h b/icicle/include/api/templates/curves/msm.h deleted file mode 100644 index 8c89a3aa2..000000000 --- a/icicle/include/api/templates/curves/msm.h +++ /dev/null @@ -1,8 +0,0 @@ -extern "C" cudaError_t ${CURVE}_precompute_msm_bases_cuda( - ${CURVE}::affine_t* bases, - int msm_size, - msm::MSMConfig& config, - ${CURVE}::affine_t* output_bases); - -extern "C" cudaError_t ${CURVE}_msm_cuda( - const ${CURVE}::scalar_t* scalars, const ${CURVE}::affine_t* points, int msm_size, msm::MSMConfig& config, ${CURVE}::projective_t* out); \ No newline at end of file diff --git a/icicle/include/api/templates/curves/msm_g2.h b/icicle/include/api/templates/curves/msm_g2.h deleted file mode 100644 index dd76a6c34..000000000 --- a/icicle/include/api/templates/curves/msm_g2.h +++ /dev/null @@ -1,8 +0,0 @@ -extern "C" cudaError_t ${CURVE}_g2_precompute_msm_bases_cuda( - ${CURVE}::g2_affine_t* bases, - int msm_size, - msm::MSMConfig& config, - ${CURVE}::g2_affine_t* output_bases); - -extern "C" cudaError_t ${CURVE}_g2_msm_cuda( - const ${CURVE}::scalar_t* scalars, const ${CURVE}::g2_affine_t* points, int msm_size, msm::MSMConfig& config, ${CURVE}::g2_projective_t* out); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/field.h b/icicle/include/api/templates/fields/field.h deleted file mode 100644 index b02e2386c..000000000 --- a/icicle/include/api/templates/fields/field.h +++ /dev/null @@ -1,4 +0,0 @@ -extern "C" void ${FIELD}_generate_scalars(${FIELD}::scalar_t* scalars, int size); - -extern "C" cudaError_t ${FIELD}_scalar_convert_montgomery( - ${FIELD}::scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/field_ext.h b/icicle/include/api/templates/fields/field_ext.h deleted file mode 100644 index fa93fb1c8..000000000 --- a/icicle/include/api/templates/fields/field_ext.h +++ /dev/null @@ -1,4 +0,0 @@ -extern "C" void ${FIELD}_extension_generate_scalars(${FIELD}::extension_t* scalars, int size); - -extern "C" cudaError_t ${FIELD}_extension_scalar_convert_montgomery( - ${FIELD}::extension_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/ntt.h b/icicle/include/api/templates/fields/ntt.h deleted file mode 100644 index 07b096f43..000000000 --- a/icicle/include/api/templates/fields/ntt.h +++ /dev/null @@ -1,7 +0,0 @@ -extern "C" cudaError_t ${FIELD}_initialize_domain( - ${FIELD}::scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode); - -extern "C" cudaError_t ${FIELD}_ntt_cuda( - const ${FIELD}::scalar_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig<${FIELD}::scalar_t>& config, ${FIELD}::scalar_t* output); - -extern "C" cudaError_t ${FIELD}_release_domain(device_context::DeviceContext& ctx); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/ntt_ext.h b/icicle/include/api/templates/fields/ntt_ext.h deleted file mode 100644 index 745431405..000000000 --- a/icicle/include/api/templates/fields/ntt_ext.h +++ /dev/null @@ -1,2 +0,0 @@ -extern "C" cudaError_t ${FIELD}_extension_ntt_cuda( - const ${FIELD}::extension_t* input, int size, ntt::NTTDir dir, ntt::NTTConfig<${FIELD}::scalar_t>& config, ${FIELD}::extension_t* output); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/poseidon.h b/icicle/include/api/templates/fields/poseidon.h deleted file mode 100644 index f8ea1ae4e..000000000 --- a/icicle/include/api/templates/fields/poseidon.h +++ /dev/null @@ -1,29 +0,0 @@ -extern "C" cudaError_t ${FIELD}_poseidon_create_cuda( - poseidon::Poseidon<${FIELD}::scalar_t>** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const ${FIELD}::scalar_t* round_constants, - const ${FIELD}::scalar_t* mds_matrix, - const ${FIELD}::scalar_t* non_sparse_matrix, - const ${FIELD}::scalar_t* sparse_matrices, - const ${FIELD}::scalar_t domain_tag, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t ${FIELD}_poseidon_load_cuda( - poseidon::Poseidon<${FIELD}::scalar_t>** poseidon, - unsigned int arity, - device_context::DeviceContext& ctx); - -extern "C" cudaError_t ${FIELD}_poseidon_hash_many_cuda( - const poseidon::Poseidon<${FIELD}::scalar_t>* poseidon, - const ${FIELD}::scalar_t* inputs, - ${FIELD}::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - ${FIELD}_poseidon_delete_cuda(poseidon::Poseidon<${FIELD}::scalar_t>* poseidon); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/poseidon2.h b/icicle/include/api/templates/fields/poseidon2.h deleted file mode 100644 index a6d5ea15f..000000000 --- a/icicle/include/api/templates/fields/poseidon2.h +++ /dev/null @@ -1,34 +0,0 @@ -extern "C" cudaError_t ${FIELD}_poseidon2_create_cuda( - poseidon2::Poseidon2<${FIELD}::scalar_t>** poseidon, - unsigned int width, - unsigned int rate, - unsigned int alpha, - unsigned int internal_rounds, - unsigned int external_rounds, - const ${FIELD}::scalar_t* round_constants, - const ${FIELD}::scalar_t* internal_matrix_diag, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t ${FIELD}_poseidon2_load_cuda( - poseidon2::Poseidon2<${FIELD}::scalar_t>** poseidon, - unsigned int width, - unsigned int rate, - poseidon2::MdsType mds_type, - poseidon2::DiffusionStrategy diffusion, - device_context::DeviceContext& ctx -); - -extern "C" cudaError_t ${FIELD}_poseidon2_hash_many_cuda( - const poseidon2::Poseidon2<${FIELD}::scalar_t>* poseidon, - const ${FIELD}::scalar_t* inputs, - ${FIELD}::scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg); - -extern "C" cudaError_t - ${FIELD}_poseidon2_delete_cuda(poseidon2::Poseidon2<${FIELD}::scalar_t>* poseidon, device_context::DeviceContext& ctx); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/tree.h b/icicle/include/api/templates/fields/tree.h deleted file mode 100644 index 06f102975..000000000 --- a/icicle/include/api/templates/fields/tree.h +++ /dev/null @@ -1,16 +0,0 @@ -extern "C" cudaError_t ${FIELD}_build_merkle_tree( - const ${FIELD}::scalar_t* leaves, - ${FIELD}::scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher<${FIELD}::scalar_t, ${FIELD}::scalar_t>* compression, - const hash::Hasher<${FIELD}::scalar_t, ${FIELD}::scalar_t>* bottom_layer, - const merkle_tree::TreeBuilderConfig& tree_config); - - extern "C" cudaError_t ${FIELD}_mmcs_commit_cuda( - const matrix::Matrix<${FIELD}::scalar_t>* leaves, - unsigned int number_of_inputs, - ${FIELD}::scalar_t* digests, - const hash::Hasher<${FIELD}::scalar_t, ${FIELD}::scalar_t>* hasher, - const hash::Hasher<${FIELD}::scalar_t, ${FIELD}::scalar_t>* compression, - const merkle_tree::TreeBuilderConfig& tree_config); \ No newline at end of file diff --git a/icicle/include/api/templates/fields/vec_ops.h b/icicle/include/api/templates/fields/vec_ops.h deleted file mode 100644 index 7fe1c9bef..000000000 --- a/icicle/include/api/templates/fields/vec_ops.h +++ /dev/null @@ -1,23 +0,0 @@ -extern "C" cudaError_t ${FIELD}_mul_cuda( - ${FIELD}::scalar_t* vec_a, ${FIELD}::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::scalar_t* result); - -extern "C" cudaError_t ${FIELD}_add_cuda( - ${FIELD}::scalar_t* vec_a, ${FIELD}::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::scalar_t* result); - -extern "C" cudaError_t ${FIELD}_accumulate_cuda( - ${FIELD}::scalar_t* vec_a, ${FIELD}::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t ${FIELD}_sub_cuda( - ${FIELD}::scalar_t* vec_a, ${FIELD}::scalar_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::scalar_t* result); - -extern "C" cudaError_t ${FIELD}_transpose_matrix_cuda( - const ${FIELD}::scalar_t* input, - uint32_t row_size, - uint32_t column_size, - ${FIELD}::scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t ${FIELD}_bit_reverse_cuda( - const ${FIELD}::scalar_t* input, uint64_t n, vec_ops::BitReverseConfig& config, ${FIELD}::scalar_t* output); diff --git a/icicle/include/api/templates/fields/vec_ops_ext.h b/icicle/include/api/templates/fields/vec_ops_ext.h deleted file mode 100644 index 0266e9cc7..000000000 --- a/icicle/include/api/templates/fields/vec_ops_ext.h +++ /dev/null @@ -1,23 +0,0 @@ -extern "C" cudaError_t ${FIELD}_extension_mul_cuda( - ${FIELD}::extension_t* vec_a, ${FIELD}::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::extension_t* result); - -extern "C" cudaError_t ${FIELD}_extension_add_cuda( - ${FIELD}::extension_t* vec_a, ${FIELD}::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::extension_t* result); - -extern "C" cudaError_t ${FIELD}_extension_accumulate_cuda( - ${FIELD}::extension_t* vec_a, ${FIELD}::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config); - -extern "C" cudaError_t ${FIELD}_extension_sub_cuda( - ${FIELD}::extension_t* vec_a, ${FIELD}::extension_t* vec_b, int n, vec_ops::VecOpsConfig& config, ${FIELD}::extension_t* result); - -extern "C" cudaError_t ${FIELD}_extension_transpose_matrix_cuda( - const ${FIELD}::extension_t* input, - uint32_t row_size, - uint32_t column_size, - ${FIELD}::extension_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - -extern "C" cudaError_t ${FIELD}_extension_bit_reverse_cuda( - const ${FIELD}::extension_t* input, uint64_t n, vec_ops::BitReverseConfig& config, ${FIELD}::extension_t* output); diff --git a/icicle/include/curves/macro.h b/icicle/include/curves/macro.h deleted file mode 100644 index 6ce3cb664..000000000 --- a/icicle/include/curves/macro.h +++ /dev/null @@ -1,42 +0,0 @@ -#pragma once -#ifndef CURVE_MACRO_H -#define CURVE_MACRO_H - -#define CURVE_DEFINITIONS \ - /** \ - * Base field of G1 curve. Is always a prime field. \ - */ \ - typedef Field point_field_t; \ - \ - static constexpr point_field_t generator_x = point_field_t{g1_gen_x}; \ - static constexpr point_field_t generator_y = point_field_t{g1_gen_y}; \ - static constexpr point_field_t b = point_field_t{weierstrass_b}; \ - /** \ - * [Projective representation](https://hyperelliptic.org/EFD/g1p/auto-shortw-projective.html) \ - * of G1 curve consisting of three coordinates of type [point_field_t](point_field_t). \ - */ \ - typedef Projective projective_t; \ - /** \ - * Affine representation of G1 curve consisting of two coordinates of type [point_field_t](point_field_t). \ - */ \ - typedef Affine affine_t; - -#define G2_CURVE_DEFINITIONS \ - typedef ExtensionField g2_point_field_t; \ - static constexpr g2_point_field_t g2_generator_x = \ - g2_point_field_t{point_field_t{g2_gen_x_re}, point_field_t{g2_gen_x_im}}; \ - static constexpr g2_point_field_t g2_generator_y = \ - g2_point_field_t{point_field_t{g2_gen_y_re}, point_field_t{g2_gen_y_im}}; \ - static constexpr g2_point_field_t g2_b = \ - g2_point_field_t{point_field_t{weierstrass_b_g2_re}, point_field_t{weierstrass_b_g2_im}}; \ - \ - /** \ - * [Projective representation](https://hyperelliptic.org/EFD/g1p/auto-shortw-projective.html) of G2 curve. \ - */ \ - typedef Projective g2_projective_t; \ - /** \ - * Affine representation of G1 curve. \ - */ \ - typedef Affine g2_affine_t; - -#endif \ No newline at end of file diff --git a/icicle/include/curves/params/bls12_377.cuh b/icicle/include/curves/params/bls12_377.cuh deleted file mode 100644 index 3e81fb176..000000000 --- a/icicle/include/curves/params/bls12_377.cuh +++ /dev/null @@ -1,48 +0,0 @@ -#pragma once -#ifndef BLS12_377_PARAMS_H -#define BLS12_377_PARAMS_H - -#include "fields/storage.cuh" - -#include "curves/macro.h" -#include "curves/projective.cuh" -#include "fields/snark_fields/bls12_377_base.cuh" -#include "fields/snark_fields/bls12_377_scalar.cuh" -#include "fields/quadratic_extension.cuh" - -namespace bls12_377 { - // G1 and G2 generators - static constexpr storage g1_gen_x = {0xb21be9ef, 0xeab9b16e, 0xffcd394e, 0xd5481512, - 0xbd37cb5c, 0x188282c8, 0xaa9d41bb, 0x85951e2c, - 0xbf87ff54, 0xc8fc6225, 0xfe740a67, 0x008848de}; - static constexpr storage g1_gen_y = {0x559c8ea6, 0xfd82de55, 0x34a9591a, 0xc2fe3d36, - 0x4fb82305, 0x6d182ad4, 0xca3e52d9, 0xbd7fb348, - 0x30afeec4, 0x1f674f5d, 0xc5102eff, 0x01914a69}; - static constexpr storage g2_gen_x_re = {0x7c005196, 0x74e3e48f, 0xbb535402, 0x71889f52, - 0x57db6b9b, 0x7ea501f5, 0x203e5031, 0xc565f071, - 0xa3841d01, 0xc89630a2, 0x71c785fe, 0x018480be}; - static constexpr storage g2_gen_x_im = {0x6ea16afe, 0xb26bfefa, 0xbff76fe6, 0x5cf89984, - 0x0799c9de, 0xe7223ece, 0x6651cecb, 0x532777ee, - 0xb1b140d5, 0x70dc5a51, 0xe7004031, 0x00ea6040}; - static constexpr storage g2_gen_y_re = {0x09fd4ddf, 0xf0940944, 0x6d8c7c2e, 0xf2cf8888, - 0xf832d204, 0xe458c282, 0x74b49a58, 0xde03ed72, - 0xcbb2efb4, 0xd960736b, 0x5d446f7b, 0x00690d66}; - static constexpr storage g2_gen_y_im = {0x85eb8f93, 0xd9a1cdd1, 0x5e52270b, 0x4279b83f, - 0xcee304c2, 0x2463b01a, 0x3d591bf1, 0x61ef11ac, - 0x151a70aa, 0x9e549da3, 0xd2835518, 0x00f8169f}; - - static constexpr storage weierstrass_b = {0x00000001, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage weierstrass_b_g2_re = { - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage weierstrass_b_g2_im = { - 0x9999999a, 0x1c9ed999, 0x1ccccccd, 0x0dd39e5c, 0x3c6bf800, 0x129207b6, - 0xcd5fd889, 0xdc7b4f91, 0x7460c589, 0x43bd0373, 0xdb0fd6f3, 0x010222f6}; - - CURVE_DEFINITIONS - G2_CURVE_DEFINITIONS -} // namespace bls12_377 - -#endif diff --git a/icicle/include/curves/params/bls12_381.cuh b/icicle/include/curves/params/bls12_381.cuh deleted file mode 100644 index 86c7ef6a9..000000000 --- a/icicle/include/curves/params/bls12_381.cuh +++ /dev/null @@ -1,48 +0,0 @@ -#pragma once -#ifndef BLS12_381_PARAMS_H -#define BLS12_381_PARAMS_H - -#include "fields/storage.cuh" - -#include "curves/macro.h" -#include "curves/projective.cuh" -#include "fields/snark_fields/bls12_381_base.cuh" -#include "fields/snark_fields/bls12_381_scalar.cuh" -#include "fields/quadratic_extension.cuh" - -namespace bls12_381 { - // G1 and G2 generators - static constexpr storage g1_gen_x = {0xdb22c6bb, 0xfb3af00a, 0xf97a1aef, 0x6c55e83f, - 0x171bac58, 0xa14e3a3f, 0x9774b905, 0xc3688c4f, - 0x4fa9ac0f, 0x2695638c, 0x3197d794, 0x17f1d3a7}; - static constexpr storage g1_gen_y = {0x46c5e7e1, 0x0caa2329, 0xa2888ae4, 0xd03cc744, - 0x2c04b3ed, 0x00db18cb, 0xd5d00af6, 0xfcf5e095, - 0x741d8ae4, 0xa09e30ed, 0xe3aaa0f1, 0x08b3f481}; - static constexpr storage g2_gen_x_re = {0xc121bdb8, 0xd48056c8, 0xa805bbef, 0x0bac0326, - 0x7ae3d177, 0xb4510b64, 0xfa403b02, 0xc6e47ad4, - 0x2dc51051, 0x26080527, 0xf08f0a91, 0x024aa2b2}; - static constexpr storage g2_gen_x_im = {0x5d042b7e, 0xe5ac7d05, 0x13945d57, 0x334cf112, - 0xdc7f5049, 0xb5da61bb, 0x9920b61a, 0x596bd0d0, - 0x88274f65, 0x7dacd3a0, 0x52719f60, 0x13e02b60}; - static constexpr storage g2_gen_y_re = {0x08b82801, 0xe1935486, 0x3baca289, 0x923ac9cc, - 0x5160d12c, 0x6d429a69, 0x8cbdd3a7, 0xadfd9baa, - 0xda2e351a, 0x8cc9cdc6, 0x727d6e11, 0x0ce5d527}; - static constexpr storage g2_gen_y_im = {0xf05f79be, 0xaaa9075f, 0x5cec1da1, 0x3f370d27, - 0x572e99ab, 0x267492ab, 0x85a763af, 0xcb3e287e, - 0x2bc28b99, 0x32acd2b0, 0x2ea734cc, 0x0606c4a0}; - - static constexpr storage weierstrass_b = {0x00000004, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage weierstrass_b_g2_re = { - 0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage weierstrass_b_g2_im = { - 0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - - CURVE_DEFINITIONS - G2_CURVE_DEFINITIONS -} // namespace bls12_381 - -#endif diff --git a/icicle/include/curves/params/bn254.cuh b/icicle/include/curves/params/bn254.cuh deleted file mode 100644 index 4ae43760f..000000000 --- a/icicle/include/curves/params/bn254.cuh +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once -#ifndef BN254_PARAMS_H -#define BN254_PARAMS_H - -#include "fields/storage.cuh" - -#include "curves/macro.h" -#include "curves/projective.cuh" -#include "fields/snark_fields/bn254_base.cuh" -#include "fields/snark_fields/bn254_scalar.cuh" -#include "fields/quadratic_extension.cuh" - -namespace bn254 { - // G1 and G2 generators - static constexpr storage g1_gen_x = {0x00000001, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage g1_gen_y = {0x00000002, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage g2_gen_x_re = {0xd992f6ed, 0x46debd5c, 0xf75edadd, 0x674322d4, - 0x5e5c4479, 0x426a0066, 0x121f1e76, 0x1800deef}; - static constexpr storage g2_gen_x_im = {0xaef312c2, 0x97e485b7, 0x35a9e712, 0xf1aa4933, - 0x31fb5d25, 0x7260bfb7, 0x920d483a, 0x198e9393}; - static constexpr storage g2_gen_y_re = {0x66fa7daa, 0x4ce6cc01, 0x0c43d37b, 0xe3d1e769, - 0x8dcb408f, 0x4aab7180, 0xdb8c6deb, 0x12c85ea5}; - static constexpr storage g2_gen_y_im = {0xd122975b, 0x55acdadc, 0x70b38ef3, 0xbc4b3133, - 0x690c3395, 0xec9e99ad, 0x585ff075, 0x090689d0}; - - static constexpr storage weierstrass_b = {0x00000003, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage weierstrass_b_g2_re = { - 0x24a138e5, 0x3267e6dc, 0x59dbefa3, 0xb5b4c5e5, 0x1be06ac3, 0x81be1899, 0xceb8aaae, 0x2b149d40}; - static constexpr storage weierstrass_b_g2_im = { - 0x85c315d2, 0xe4a2bd06, 0xe52d1852, 0xa74fa084, 0xeed8fdf4, 0xcd2cafad, 0x3af0fed4, 0x009713b0}; - - CURVE_DEFINITIONS - G2_CURVE_DEFINITIONS -} // namespace bn254 - -#endif diff --git a/icicle/include/curves/params/bw6_761.cuh b/icicle/include/curves/params/bw6_761.cuh deleted file mode 100644 index ce649f5e9..000000000 --- a/icicle/include/curves/params/bw6_761.cuh +++ /dev/null @@ -1,58 +0,0 @@ -#pragma once -#ifndef BW6_761_PARAMS_H -#define BW6_761_PARAMS_H - -#include "fields/storage.cuh" - -#include "curves/macro.h" -#include "curves/projective.cuh" -#include "fields/snark_fields/bw6_761_base.cuh" -#include "fields/snark_fields/bw6_761_scalar.cuh" -#include "fields/quadratic_extension.cuh" - -namespace bw6_761 { - // G1 and G2 generators - static constexpr storage g1_gen_x = { - 0x66e5b43d, 0x4088f3af, 0xa6af603f, 0x055928ac, 0x56133e82, 0x6750dd03, 0x280ca27f, 0x03758f9a, - 0xc9ea0971, 0x5bd71fa0, 0x47729b90, 0xa17a54ce, 0x94c2e746, 0x11dbfcd2, 0xc15520ac, 0x79017ffa, - 0x85f56fc7, 0xee05c54b, 0x551b27f0, 0xe6a0cfb7, 0xa477beae, 0xb277ce98, 0x0ea190c8, 0x01075b02}; - static constexpr storage g1_gen_y = { - 0xb4e95363, 0xbafc8f2d, 0x0b20d2a1, 0xad1cb2be, 0xcad0fb93, 0xb2b08119, 0xb3053253, 0x9f9df141, - 0x6fc2cdd4, 0xbe3fb90b, 0x717a4c55, 0xcc685d31, 0x71b5b806, 0xc5b8fa17, 0xaf7e0dba, 0x265909f1, - 0xa2e573a3, 0x1a7348d2, 0x884c9ec6, 0x0f952589, 0x45cc2a42, 0xe6fd637b, 0x0a6fc574, 0x0058b84e}; - static constexpr storage g2_gen_x = { - 0xcd025f1c, 0xa830c194, 0xe1bf995b, 0x6410cf4f, 0xc2ad54b0, 0x00e96efb, 0x3cd208d7, 0xce6948cb, - 0x00e1b6ba, 0x963317a3, 0xac70e7c7, 0xc5bbcae9, 0xf09feb58, 0x734ec3f1, 0xab3da268, 0x26b41c5d, - 0x13890f6d, 0x4c062010, 0xc5a7115f, 0xd61053aa, 0x69d660f9, 0xc852a82e, 0x41d9b816, 0x01101332}; - static constexpr storage g2_gen_y = { - 0x28c73b61, 0xeb70a167, 0xf9eac689, 0x91ec0594, 0x3c5a02a5, 0x58aa2d3a, 0x504affc7, 0x3ea96fcd, - 0xffa82300, 0x8906c170, 0xd2c712b8, 0x64f293db, 0x33293fef, 0x94c97eb7, 0x0b95a59c, 0x0a1d86c8, - 0x53ffe316, 0x81a78e27, 0xcec2181c, 0x26b7cf9a, 0xe4b6d2dc, 0x8179eb10, 0x7761369f, 0x0017c335}; - - static constexpr storage weierstrass_b = { - 0x0000008a, 0xf49d0000, 0x70000082, 0xe6913e68, 0xeaf0a437, 0x160cf8ae, 0x5667a8f8, 0x98a116c2, - 0x73ebff2e, 0x71dcd3dc, 0x12f9fd90, 0x8689c8ed, 0x25b42304, 0x03cebaff, 0xe584e919, 0x707ba638, - 0x8087be41, 0x528275ef, 0x81d14688, 0xb926186a, 0x04faff3e, 0xd187c940, 0xfb83ce0a, 0x0122e824}; - static constexpr storage g2_weierstrass_b = { - 0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - - CURVE_DEFINITIONS - - typedef point_field_t g2_point_field_t; - static constexpr g2_point_field_t g2_generator_x = g2_point_field_t{g2_gen_x}; - static constexpr g2_point_field_t g2_generator_y = g2_point_field_t{g2_gen_y}; - static constexpr g2_point_field_t g2_b = g2_point_field_t{g2_weierstrass_b}; - - /** - * [Projective representation](https://hyperelliptic.org/EFD/g1p/auto-shortw-projective.html) of G2 curve. - */ - typedef Projective g2_projective_t; - /** - * Affine representation of G1 curve. - */ - typedef Affine g2_affine_t; -} // namespace bw6_761 - -#endif diff --git a/icicle/include/curves/params/grumpkin.cuh b/icicle/include/curves/params/grumpkin.cuh deleted file mode 100644 index 855897a17..000000000 --- a/icicle/include/curves/params/grumpkin.cuh +++ /dev/null @@ -1,26 +0,0 @@ -#pragma once -#ifndef GRUMPKIN_PARAMS_H -#define GRUMPKIN_PARAMS_H - -#include "fields/storage.cuh" - -#include "curves/macro.h" -#include "curves/projective.cuh" -#include "fields/snark_fields/grumpkin_base.cuh" -#include "fields/snark_fields/grumpkin_scalar.cuh" - -namespace grumpkin { - typedef bn254::fp_config fq_config; - // G1 generator - static constexpr storage g1_gen_x = {0x00000001, 0x00000000, 0x00000000, 0x00000000, - 0x00000000, 0x00000000, 0x00000000, 0x00000000}; - static constexpr storage g1_gen_y = {0x823f272c, 0x833fc48d, 0xf1181294, 0x2d270d45, - 0x6a45d63, 0xcf135e75, 0x00000002, 0x00000000}; - - static constexpr storage weierstrass_b = {0xeffffff0, 0x43e1f593, 0x79b97091, 0x2833e848, - 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72}; - - CURVE_DEFINITIONS -} // namespace grumpkin - -#endif diff --git a/icicle/include/curves/projective.cuh b/icicle/include/curves/projective.cuh deleted file mode 100644 index 47ff27a83..000000000 --- a/icicle/include/curves/projective.cuh +++ /dev/null @@ -1,239 +0,0 @@ -#pragma once - -#include "affine.cuh" -#include "gpu-utils/sharedmem.cuh" - -template -class Projective -{ - friend Affine; - -public: - typedef Affine Aff; - typedef SCALAR_FF Scalar; - - static constexpr unsigned SCALAR_FF_NBITS = SCALAR_FF::NBITS; - static constexpr unsigned FF_NBITS = FF::NBITS; - - FF x; - FF y; - FF z; - - static HOST_DEVICE_INLINE Projective zero() { return {FF::zero(), FF::one(), FF::zero()}; } - - static HOST_DEVICE_INLINE Affine to_affine(const Projective& point) - { - FF denom = FF::inverse(point.z); - return {point.x * denom, point.y * denom}; - } - - static HOST_DEVICE_INLINE Projective from_affine(const Affine& point) - { - return point == Affine::zero() ? zero() : Projective{point.x, point.y, FF::one()}; - } - - static HOST_DEVICE_INLINE Projective to_montgomery(const Projective& point) - { - return {FF::to_montgomery(point.x), FF::to_montgomery(point.y), FF::to_montgomery(point.z)}; - } - - static HOST_DEVICE_INLINE Projective from_montgomery(const Projective& point) - { - return {FF::from_montgomery(point.x), FF::from_montgomery(point.y), FF::from_montgomery(point.z)}; - } - - static HOST_DEVICE_INLINE Projective generator() { return {GENERATOR_X, GENERATOR_Y, FF::one()}; } - - static HOST_DEVICE_INLINE Projective neg(const Projective& point) { return {point.x, FF::neg(point.y), point.z}; } - - static HOST_DEVICE_INLINE Projective dbl(const Projective& point) - { - const FF X = point.x; - const FF Y = point.y; - const FF Z = point.z; - - // TODO: Change to efficient dbl once implemented for field.cuh - FF t0 = FF::sqr(Y); // 1. t0 ← Y · Y - FF Z3 = t0 + t0; // 2. Z3 ← t0 + t0 - Z3 = Z3 + Z3; // 3. Z3 ← Z3 + Z3 - Z3 = Z3 + Z3; // 4. Z3 ← Z3 + Z3 - FF t1 = Y * Z; // 5. t1 ← Y · Z - FF t2 = FF::sqr(Z); // 6. t2 ← Z · Z - t2 = FF::template mul_unsigned<3>(FF::template mul_const(t2)); // 7. t2 ← b3 · t2 - FF X3 = t2 * Z3; // 8. X3 ← t2 · Z3 - FF Y3 = t0 + t2; // 9. Y3 ← t0 + t2 - Z3 = t1 * Z3; // 10. Z3 ← t1 · Z3 - t1 = t2 + t2; // 11. t1 ← t2 + t2 - t2 = t1 + t2; // 12. t2 ← t1 + t2 - t0 = t0 - t2; // 13. t0 ← t0 − t2 - Y3 = t0 * Y3; // 14. Y3 ← t0 · Y3 - Y3 = X3 + Y3; // 15. Y3 ← X3 + Y3 - t1 = X * Y; // 16. t1 ← X · Y - X3 = t0 * t1; // 17. X3 ← t0 · t1 - X3 = X3 + X3; // 18. X3 ← X3 + X3 - return {X3, Y3, Z3}; - } - - friend HOST_DEVICE_INLINE Projective operator+(Projective p1, const Projective& p2) - { - const FF X1 = p1.x; // < 2 - const FF Y1 = p1.y; // < 2 - const FF Z1 = p1.z; // < 2 - const FF X2 = p2.x; // < 2 - const FF Y2 = p2.y; // < 2 - const FF Z2 = p2.z; // < 2 - const FF t00 = X1 * X2; // t00 ← X1 · X2 < 2 - const FF t01 = Y1 * Y2; // t01 ← Y1 · Y2 < 2 - const FF t02 = Z1 * Z2; // t02 ← Z1 · Z2 < 2 - const FF t03 = X1 + Y1; // t03 ← X1 + Y1 < 4 - const FF t04 = X2 + Y2; // t04 ← X2 + Y2 < 4 - const FF t05 = t03 * t04; // t03 ← t03 · t04 < 3 - const FF t06 = t00 + t01; // t06 ← t00 + t01 < 4 - const FF t07 = t05 - t06; // t05 ← t05 − t06 < 2 - const FF t08 = Y1 + Z1; // t08 ← Y1 + Z1 < 4 - const FF t09 = Y2 + Z2; // t09 ← Y2 + Z2 < 4 - const FF t10 = t08 * t09; // t10 ← t08 · t09 < 3 - const FF t11 = t01 + t02; // t11 ← t01 + t02 < 4 - const FF t12 = t10 - t11; // t12 ← t10 − t11 < 2 - const FF t13 = X1 + Z1; // t13 ← X1 + Z1 < 4 - const FF t14 = X2 + Z2; // t14 ← X2 + Z2 < 4 - const FF t15 = t13 * t14; // t15 ← t13 · t14 < 3 - const FF t16 = t00 + t02; // t16 ← t00 + t02 < 4 - const FF t17 = t15 - t16; // t17 ← t15 − t16 < 2 - const FF t18 = t00 + t00; // t18 ← t00 + t00 < 2 - const FF t19 = t18 + t00; // t19 ← t18 + t00 < 2 - const FF t20 = FF::template mul_unsigned<3>(FF::template mul_const(t02)); // t20 ← b3 · t02 < 2 - const FF t21 = t01 + t20; // t21 ← t01 + t20 < 2 - const FF t22 = t01 - t20; // t22 ← t01 − t20 < 2 - const FF t23 = FF::template mul_unsigned<3>(FF::template mul_const(t17)); // t23 ← b3 · t17 < 2 - const auto t24 = FF::mul_wide(t12, t23); // t24 ← t12 · t23 < 2 - const auto t25 = FF::mul_wide(t07, t22); // t25 ← t07 · t22 < 2 - const FF X3 = FF::reduce(t25 - t24); // X3 ← t25 − t24 < 2 - const auto t27 = FF::mul_wide(t23, t19); // t27 ← t23 · t19 < 2 - const auto t28 = FF::mul_wide(t22, t21); // t28 ← t22 · t21 < 2 - const FF Y3 = FF::reduce(t28 + t27); // Y3 ← t28 + t27 < 2 - const auto t30 = FF::mul_wide(t19, t07); // t30 ← t19 · t07 < 2 - const auto t31 = FF::mul_wide(t21, t12); // t31 ← t21 · t12 < 2 - const FF Z3 = FF::reduce(t31 + t30); // Z3 ← t31 + t30 < 2 - return {X3, Y3, Z3}; - } - - friend HOST_DEVICE_INLINE Projective operator-(Projective p1, const Projective& p2) { return p1 + neg(p2); } - - friend HOST_DEVICE_INLINE Projective operator+(Projective p1, const Affine& p2) - { - const FF X1 = p1.x; // < 2 - const FF Y1 = p1.y; // < 2 - const FF Z1 = p1.z; // < 2 - const FF X2 = p2.x; // < 2 - const FF Y2 = p2.y; // < 2 - const FF t00 = X1 * X2; // t00 ← X1 · X2 < 2 - const FF t01 = Y1 * Y2; // t01 ← Y1 · Y2 < 2 - const FF t02 = Z1; // t02 ← Z1 < 2 - const FF t03 = X1 + Y1; // t03 ← X1 + Y1 < 4 - const FF t04 = X2 + Y2; // t04 ← X2 + Y2 < 4 - const FF t05 = t03 * t04; // t03 ← t03 · t04 < 3 - const FF t06 = t00 + t01; // t06 ← t00 + t01 < 4 - const FF t07 = t05 - t06; // t05 ← t05 − t06 < 2 - const FF t08 = Y1 + Z1; // t08 ← Y1 + Z1 < 4 - const FF t09 = Y2 + FF::one(); // t09 ← Y2 + 1 < 4 - const FF t10 = t08 * t09; // t10 ← t08 · t09 < 3 - const FF t11 = t01 + t02; // t11 ← t01 + t02 < 4 - const FF t12 = t10 - t11; // t12 ← t10 − t11 < 2 - const FF t13 = X1 + Z1; // t13 ← X1 + Z1 < 4 - const FF t14 = X2 + FF::one(); // t14 ← X2 + 1 < 4 - const FF t15 = t13 * t14; // t15 ← t13 · t14 < 3 - const FF t16 = t00 + t02; // t16 ← t00 + t02 < 4 - const FF t17 = t15 - t16; // t17 ← t15 − t16 < 2 - const FF t18 = t00 + t00; // t18 ← t00 + t00 < 2 - const FF t19 = t18 + t00; // t19 ← t18 + t00 < 2 - const FF t20 = FF::template mul_unsigned<3>(FF::template mul_const(t02)); // t20 ← b3 · t02 < 2 - const FF t21 = t01 + t20; // t21 ← t01 + t20 < 2 - const FF t22 = t01 - t20; // t22 ← t01 − t20 < 2 - const FF t23 = FF::template mul_unsigned<3>(FF::template mul_const(t17)); // t23 ← b3 · t17 < 2 - const auto t24 = FF::mul_wide(t12, t23); // t24 ← t12 · t23 < 2 - const auto t25 = FF::mul_wide(t07, t22); // t25 ← t07 · t22 < 2 - const FF X3 = FF::reduce(t25 - t24); // X3 ← t25 − t24 < 2 - const auto t27 = FF::mul_wide(t23, t19); // t27 ← t23 · t19 < 2 - const auto t28 = FF::mul_wide(t22, t21); // t28 ← t22 · t21 < 2 - const FF Y3 = FF::reduce(t28 + t27); // Y3 ← t28 + t27 < 2 - const auto t30 = FF::mul_wide(t19, t07); // t30 ← t19 · t07 < 2 - const auto t31 = FF::mul_wide(t21, t12); // t31 ← t21 · t12 < 2 - const FF Z3 = FF::reduce(t31 + t30); // Z3 ← t31 + t30 < 2 - return {X3, Y3, Z3}; - } - - friend HOST_DEVICE_INLINE Projective operator-(Projective p1, const Affine& p2) - { - return p1 + Affine::neg(p2); - } - - friend HOST_DEVICE_INLINE Projective operator*(SCALAR_FF scalar, const Projective& point) - { - Projective res = zero(); -#ifdef __CUDA_ARCH__ - UNROLL -#endif - for (int i = 0; i < SCALAR_FF::NBITS; i++) { - if (i > 0) { res = dbl(res); } - if (scalar.get_scalar_digit(SCALAR_FF::NBITS - i - 1, 1)) { res = res + point; } - } - return res; - } - - friend HOST_DEVICE_INLINE Projective operator*(const Projective& point, SCALAR_FF scalar) { return scalar * point; } - - friend HOST_DEVICE_INLINE bool operator==(const Projective& p1, const Projective& p2) - { - return (p1.x * p2.z == p2.x * p1.z) && (p1.y * p2.z == p2.y * p1.z); - } - - friend HOST_DEVICE_INLINE bool operator!=(const Projective& p1, const Projective& p2) { return !(p1 == p2); } - - friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Projective& point) - { - os << "Point { x: " << point.x << "; y: " << point.y << "; z: " << point.z << " }"; - return os; - } - - static HOST_DEVICE_INLINE bool is_zero(const Projective& point) - { - return point.x == FF::zero() && point.y != FF::zero() && point.z == FF::zero(); - } - - static HOST_DEVICE_INLINE bool is_on_curve(const Projective& point) - { - if (is_zero(point)) return true; - bool eq_holds = - (FF::template mul_const(FF::sqr(point.z) * point.z) + FF::sqr(point.x) * point.x == - point.z * FF::sqr(point.y)); - return point.z != FF::zero() && eq_holds; - } - - static HOST_INLINE Projective rand_host() - { - SCALAR_FF rand_scalar = SCALAR_FF::rand_host(); - return rand_scalar * generator(); - } - - static void rand_host_many(Projective* out, int size) - { - for (int i = 0; i < size; i++) - out[i] = (i % size < 100) ? rand_host() : out[i - 100]; - } - - static void rand_host_many_affine(Affine* out, int size) - { - for (int i = 0; i < size; i++) - out[i] = (i % size < 100) ? to_affine(rand_host()) : out[i - 100]; - } -}; - -template -struct SharedMemory> { - __device__ Projective* getPointer() - { - extern __shared__ Projective s_projective_[]; - return s_projective_; - } -}; diff --git a/icicle/include/fields/host_math.cuh b/icicle/include/fields/host_math.cuh deleted file mode 100644 index 0cdde0ca0..000000000 --- a/icicle/include/fields/host_math.cuh +++ /dev/null @@ -1,188 +0,0 @@ -#pragma once -#ifndef HOST_MATH_H -#define HOST_MATH_H - -#include -#include -#include "gpu-utils/modifiers.cuh" -#include "storage.cuh" - -namespace host_math { - - // return x + y with uint32_t operands - static constexpr __host__ uint32_t add(const uint32_t x, const uint32_t y) { return x + y; } - - // return x + y + carry with uint32_t operands - static constexpr __host__ uint32_t addc(const uint32_t x, const uint32_t y, const uint32_t carry) - { - return x + y + carry; - } - - // return x + y and carry out with uint32_t operands - static constexpr __host__ uint32_t add_cc(const uint32_t x, const uint32_t y, uint32_t& carry) - { - uint32_t result = x + y; - carry = x > result; - return result; - } - - // return x + y + carry and carry out with uint32_t operands - static constexpr __host__ uint32_t addc_cc(const uint32_t x, const uint32_t y, uint32_t& carry) - { - const uint32_t result = x + y + carry; - carry = carry && x >= result || !carry && x > result; - return result; - } - - // return x - y with uint32_t operands - static constexpr __host__ uint32_t sub(const uint32_t x, const uint32_t y) { return x - y; } - - // return x - y - borrow with uint32_t operands - static constexpr __host__ uint32_t subc(const uint32_t x, const uint32_t y, const uint32_t borrow) - { - return x - y - borrow; - } - - // return x - y and borrow out with uint32_t operands - static constexpr __host__ uint32_t sub_cc(const uint32_t x, const uint32_t y, uint32_t& borrow) - { - uint32_t result = x - y; - borrow = x < result; - return result; - } - - // return x - y - borrow and borrow out with uint32_t operands - static constexpr __host__ uint32_t subc_cc(const uint32_t x, const uint32_t y, uint32_t& borrow) - { - const uint32_t result = x - y - borrow; - borrow = borrow && x <= result || !borrow && x < result; - return result; - } - - // return x * y + z + carry and carry out with uint32_t operands - static constexpr __host__ uint32_t madc_cc(const uint32_t x, const uint32_t y, const uint32_t z, uint32_t& carry) - { - uint64_t r = static_cast(x) * y + z + carry; - carry = (uint32_t)(r >> 32); - uint32_t result = r & 0xffffffff; - return result; - } - - template - struct carry_chain { - unsigned index; - - constexpr HOST_INLINE carry_chain() : index(0) {} - - constexpr HOST_INLINE uint32_t add(const uint32_t x, const uint32_t y, uint32_t& carry) - { - index++; - if (index == 1 && OPS_COUNT == 1 && !CARRY_IN && !CARRY_OUT) - return host_math::add(x, y); - else if (index == 1 && !CARRY_IN) - return host_math::add_cc(x, y, carry); - else if (index < OPS_COUNT || CARRY_OUT) - return host_math::addc_cc(x, y, carry); - else - return host_math::addc(x, y, carry); - } - - constexpr HOST_INLINE uint32_t sub(const uint32_t x, const uint32_t y, uint32_t& carry) - { - index++; - if (index == 1 && OPS_COUNT == 1 && !CARRY_IN && !CARRY_OUT) - return host_math::sub(x, y); - else if (index == 1 && !CARRY_IN) - return host_math::sub_cc(x, y, carry); - else if (index < OPS_COUNT || CARRY_OUT) - return host_math::subc_cc(x, y, carry); - else - return host_math::subc(x, y, carry); - } - }; - - template - static constexpr HOST_INLINE void - multiply_raw(const storage& as, const storage& bs, storage& rs) - { - const uint32_t* a = as.limbs; - const uint32_t* b = bs.limbs; - uint32_t* r = rs.limbs; - for (unsigned i = 0; i < NLIMBS_B; i++) { - uint32_t carry = 0; - for (unsigned j = 0; j < NLIMBS_A; j++) - r[j + i] = host_math::madc_cc(a[j], b[i], r[j + i], carry); - r[NLIMBS_A + i] = carry; - } - } - - template - static constexpr HOST_INLINE uint32_t - add_sub_limbs(const storage& xs, const storage& ys, storage& rs) - { - const uint32_t* x = xs.limbs; - const uint32_t* y = ys.limbs; - uint32_t* r = rs.limbs; - uint32_t carry = 0; - carry_chain chain; - for (unsigned i = 0; i < NLIMBS; i++) - r[i] = SUBTRACT ? chain.sub(x[i], y[i], carry) : chain.add(x[i], y[i], carry); - return CARRY_OUT ? carry : 0; - } - - template - static constexpr HOST_INLINE storage left_shift(const storage& xs) - { - if constexpr (BITS == 0) - return xs; - else { - constexpr unsigned BITS32 = BITS % 32; - constexpr unsigned LIMBS_GAP = BITS / 32; - storage out{}; - if constexpr (LIMBS_GAP < NLIMBS) { - out.limbs[LIMBS_GAP] = xs.limbs[0] << BITS32; - for (unsigned i = 1; i < NLIMBS - LIMBS_GAP; i++) - out.limbs[i + LIMBS_GAP] = (xs.limbs[i] << BITS32) + (xs.limbs[i - 1] >> (32 - BITS32)); - } - return out; - } - } - - template - static constexpr HOST_INLINE storage right_shift(const storage& xs) - { - if constexpr (BITS == 0) - return xs; - else { - constexpr unsigned BITS32 = BITS % 32; - constexpr unsigned LIMBS_GAP = BITS / 32; - storage out{}; - if constexpr (LIMBS_GAP < NLIMBS - 1) { - for (unsigned i = 0; i < NLIMBS - LIMBS_GAP - 1; i++) - out.limbs[i] = (xs.limbs[i + LIMBS_GAP] >> BITS32) + (xs.limbs[i + LIMBS_GAP + 1] << (32 - BITS32)); - } - if constexpr (LIMBS_GAP < NLIMBS) out.limbs[NLIMBS - LIMBS_GAP - 1] = (xs.limbs[NLIMBS - 1] >> BITS32); - return out; - } - } - - template - static constexpr HOST_INLINE void integer_division( - const storage& num, const storage& denom, storage& q, storage& r) - { - storage temp = {}; - for (int limb_idx = NLIMBS_NUM - 1; limb_idx >= 0; limb_idx--) { - for (int bit_idx = 31; bit_idx >= 0; bit_idx--) { - r = left_shift(r); - r.limbs[0] |= ((num.limbs[limb_idx] >> bit_idx) & 1); - uint32_t c = add_sub_limbs(r, denom, temp); - if (limb_idx < NLIMBS_Q & !c) { - r = temp; - q.limbs[limb_idx] |= 1 << bit_idx; - } - } - } - } -} // namespace host_math - -#endif \ No newline at end of file diff --git a/icicle/include/fields/ptx.cuh b/icicle/include/fields/ptx.cuh deleted file mode 100644 index 7625bd928..000000000 --- a/icicle/include/fields/ptx.cuh +++ /dev/null @@ -1,282 +0,0 @@ -#pragma once -#include -#include - -namespace ptx { - - __device__ __forceinline__ uint32_t add(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm("add.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t add_cc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("add.cc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t addc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("addc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t addc_cc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("addc.cc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t sub(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm("sub.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t sub_cc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("sub.cc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t subc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("subc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t subc_cc(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm volatile("subc.cc.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t mul_lo(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm("mul.lo.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t mul_hi(const uint32_t x, const uint32_t y) - { - uint32_t result; - asm("mul.hi.u32 %0, %1, %2;" : "=r"(result) : "r"(x), "r"(y)); - return result; - } - - __device__ __forceinline__ uint32_t mad_lo(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm("mad.lo.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t mad_hi(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm("mad.hi.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t mad_lo_cc(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("mad.lo.cc.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t mad_hi_cc(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("mad.hi.cc.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t madc_lo(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("madc.lo.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t madc_hi(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("madc.hi.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t madc_lo_cc(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("madc.lo.cc.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint32_t madc_hi_cc(const uint32_t x, const uint32_t y, const uint32_t z) - { - uint32_t result; - asm volatile("madc.hi.cc.u32 %0, %1, %2, %3;" : "=r"(result) : "r"(x), "r"(y), "r"(z)); - return result; - } - - __device__ __forceinline__ uint64_t mov_b64(uint32_t lo, uint32_t hi) - { - uint64_t result; - asm("mov.b64 %0, {%1,%2};" : "=l"(result) : "r"(lo), "r"(hi)); - return result; - } - - // Gives u64 overloads a dedicated namespace. - // Callers should know exactly what they're calling (no implicit conversions). - namespace u64 { - - __device__ __forceinline__ uint64_t add(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm("add.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t add_cc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("add.cc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t addc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("addc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t addc_cc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("addc.cc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t sub(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm("sub.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t sub_cc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("sub.cc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t subc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("subc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t subc_cc(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm volatile("subc.cc.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t mul_lo(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm("mul.lo.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t mul_hi(const uint64_t x, const uint64_t y) - { - uint64_t result; - asm("mul.hi.u64 %0, %1, %2;" : "=l"(result) : "l"(x), "l"(y)); - return result; - } - - __device__ __forceinline__ uint64_t mad_lo(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm("mad.lo.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t mad_hi(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm("mad.hi.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t mad_lo_cc(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("mad.lo.cc.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t mad_hi_cc(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("mad.hi.cc.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t madc_lo(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("madc.lo.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t madc_hi(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("madc.hi.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t madc_lo_cc(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("madc.lo.cc.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - __device__ __forceinline__ uint64_t madc_hi_cc(const uint64_t x, const uint64_t y, const uint64_t z) - { - uint64_t result; - asm volatile("madc.hi.cc.u64 %0, %1, %2, %3;" : "=l"(result) : "l"(x), "l"(y), "l"(z)); - return result; - } - - } // namespace u64 - - __device__ __forceinline__ void bar_arrive(const unsigned name, const unsigned count) - { - asm volatile("bar.arrive %0, %1;" : : "r"(name), "r"(count) : "memory"); - } - - __device__ __forceinline__ void bar_sync(const unsigned name, const unsigned count) - { - asm volatile("bar.sync %0, %1;" : : "r"(name), "r"(count) : "memory"); - } - -} // namespace ptx \ No newline at end of file diff --git a/icicle/include/fields/quadratic_extension.cuh b/icicle/include/fields/quadratic_extension.cuh deleted file mode 100644 index 100653868..000000000 --- a/icicle/include/fields/quadratic_extension.cuh +++ /dev/null @@ -1,206 +0,0 @@ -#pragma once - -#include "field.cuh" -#include "gpu-utils/modifiers.cuh" -#include "gpu-utils/sharedmem.cuh" - -template -class ExtensionField -{ -private: - friend T; - - typedef typename T::Wide FWide; - - struct ExtensionWide { - FWide real; - FWide imaginary; - - friend HOST_DEVICE_INLINE ExtensionWide operator+(ExtensionWide xs, const ExtensionWide& ys) - { - return ExtensionWide{xs.real + ys.real, xs.imaginary + ys.imaginary}; - } - - friend HOST_DEVICE_INLINE ExtensionWide operator-(ExtensionWide xs, const ExtensionWide& ys) - { - return ExtensionWide{xs.real - ys.real, xs.imaginary - ys.imaginary}; - } - }; - -public: - typedef T FF; - static constexpr unsigned TLC = 2 * CONFIG::limbs_count; - - FF real; - FF imaginary; - - static constexpr HOST_DEVICE_INLINE ExtensionField zero() { return ExtensionField{FF::zero(), FF::zero()}; } - - static constexpr HOST_DEVICE_INLINE ExtensionField one() { return ExtensionField{FF::one(), FF::zero()}; } - - static constexpr HOST_DEVICE_INLINE ExtensionField to_montgomery(const ExtensionField& xs) - { - return ExtensionField{xs.real * FF{CONFIG::montgomery_r}, xs.imaginary * FF{CONFIG::montgomery_r}}; - } - - static constexpr HOST_DEVICE_INLINE ExtensionField from_montgomery(const ExtensionField& xs) - { - return ExtensionField{xs.real * FF{CONFIG::montgomery_r_inv}, xs.imaginary * FF{CONFIG::montgomery_r_inv}}; - } - - static HOST_INLINE ExtensionField rand_host() { return ExtensionField{FF::rand_host(), FF::rand_host()}; } - - static void rand_host_many(ExtensionField* out, int size) - { - for (int i = 0; i < size; i++) - out[i] = rand_host(); - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionField sub_modulus(const ExtensionField& xs) - { - return ExtensionField{FF::sub_modulus(&xs.real), FF::sub_modulus(&xs.imaginary)}; - } - - friend std::ostream& operator<<(std::ostream& os, const ExtensionField& xs) - { - os << "{ Real: " << xs.real << " }; { Imaginary: " << xs.imaginary << " }"; - return os; - } - - friend HOST_DEVICE_INLINE ExtensionField operator+(ExtensionField xs, const ExtensionField& ys) - { - return ExtensionField{xs.real + ys.real, xs.imaginary + ys.imaginary}; - } - - friend HOST_DEVICE_INLINE ExtensionField operator-(ExtensionField xs, const ExtensionField& ys) - { - return ExtensionField{xs.real - ys.real, xs.imaginary - ys.imaginary}; - } - - friend HOST_DEVICE_INLINE ExtensionField operator+(FF xs, const ExtensionField& ys) - { - return ExtensionField{xs + ys.real, ys.imaginary}; - } - - friend HOST_DEVICE_INLINE ExtensionField operator-(FF xs, const ExtensionField& ys) - { - return ExtensionField{xs - ys.real, FF::neg(ys.imaginary)}; - } - - friend HOST_DEVICE_INLINE ExtensionField operator+(ExtensionField xs, const FF& ys) - { - return ExtensionField{xs.real + ys, xs.imaginary}; - } - - friend HOST_DEVICE_INLINE ExtensionField operator-(ExtensionField xs, const FF& ys) - { - return ExtensionField{xs.real - ys, xs.imaginary}; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ExtensionField& xs, const ExtensionField& ys) - { - FWide real_prod = FF::mul_wide(xs.real, ys.real); - FWide imaginary_prod = FF::mul_wide(xs.imaginary, ys.imaginary); - FWide prod_of_sums = FF::mul_wide(xs.real + xs.imaginary, ys.real + ys.imaginary); - FWide nonresidue_times_im = FF::template mul_unsigned(imaginary_prod); - nonresidue_times_im = CONFIG::nonresidue_is_negative ? FWide::neg(nonresidue_times_im) : nonresidue_times_im; - return ExtensionWide{real_prod + nonresidue_times_im, prod_of_sums - real_prod - imaginary_prod}; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ExtensionField& xs, const FF& ys) - { - return ExtensionWide{FF::mul_wide(xs.real, ys), FF::mul_wide(xs.imaginary, ys)}; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const FF& xs, const ExtensionField& ys) - { - return mul_wide(ys, xs); - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionField reduce(const ExtensionWide& xs) - { - return ExtensionField{ - FF::template reduce(xs.real), FF::template reduce(xs.imaginary)}; - } - - template - friend HOST_DEVICE_INLINE ExtensionField operator*(const T1& xs, const T2& ys) - { - ExtensionWide xy = mul_wide(xs, ys); - return reduce(xy); - } - - friend HOST_DEVICE_INLINE bool operator==(const ExtensionField& xs, const ExtensionField& ys) - { - return (xs.real == ys.real) && (xs.imaginary == ys.imaginary); - } - - friend HOST_DEVICE_INLINE bool operator!=(const ExtensionField& xs, const ExtensionField& ys) { return !(xs == ys); } - - template - static HOST_DEVICE_INLINE ExtensionField mul_const(const ExtensionField& xs) - { - static constexpr FF mul_real = multiplier.real; - static constexpr FF mul_imaginary = multiplier.imaginary; - const FF xs_real = xs.real; - const FF xs_imaginary = xs.imaginary; - FF real_prod = FF::template mul_const(xs_real); - FF imaginary_prod = FF::template mul_const(xs_imaginary); - FF re_im = FF::template mul_const(xs_imaginary); - FF im_re = FF::template mul_const(xs_real); - FF nonresidue_times_im = FF::template mul_unsigned(imaginary_prod); - nonresidue_times_im = CONFIG::nonresidue_is_negative ? FF::neg(nonresidue_times_im) : nonresidue_times_im; - return ExtensionField{real_prod + nonresidue_times_im, re_im + im_re}; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionField mul_unsigned(const ExtensionField& xs) - { - return {FF::template mul_unsigned(xs.real), FF::template mul_unsigned(xs.imaginary)}; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionWide sqr_wide(const ExtensionField& xs) - { - // TODO: change to a more efficient squaring - return mul_wide(xs, xs); - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionField sqr(const ExtensionField& xs) - { - // TODO: change to a more efficient squaring - return xs * xs; - } - - template - static constexpr HOST_DEVICE_INLINE ExtensionField neg(const ExtensionField& xs) - { - return ExtensionField{FF::neg(xs.real), FF::neg(xs.imaginary)}; - } - - // inverse of zero is set to be zero which is what we want most of the time - static constexpr HOST_DEVICE_INLINE ExtensionField inverse(const ExtensionField& xs) - { - ExtensionField xs_conjugate = {xs.real, FF::neg(xs.imaginary)}; - FF nonresidue_times_im = FF::template mul_unsigned(FF::sqr(xs.imaginary)); - nonresidue_times_im = CONFIG::nonresidue_is_negative ? FF::neg(nonresidue_times_im) : nonresidue_times_im; - // TODO: wide here - FF xs_norm_squared = FF::sqr(xs.real) - nonresidue_times_im; - return xs_conjugate * ExtensionField{FF::inverse(xs_norm_squared), FF::zero()}; - } -}; - -template -struct SharedMemory> { - __device__ ExtensionField* getPointer() - { - extern __shared__ ExtensionField s_ext2_scalar_[]; - return s_ext2_scalar_; - } -}; \ No newline at end of file diff --git a/icicle/include/fields/snark_fields/bw6_761_base.cuh b/icicle/include/fields/snark_fields/bw6_761_base.cuh deleted file mode 100644 index 4ec110ffd..000000000 --- a/icicle/include/fields/snark_fields/bw6_761_base.cuh +++ /dev/null @@ -1,18 +0,0 @@ -#pragma once -#ifndef BW6_761_BASE_BASE_H -#define BW6_761_BASE_BASE_H - -#include "fields/storage.cuh" -#include "fields/params_gen.cuh" - -namespace bw6_761 { - struct fq_config { - static constexpr storage<24> modulus = {0x0000008b, 0xf49d0000, 0x70000082, 0xe6913e68, 0xeaf0a437, 0x160cf8ae, - 0x5667a8f8, 0x98a116c2, 0x73ebff2e, 0x71dcd3dc, 0x12f9fd90, 0x8689c8ed, - 0x25b42304, 0x03cebaff, 0xe584e919, 0x707ba638, 0x8087be41, 0x528275ef, - 0x81d14688, 0xb926186a, 0x04faff3e, 0xd187c940, 0xfb83ce0a, 0x0122e824}; - PARAMS(modulus) - }; -} // namespace bw6_761 - -#endif \ No newline at end of file diff --git a/icicle/include/fields/storage.cuh b/icicle/include/fields/storage.cuh deleted file mode 100644 index a8d432127..000000000 --- a/icicle/include/fields/storage.cuh +++ /dev/null @@ -1,25 +0,0 @@ -#pragma once -#include - -#define LIMBS_ALIGNMENT(x) ((x) % 4 == 0 ? 16 : ((x) % 2 == 0 ? 8 : 4)) - -template -struct -#ifdef __CUDA_ARCH__ - __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) -#endif - storage -{ - static constexpr unsigned LC = LIMBS_COUNT; - uint32_t limbs[LIMBS_COUNT]; -}; - -template -struct -#ifdef __CUDA_ARCH__ - __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) -#endif - storage_array -{ - storage storages[OMEGAS_COUNT]; -}; \ No newline at end of file diff --git a/icicle/include/gpu-utils/device_context.cuh b/icicle/include/gpu-utils/device_context.cuh deleted file mode 100644 index b1448a5ac..000000000 --- a/icicle/include/gpu-utils/device_context.cuh +++ /dev/null @@ -1,58 +0,0 @@ -#pragma once -#ifndef DEVICE_CONTEXT_H -#define DEVICE_CONTEXT_H - -#include -#include "gpu-utils/error_handler.cuh" - -namespace device_context { - - constexpr std::size_t MAX_DEVICES = 32; - - /** - * Properties of the device used in icicle functions. - */ - struct DeviceContext { - cudaStream_t& stream; /**< Stream to use. Default value: 0. */ - std::size_t device_id; /**< Index of the currently used GPU. Default value: 0. */ - cudaMemPool_t mempool; /**< Mempool to use. Default value: 0. */ - }; - - /** - * Return default device context that corresponds to using the default stream of the first GPU - */ - inline DeviceContext get_default_device_context() // TODO: naming convention ? - { - static cudaStream_t default_stream = (cudaStream_t)0; - return DeviceContext{ - (cudaStream_t&)default_stream, // stream - 0, // device_id - 0, // mempool - }; - } - - // checking whether a pointer is on host or device and asserts device matches provided device - static bool is_host_ptr(const void* p, int device_id = 0) - { - cudaPointerAttributes attributes; - CHK_STICKY(cudaPointerGetAttributes(&attributes, p)); - const bool is_on_host = attributes.type == cudaMemoryTypeHost || - attributes.type == cudaMemoryTypeUnregistered; // unregistered is host memory - const bool is_on_cur_device = !is_on_host && attributes.device == device_id; - const bool is_valid_ptr = is_on_host || is_on_cur_device; - if (!is_valid_ptr) { THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Invalid ptr"); } - - return is_on_host; - } - - static int get_cuda_device(const void* p) - { - cudaPointerAttributes attributes; - CHK_STICKY(cudaPointerGetAttributes(&attributes, p)); - const bool is_on_host = attributes.type == cudaMemoryTypeHost || - attributes.type == cudaMemoryTypeUnregistered; // unregistered is host memory - return is_on_host ? -1 : attributes.device; - } - -} // namespace device_context -#endif \ No newline at end of file diff --git a/icicle/include/gpu-utils/error_handler.cuh b/icicle/include/gpu-utils/error_handler.cuh deleted file mode 100644 index 1254ec60c..000000000 --- a/icicle/include/gpu-utils/error_handler.cuh +++ /dev/null @@ -1,152 +0,0 @@ -#pragma once -#ifndef ERR_H -#define ERR_H - -#include - -#include -#include -#include - -enum class IcicleError_t { - IcicleSuccess = 0, - InvalidArgument = 1, - MemoryAllocationError = 2, - UndefinedError = 999999999, -}; - -std::string inline IcicleGetErrorString(IcicleError_t error) -{ - switch (error) { - case IcicleError_t::IcicleSuccess: - return "Success"; - case IcicleError_t::InvalidArgument: - return "Invalid argument"; - case IcicleError_t::MemoryAllocationError: - return "Memory allocation error"; - case IcicleError_t::UndefinedError: - return "Undefined error occurred"; - default: - return "Unknown error code"; - } -} - -class IcicleError : public std::runtime_error -{ -private: - int errCode; // Field to store the error code - -public: - // Constructor for cudaError_t with optional message - IcicleError(cudaError_t cudaError, const std::string& msg = "") - : std::runtime_error("CUDA Error: " + std::string(cudaGetErrorString(cudaError)) + " " + msg), - errCode(static_cast(cudaError)) - { - } - - // Constructor for cudaError_t with const char* message - IcicleError(cudaError_t cudaError, const char* msg) : IcicleError(cudaError, std::string(msg)) {} - - // Constructor for IcicleError_t with optional message - IcicleError(IcicleError_t icicleError, const std::string& msg = "") - : std::runtime_error("Icicle Error: " + IcicleGetErrorString(icicleError) + " " + msg), - errCode(static_cast(icicleError)) - { - } - - // Constructor for IcicleError_t with const char* message - IcicleError(IcicleError_t icicleError, const char* msg) : IcicleError(icicleError, std::string(msg)) {} - - // Getter for errCode - int getErrorCode() const { return errCode; } -}; - -// TODO: ? do{..}while(0) as per https://hownot2code.wordpress.com/2016/12/05/do-while-0-in-macros/ - -#define CHK_ERR(err, func, file, line) check(err, func, file, line) -#define CHK_LOG(val) check((val), #val, __FILE__, __LINE__) -#define CHK_VAL(val, file, line) check((val), #val, file, line) - -cudaError_t inline check(cudaError_t err, const char* const func, const char* const file, const int line) -{ - if (err != cudaSuccess) { - std::cerr << "CUDA Runtime Error by: " << func << " at: " << file << ":" << line << std::endl; - std::cerr << cudaGetErrorString(err) << std::endl << std::endl; - } - - return err; -} - -// TODO: one macro that optionally (by compile-time switch) doesn't throw -#define CHK_STICKY_NO_THROW(val) checkCudaErrorIsSticky((val), #val, __FILE__, __LINE__, false) - -#define CHK_LAST_STICKY_NO_THROW() \ - checkCudaErrorIsSticky(cudaGetLastError(), "cudaGetLastError", __FILE__, __LINE__, false) - -#define CHK_LAST() checkCudaErrorIsSticky(cudaGetLastError(), "cudaGetLastError", __FILE__, __LINE__) - -#define CHK_STICKY(val) checkCudaErrorIsSticky((val), #val, __FILE__, __LINE__) - -#define THROW_ICICLE_CUDA(val) throwIcicleCudaErr(val, __FUNCTION__, __FILE__, __LINE__) -#define THROW_ICICLE_CUDA_ERR(val, func, file, line) throwIcicleCudaErr(val, func, file, line) -void inline throwIcicleCudaErr( - cudaError_t err, const char* const func, const char* const file, const int line, bool isUnrecoverable = true) -{ - // TODO: fmt::format introduced only in C++20 - std::string err_msg = (isUnrecoverable ? "!!!Unrecoverable!!! : " : "") + std::string{cudaGetErrorString(err)} + - " : detected by: " + func + " at: " + file + ":" + std::to_string(line) + - "\nThe error is reported there and may be caused by prior calls.\n"; - std::cerr << err_msg << std::endl; // TODO: Logging - throw IcicleError{err, err_msg}; -} - -#define THROW_ICICLE(val, reason, func, file, line) throwIcicleErr(val, reason, func, file, line) -#define THROW_ICICLE_ERR(val, reason) throwIcicleErr(val, reason, __FUNCTION__, __FILE__, __LINE__) -void inline throwIcicleErr( - IcicleError_t err, const char* const reason, const char* const func, const char* const file, const int line) -{ - std::string err_msg = std::string{IcicleGetErrorString(err)} + " : by: " + func + " at: " + file + ":" + - std::to_string(line) + " error: " + reason; - std::cerr << err_msg << std::endl; // TODO: Logging - throw IcicleError{err, err_msg}; -} - -cudaError_t inline checkCudaErrorIsSticky( - cudaError_t err, const char* const func, const char* const file, const int line, bool isThrowing = true) -{ - if (err != cudaSuccess) { - // check for sticky (unrecoverable) error when the only option is to restart process - cudaError_t err2 = cudaDeviceSynchronize(); - bool is_logged; - if (err2 != cudaSuccess) { // we suspect sticky error - if (err != err2) { - is_logged = true; - CHK_ERR(err, func, file, line); - } - // we are practically almost sure error is sticky - if (isThrowing) { - THROW_ICICLE_CUDA_ERR(err, func, file, line); - } else { - err = err2; - } - } - if (!is_logged) CHK_ERR(err, func, file, line); - } - - return err; -} - -// most common macros to use -#define CHK_INIT_IF_RETURN() \ - { \ - cudaError_t err_result = CHK_LAST(); \ - if (err_result != cudaSuccess) return err_result; \ - } - -#define CHK_IF_RETURN(val) \ - { \ - cudaError_t err_result = CHK_STICKY(val); \ - if (err_result != cudaSuccess) return err_result; \ - } - -#endif diff --git a/icicle/include/gpu-utils/modifiers.cuh b/icicle/include/gpu-utils/modifiers.cuh deleted file mode 100644 index 04ad395df..000000000 --- a/icicle/include/gpu-utils/modifiers.cuh +++ /dev/null @@ -1,11 +0,0 @@ -#if defined(DEVMODE) || defined(DEBUG) -#define INLINE_MACRO -#define UNROLL -#else -#define INLINE_MACRO __forceinline__ -#define UNROLL #pragma unroll -#endif - -#define HOST_INLINE __host__ INLINE_MACRO -#define DEVICE_INLINE __device__ INLINE_MACRO -#define HOST_DEVICE_INLINE __host__ __device__ INLINE_MACRO diff --git a/icicle/include/gpu-utils/sharedmem.cuh b/icicle/include/gpu-utils/sharedmem.cuh deleted file mode 100644 index 404af55e4..000000000 --- a/icicle/include/gpu-utils/sharedmem.cuh +++ /dev/null @@ -1,221 +0,0 @@ -// based on https://leimao.github.io/blog/CUDA-Shared-Memory-Templated-Kernel/ -// may be outdated, but only worked like that - -// ------------------------------------------------------------- -// cuDPP -- CUDA Data Parallel Primitives library -// ------------------------------------------------------------- -// $Revision: 5636 $ -// $Date: 2009-07-02 13:39:38 +1000 (Thu, 02 Jul 2009) $ -// ------------------------------------------------------------- -// This source code is distributed under the terms of license.txt -// in the root directory of this source distribution. -// ------------------------------------------------------------- - -/** - * @file - * sharedmem.h - * - * @brief Shared memory declaration struct for templatized types. - * - * Because dynamically sized shared memory arrays are declared "extern" in CUDA, - * we can't templatize their types directly. To get around this, we declare a - * simple wrapper struct that will declare the extern array with a different - * name depending on the type. This avoids linker errors about multiple - * definitions. - * - * To use dynamically allocated shared memory in a templatized __global__ or - * __device__ function, just replace code like this: - * - * 

- *  template
- *  __global__ void
- *  foo( T* d_out, T* d_in)
- *  {
- *      // Shared mem size is determined by the host app at run time
- *      extern __shared__  T sdata[];
- *      ...
- *      doStuff(sdata);
- *      ...
- *  }
- *
- * - * With this - * 
- *  template
- *  __global__ void
- *  foo( T* d_out, T* d_in)
- *  {
- *      // Shared mem size is determined by the host app at run time
- *      SharedMemory smem;
- *      T* sdata = smem.getPointer();
- *      ...
- *      doStuff(sdata);
- *      ...
- *  }
- *
- */ - -#ifndef _SHAREDMEM_H_ -#define _SHAREDMEM_H_ - -/** @brief Wrapper class for templatized dynamic shared memory arrays. - * - * This struct uses template specialization on the type \a T to declare - * a differently named dynamic shared memory array for each type - * (\code extern __shared__ T s_type[] \endcode). - * - * Currently there are specializations for the following types: - * \c int, \c uint, \c char, \c uchar, \c short, \c ushort, \c long, - * \c unsigned long, \c bool, \c float, and \c double. One can also specialize it - * for user defined types. - */ -template -struct SharedMemory { - //! @brief Return a pointer to the runtime-sized shared memory array. - //! @returns Pointer to runtime-sized shared memory array - __device__ T* getPointer() - { - // extern __device__ void Error_UnsupportedType(); // Ensure that we won't compile any un-specialized types - // Error_UnsupportedType(); - return (T*)0; - } - // TODO: Use operator overloading to make this class look like a regular array -}; - -// Following are the specializations for the following types. -// int, uint, char, uchar, short, ushort, long, ulong, bool, float, and double -// One could also specialize it for user-defined types. - -template <> -struct SharedMemory { - __device__ int* getPointer() - { - extern __shared__ int s_int[]; - return s_int; - } -}; - -template <> -struct SharedMemory { - __device__ unsigned int* getPointer() - { - extern __shared__ unsigned int s_uint[]; - return s_uint; - } -}; - -template <> -struct SharedMemory { - __device__ char* getPointer() - { - extern __shared__ char s_char[]; - return s_char; - } -}; - -template <> -struct SharedMemory { - __device__ unsigned char* getPointer() - { - extern __shared__ unsigned char s_uchar[]; - return s_uchar; - } -}; - -template <> -struct SharedMemory { - __device__ short* getPointer() - { - extern __shared__ short s_short[]; - return s_short; - } -}; - -template <> -struct SharedMemory { - __device__ unsigned short* getPointer() - { - extern __shared__ unsigned short s_ushort[]; - return s_ushort; - } -}; - -template <> -struct SharedMemory { - __device__ long* getPointer() - { - extern __shared__ long s_long[]; - return s_long; - } -}; - -template <> -struct SharedMemory { - __device__ unsigned long* getPointer() - { - extern __shared__ unsigned long s_ulong[]; - return s_ulong; - } -}; - -template <> -struct SharedMemory { - __device__ long long* getPointer() - { - extern __shared__ long long s_longlong[]; - return s_longlong; - } -}; - -template <> -struct SharedMemory { - __device__ unsigned long long* getPointer() - { - extern __shared__ unsigned long long s_ulonglong[]; - return s_ulonglong; - } -}; - -template <> -struct SharedMemory { - __device__ bool* getPointer() - { - extern __shared__ bool s_bool[]; - return s_bool; - } -}; - -template <> -struct SharedMemory { - __device__ float* getPointer() - { - extern __shared__ float s_float[]; - return s_float; - } -}; - -template <> -struct SharedMemory { - __device__ double* getPointer() - { - extern __shared__ double s_double[]; - return s_double; - } -}; - -template <> -struct SharedMemory { - __device__ uchar4* getPointer() - { - extern __shared__ uchar4 s_uchar4[]; - return s_uchar4; - } -}; - -#endif //_SHAREDMEM_H_ - -// Leave this at the end of the file -// Local Variables: -// mode:c++ -// c-file-style: "NVIDIA" -// End: \ No newline at end of file diff --git a/icicle/include/hash/hash.cuh b/icicle/include/hash/hash.cuh deleted file mode 100644 index 338d337dd..000000000 --- a/icicle/include/hash/hash.cuh +++ /dev/null @@ -1,174 +0,0 @@ -#pragma once -#ifndef HASH_H -#define HASH_H - -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "matrix/matrix.cuh" -#include - -using matrix::Matrix; - -/** - * @namespace hash - * Includes classes and methods for describing hash functions. - */ -namespace hash { - - /** - * @struct HashConfig - * Encodes hash operations parameters. - */ - struct HashConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream id. */ - bool are_inputs_on_device; /**< True if inputs are on device and false if they're on host. Default value: false. */ - bool - are_outputs_on_device; /**< True if outputs are on device and false if they're on host. Default value: false. */ - bool is_async; /**< Whether to run the hash operations asynchronously. If set to `true`, the functions will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, - * functions will block the current CPU thread. */ - }; - - /** - * A function that returns the default value of [HashConfig](@ref HashConfig) for the [Hasher](@ref - * Hasher) class. - * @return Default value of [HashConfig](@ref HashConfig). - */ - static HashConfig - default_hash_config(const device_context::DeviceContext& ctx = device_context::get_default_device_context()) - { - HashConfig config = { - ctx, // ctx - false, // are_inputs_on_device - false, // are_outputs_on_device - false, // is_async - }; - return config; - } - - /** - * @class Hasher - * - * An interface containing methods for hashing - * - * @tparam PreImage type of inputs elements - * @tparam Image type of state elements. Also used to describe the type of hash output - */ - template - class Hasher - { - public: - /// @brief the width of permutation state - const unsigned int width; - - /// @brief how many elements a state can fit per 1 permutation. Used with domain separation. - const unsigned int preimage_max_length; - - /// @brief portion of the state to absorb input into, or squeeze output from - const unsigned int rate; - - /// @brief start squeezing from this offset. Used with domain separation. - const unsigned int offset; - - Hasher(unsigned int width, unsigned int preimage_max_length, unsigned int rate, unsigned int offset) - : width(width), preimage_max_length(preimage_max_length), rate(rate), offset(offset) - { - assert( - rate * sizeof(PreImage) <= preimage_max_length * sizeof(Image) && - "Input rate can not be bigger than preimage max length"); - } - - virtual cudaError_t hash_2d( - const Matrix* inputs, - Image* states, - unsigned int number_of_inputs, - unsigned int output_len, - uint64_t number_of_rows, - const device_context::DeviceContext& ctx) const - { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Absorb 2d is not implemented for this hash"); - return cudaError_t::cudaSuccess; - }; - - virtual cudaError_t compress_and_inject( - const Matrix* matrices_to_inject, - unsigned int number_of_inputs, - uint64_t number_of_rows, - const Image* prev_layer, - Image* next_layer, - unsigned int digest_elements, - const device_context::DeviceContext& ctx) const - { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Compress and inject is not implemented for this hash"); - return cudaError_t::cudaSuccess; - } - - /// @param input pointer to input allocated on-device - /// @param out pointer to output allocated on-device - cudaError_t compress_many( - const Image* input, - Image* out, - unsigned int number_of_states, - unsigned int output_len, - const HashConfig& cfg) const - { - return hash_many((const PreImage*)input, out, number_of_states, width, output_len, cfg); - } - - virtual cudaError_t run_hash_many_kernel( - const PreImage* input, - Image* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const device_context::DeviceContext& ctx) const - { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Hash many kernel is not implemented for this hash"); - return cudaError_t::cudaSuccess; - }; - - cudaError_t hash_many( - const PreImage* input, - Image* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const HashConfig& cfg) const - { - const PreImage* d_input; - PreImage* d_alloc_input; - Image* d_output; - if (!cfg.are_inputs_on_device) { - CHK_IF_RETURN(cudaMallocAsync(&d_alloc_input, number_of_states * input_len * sizeof(PreImage), cfg.ctx.stream)); - CHK_IF_RETURN(cudaMemcpyAsync( - d_alloc_input, input, number_of_states * input_len * sizeof(PreImage), cudaMemcpyHostToDevice, - cfg.ctx.stream)); - d_input = d_alloc_input; - } else { - d_input = input; - } - - if (!cfg.are_outputs_on_device) { - CHK_IF_RETURN(cudaMallocAsync(&d_output, number_of_states * output_len * sizeof(Image), cfg.ctx.stream)); - } else { - d_output = output; - } - - CHK_IF_RETURN(run_hash_many_kernel(d_input, d_output, number_of_states, input_len, output_len, cfg.ctx)); - - if (!cfg.are_inputs_on_device) { CHK_IF_RETURN(cudaFreeAsync(d_alloc_input, cfg.ctx.stream)); } - if (!cfg.are_outputs_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync( - output, d_output, number_of_states * output_len * sizeof(Image), cudaMemcpyDeviceToHost, cfg.ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(d_output, cfg.ctx.stream)); - } - - if (!cfg.is_async) CHK_IF_RETURN(cudaStreamSynchronize(cfg.ctx.stream)); - - return CHK_LAST(); - }; - }; -} // namespace hash - -#endif \ No newline at end of file diff --git a/icicle/include/hash/keccak/keccak.cuh b/icicle/include/hash/keccak/keccak.cuh deleted file mode 100644 index d95c22a40..000000000 --- a/icicle/include/hash/keccak/keccak.cuh +++ /dev/null @@ -1,40 +0,0 @@ -#pragma once -#ifndef KECCAK_H -#define KECCAK_H - -#include -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" - -#include "hash/hash.cuh" - -using namespace hash; - -namespace keccak { - // Input rate in bytes - const int KECCAK_256_RATE = 136; - const int KECCAK_512_RATE = 72; - - // Digest size in u64 - const int KECCAK_256_DIGEST = 4; - const int KECCAK_512_DIGEST = 8; - - // Number of state elements in u64 - const int KECCAK_STATE_SIZE = 25; - - class Keccak : public Hasher - { - public: - cudaError_t run_hash_many_kernel( - const uint8_t* input, - uint64_t* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const device_context::DeviceContext& ctx) const override; - - Keccak(unsigned int rate) : Hasher(KECCAK_STATE_SIZE, KECCAK_STATE_SIZE, rate, 0) {} - }; -} // namespace keccak - -#endif \ No newline at end of file diff --git a/icicle/include/icicle/api/babybear.h b/icicle/include/icicle/api/babybear.h new file mode 100644 index 000000000..c0104443e --- /dev/null +++ b/icicle/include/icicle/api/babybear.h @@ -0,0 +1,73 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/fields/stark_fields/babybear.h" +#include "icicle/ntt.h" +#include "icicle/vec_ops.h" + +extern "C" eIcicleError babybear_ntt_init_domain( + babybear::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError babybear_ntt( + const babybear::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, babybear::scalar_t* output); + +extern "C" eIcicleError babybear_ntt_release_domain(); + +extern "C" eIcicleError babybear_vector_mul( + const babybear::scalar_t* vec_a, const babybear::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::scalar_t* result); + +extern "C" eIcicleError babybear_vector_add( + const babybear::scalar_t* vec_a, const babybear::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::scalar_t* result); + +extern "C" eIcicleError babybear_vector_sub( + const babybear::scalar_t* vec_a, const babybear::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::scalar_t* result); + +extern "C" eIcicleError babybear_matrix_transpose( + const babybear::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + babybear::scalar_t* output); + +extern "C" eIcicleError babybear_bit_reverse( + const babybear::scalar_t* input, uint64_t n, const VecOpsConfig* config, babybear::scalar_t* output); + + +extern "C" eIcicleError babybear_extension_vector_mul( + const babybear::extension_t* vec_a, const babybear::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::extension_t* result); + +extern "C" eIcicleError babybear_extension_vector_add( + const babybear::extension_t* vec_a, const babybear::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::extension_t* result); + +extern "C" eIcicleError babybear_extension_vector_sub( + const babybear::extension_t* vec_a, const babybear::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, babybear::extension_t* result); + +extern "C" eIcicleError babybear_extension_matrix_transpose( + const babybear::extension_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + babybear::extension_t* output); + +extern "C" eIcicleError babybear_extension_bit_reverse( + const babybear::extension_t* input, uint64_t n, const VecOpsConfig* config, babybear::extension_t* output); + + +extern "C" void babybear_extension_generate_scalars(babybear::extension_t* scalars, int size); + +extern "C" eIcicleError babybear_extension_scalar_convert_montgomery( + const babybear::extension_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, babybear::extension_t* output); + +extern "C" eIcicleError babybear_extension_ntt( + const babybear::extension_t* input, int size, NTTDir dir, const NTTConfig* config, babybear::extension_t* output); + + +extern "C" void babybear_generate_scalars(babybear::scalar_t* scalars, int size); + +extern "C" void babybear_scalar_convert_montgomery( + const babybear::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, babybear::scalar_t* output); + diff --git a/icicle/include/icicle/api/bls12_377.h b/icicle/include/icicle/api/bls12_377.h new file mode 100644 index 000000000..8287a5102 --- /dev/null +++ b/icicle/include/icicle/api/bls12_377.h @@ -0,0 +1,95 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/curves/params/bls12_377.h" +#include "icicle/ntt.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" + +extern "C" bool bls12_377_eq(bls12_377::projective_t* point1, bls12_377::projective_t* point2); + +extern "C" void bls12_377_to_affine(bls12_377::projective_t* point, bls12_377::affine_t* point_out); + +extern "C" void bls12_377_generate_projective_points(bls12_377::projective_t* points, int size); + +extern "C" void bls12_377_generate_affine_points(bls12_377::affine_t* points, int size); + +extern "C" eIcicleError bls12_377_affine_convert_montgomery( + const bls12_377::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_377::affine_t* output); + +extern "C" eIcicleError bls12_377_projective_convert_montgomery( + const bls12_377::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_377::projective_t* output); + +extern "C" eIcicleError bls12_377_ecntt( + const bls12_377::projective_t* input, int size, NTTDir dir, const NTTConfig* config, bls12_377::projective_t* output); + + +extern "C" eIcicleError bls12_377_precompute_msm_bases( + const bls12_377::affine_t* bases, + int nof_bases, + const MSMConfig* config, + bls12_377::affine_t* output_bases); + +extern "C" eIcicleError bls12_377_msm( + const bls12_377::scalar_t* scalars, const bls12_377::affine_t* points, int msm_size, const MSMConfig* config, bls12_377::projective_t* out); + +extern "C" bool bls12_377_g2_eq(bls12_377::g2_projective_t* point1, bls12_377::g2_projective_t* point2); + +extern "C" void bls12_377_g2_to_affine(bls12_377::g2_projective_t* point, bls12_377::g2_affine_t* point_out); + +extern "C" void bls12_377_g2_generate_projective_points(bls12_377::g2_projective_t* points, int size); + +extern "C" void bls12_377_g2_generate_affine_points(bls12_377::g2_affine_t* points, int size); + +extern "C" eIcicleError bls12_377_g2_affine_convert_montgomery( + const bls12_377::g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_377::g2_affine_t* output); + +extern "C" eIcicleError bls12_377_g2_projective_convert_montgomery( + const bls12_377::g2_projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_377::g2_projective_t* output); + +extern "C" eIcicleError bls12_377_g2_precompute_msm_bases( + const bls12_377::g2_affine_t* bases, + int nof_bases, + const MSMConfig* config, + bls12_377::g2_affine_t* output_bases); + +extern "C" eIcicleError bls12_377_g2_msm( + const bls12_377::scalar_t* scalars, const bls12_377::g2_affine_t* points, int msm_size, const MSMConfig* config, bls12_377::g2_projective_t* out); + +extern "C" eIcicleError bls12_377_ntt_init_domain( + bls12_377::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError bls12_377_ntt( + const bls12_377::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, bls12_377::scalar_t* output); + +extern "C" eIcicleError bls12_377_ntt_release_domain(); + +extern "C" eIcicleError bls12_377_vector_mul( + const bls12_377::scalar_t* vec_a, const bls12_377::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_377::scalar_t* result); + +extern "C" eIcicleError bls12_377_vector_add( + const bls12_377::scalar_t* vec_a, const bls12_377::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_377::scalar_t* result); + +extern "C" eIcicleError bls12_377_vector_sub( + const bls12_377::scalar_t* vec_a, const bls12_377::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_377::scalar_t* result); + +extern "C" eIcicleError bls12_377_matrix_transpose( + const bls12_377::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + bls12_377::scalar_t* output); + +extern "C" eIcicleError bls12_377_bit_reverse( + const bls12_377::scalar_t* input, uint64_t n, const VecOpsConfig* config, bls12_377::scalar_t* output); + + +extern "C" void bls12_377_generate_scalars(bls12_377::scalar_t* scalars, int size); + +extern "C" void bls12_377_scalar_convert_montgomery( + const bls12_377::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, bls12_377::scalar_t* output); + diff --git a/icicle/include/icicle/api/bls12_381.h b/icicle/include/icicle/api/bls12_381.h new file mode 100644 index 000000000..d2b7d6999 --- /dev/null +++ b/icicle/include/icicle/api/bls12_381.h @@ -0,0 +1,95 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/curves/params/bls12_381.h" +#include "icicle/ntt.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" + +extern "C" bool bls12_381_eq(bls12_381::projective_t* point1, bls12_381::projective_t* point2); + +extern "C" void bls12_381_to_affine(bls12_381::projective_t* point, bls12_381::affine_t* point_out); + +extern "C" void bls12_381_generate_projective_points(bls12_381::projective_t* points, int size); + +extern "C" void bls12_381_generate_affine_points(bls12_381::affine_t* points, int size); + +extern "C" eIcicleError bls12_381_affine_convert_montgomery( + const bls12_381::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_381::affine_t* output); + +extern "C" eIcicleError bls12_381_projective_convert_montgomery( + const bls12_381::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_381::projective_t* output); + +extern "C" eIcicleError bls12_381_ecntt( + const bls12_381::projective_t* input, int size, NTTDir dir, const NTTConfig* config, bls12_381::projective_t* output); + + +extern "C" eIcicleError bls12_381_precompute_msm_bases( + const bls12_381::affine_t* bases, + int nof_bases, + const MSMConfig* config, + bls12_381::affine_t* output_bases); + +extern "C" eIcicleError bls12_381_msm( + const bls12_381::scalar_t* scalars, const bls12_381::affine_t* points, int msm_size, const MSMConfig* config, bls12_381::projective_t* out); + +extern "C" bool bls12_381_g2_eq(bls12_381::g2_projective_t* point1, bls12_381::g2_projective_t* point2); + +extern "C" void bls12_381_g2_to_affine(bls12_381::g2_projective_t* point, bls12_381::g2_affine_t* point_out); + +extern "C" void bls12_381_g2_generate_projective_points(bls12_381::g2_projective_t* points, int size); + +extern "C" void bls12_381_g2_generate_affine_points(bls12_381::g2_affine_t* points, int size); + +extern "C" eIcicleError bls12_381_g2_affine_convert_montgomery( + const bls12_381::g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_381::g2_affine_t* output); + +extern "C" eIcicleError bls12_381_g2_projective_convert_montgomery( + const bls12_381::g2_projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bls12_381::g2_projective_t* output); + +extern "C" eIcicleError bls12_381_g2_precompute_msm_bases( + const bls12_381::g2_affine_t* bases, + int nof_bases, + const MSMConfig* config, + bls12_381::g2_affine_t* output_bases); + +extern "C" eIcicleError bls12_381_g2_msm( + const bls12_381::scalar_t* scalars, const bls12_381::g2_affine_t* points, int msm_size, const MSMConfig* config, bls12_381::g2_projective_t* out); + +extern "C" eIcicleError bls12_381_ntt_init_domain( + bls12_381::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError bls12_381_ntt( + const bls12_381::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, bls12_381::scalar_t* output); + +extern "C" eIcicleError bls12_381_ntt_release_domain(); + +extern "C" eIcicleError bls12_381_vector_mul( + const bls12_381::scalar_t* vec_a, const bls12_381::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_381::scalar_t* result); + +extern "C" eIcicleError bls12_381_vector_add( + const bls12_381::scalar_t* vec_a, const bls12_381::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_381::scalar_t* result); + +extern "C" eIcicleError bls12_381_vector_sub( + const bls12_381::scalar_t* vec_a, const bls12_381::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bls12_381::scalar_t* result); + +extern "C" eIcicleError bls12_381_matrix_transpose( + const bls12_381::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + bls12_381::scalar_t* output); + +extern "C" eIcicleError bls12_381_bit_reverse( + const bls12_381::scalar_t* input, uint64_t n, const VecOpsConfig* config, bls12_381::scalar_t* output); + + +extern "C" void bls12_381_generate_scalars(bls12_381::scalar_t* scalars, int size); + +extern "C" void bls12_381_scalar_convert_montgomery( + const bls12_381::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, bls12_381::scalar_t* output); + diff --git a/icicle/include/icicle/api/bn254.h b/icicle/include/icicle/api/bn254.h new file mode 100644 index 000000000..d054f23b4 --- /dev/null +++ b/icicle/include/icicle/api/bn254.h @@ -0,0 +1,95 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/curves/params/bn254.h" +#include "icicle/ntt.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" + +extern "C" bool bn254_eq(bn254::projective_t* point1, bn254::projective_t* point2); + +extern "C" void bn254_to_affine(bn254::projective_t* point, bn254::affine_t* point_out); + +extern "C" void bn254_generate_projective_points(bn254::projective_t* points, int size); + +extern "C" void bn254_generate_affine_points(bn254::affine_t* points, int size); + +extern "C" eIcicleError bn254_affine_convert_montgomery( + const bn254::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bn254::affine_t* output); + +extern "C" eIcicleError bn254_projective_convert_montgomery( + const bn254::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bn254::projective_t* output); + +extern "C" eIcicleError bn254_ecntt( + const bn254::projective_t* input, int size, NTTDir dir, const NTTConfig* config, bn254::projective_t* output); + + +extern "C" eIcicleError bn254_precompute_msm_bases( + const bn254::affine_t* bases, + int nof_bases, + const MSMConfig* config, + bn254::affine_t* output_bases); + +extern "C" eIcicleError bn254_msm( + const bn254::scalar_t* scalars, const bn254::affine_t* points, int msm_size, const MSMConfig* config, bn254::projective_t* out); + +extern "C" bool bn254_g2_eq(bn254::g2_projective_t* point1, bn254::g2_projective_t* point2); + +extern "C" void bn254_g2_to_affine(bn254::g2_projective_t* point, bn254::g2_affine_t* point_out); + +extern "C" void bn254_g2_generate_projective_points(bn254::g2_projective_t* points, int size); + +extern "C" void bn254_g2_generate_affine_points(bn254::g2_affine_t* points, int size); + +extern "C" eIcicleError bn254_g2_affine_convert_montgomery( + const bn254::g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bn254::g2_affine_t* output); + +extern "C" eIcicleError bn254_g2_projective_convert_montgomery( + const bn254::g2_projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bn254::g2_projective_t* output); + +extern "C" eIcicleError bn254_g2_precompute_msm_bases( + const bn254::g2_affine_t* bases, + int nof_bases, + const MSMConfig* config, + bn254::g2_affine_t* output_bases); + +extern "C" eIcicleError bn254_g2_msm( + const bn254::scalar_t* scalars, const bn254::g2_affine_t* points, int msm_size, const MSMConfig* config, bn254::g2_projective_t* out); + +extern "C" eIcicleError bn254_ntt_init_domain( + bn254::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError bn254_ntt( + const bn254::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, bn254::scalar_t* output); + +extern "C" eIcicleError bn254_ntt_release_domain(); + +extern "C" eIcicleError bn254_vector_mul( + const bn254::scalar_t* vec_a, const bn254::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bn254::scalar_t* result); + +extern "C" eIcicleError bn254_vector_add( + const bn254::scalar_t* vec_a, const bn254::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bn254::scalar_t* result); + +extern "C" eIcicleError bn254_vector_sub( + const bn254::scalar_t* vec_a, const bn254::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bn254::scalar_t* result); + +extern "C" eIcicleError bn254_matrix_transpose( + const bn254::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + bn254::scalar_t* output); + +extern "C" eIcicleError bn254_bit_reverse( + const bn254::scalar_t* input, uint64_t n, const VecOpsConfig* config, bn254::scalar_t* output); + + +extern "C" void bn254_generate_scalars(bn254::scalar_t* scalars, int size); + +extern "C" void bn254_scalar_convert_montgomery( + const bn254::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, bn254::scalar_t* output); + diff --git a/icicle/include/icicle/api/bw6_761.h b/icicle/include/icicle/api/bw6_761.h new file mode 100644 index 000000000..31d3b87e2 --- /dev/null +++ b/icicle/include/icicle/api/bw6_761.h @@ -0,0 +1,95 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/curves/params/bw6_761.h" +#include "icicle/ntt.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" + +extern "C" bool bw6_761_eq(bw6_761::projective_t* point1, bw6_761::projective_t* point2); + +extern "C" void bw6_761_to_affine(bw6_761::projective_t* point, bw6_761::affine_t* point_out); + +extern "C" void bw6_761_generate_projective_points(bw6_761::projective_t* points, int size); + +extern "C" void bw6_761_generate_affine_points(bw6_761::affine_t* points, int size); + +extern "C" eIcicleError bw6_761_affine_convert_montgomery( + const bw6_761::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bw6_761::affine_t* output); + +extern "C" eIcicleError bw6_761_projective_convert_montgomery( + const bw6_761::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bw6_761::projective_t* output); + +extern "C" eIcicleError bw6_761_ecntt( + const bw6_761::projective_t* input, int size, NTTDir dir, const NTTConfig* config, bw6_761::projective_t* output); + + +extern "C" eIcicleError bw6_761_precompute_msm_bases( + const bw6_761::affine_t* bases, + int nof_bases, + const MSMConfig* config, + bw6_761::affine_t* output_bases); + +extern "C" eIcicleError bw6_761_msm( + const bw6_761::scalar_t* scalars, const bw6_761::affine_t* points, int msm_size, const MSMConfig* config, bw6_761::projective_t* out); + +extern "C" bool bw6_761_g2_eq(bw6_761::g2_projective_t* point1, bw6_761::g2_projective_t* point2); + +extern "C" void bw6_761_g2_to_affine(bw6_761::g2_projective_t* point, bw6_761::g2_affine_t* point_out); + +extern "C" void bw6_761_g2_generate_projective_points(bw6_761::g2_projective_t* points, int size); + +extern "C" void bw6_761_g2_generate_affine_points(bw6_761::g2_affine_t* points, int size); + +extern "C" eIcicleError bw6_761_g2_affine_convert_montgomery( + const bw6_761::g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, bw6_761::g2_affine_t* output); + +extern "C" eIcicleError bw6_761_g2_projective_convert_montgomery( + const bw6_761::g2_projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, bw6_761::g2_projective_t* output); + +extern "C" eIcicleError bw6_761_g2_precompute_msm_bases( + const bw6_761::g2_affine_t* bases, + int nof_bases, + const MSMConfig* config, + bw6_761::g2_affine_t* output_bases); + +extern "C" eIcicleError bw6_761_g2_msm( + const bw6_761::scalar_t* scalars, const bw6_761::g2_affine_t* points, int msm_size, const MSMConfig* config, bw6_761::g2_projective_t* out); + +extern "C" eIcicleError bw6_761_ntt_init_domain( + bw6_761::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError bw6_761_ntt( + const bw6_761::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, bw6_761::scalar_t* output); + +extern "C" eIcicleError bw6_761_ntt_release_domain(); + +extern "C" eIcicleError bw6_761_vector_mul( + const bw6_761::scalar_t* vec_a, const bw6_761::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bw6_761::scalar_t* result); + +extern "C" eIcicleError bw6_761_vector_add( + const bw6_761::scalar_t* vec_a, const bw6_761::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bw6_761::scalar_t* result); + +extern "C" eIcicleError bw6_761_vector_sub( + const bw6_761::scalar_t* vec_a, const bw6_761::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, bw6_761::scalar_t* result); + +extern "C" eIcicleError bw6_761_matrix_transpose( + const bw6_761::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + bw6_761::scalar_t* output); + +extern "C" eIcicleError bw6_761_bit_reverse( + const bw6_761::scalar_t* input, uint64_t n, const VecOpsConfig* config, bw6_761::scalar_t* output); + + +extern "C" void bw6_761_generate_scalars(bw6_761::scalar_t* scalars, int size); + +extern "C" void bw6_761_scalar_convert_montgomery( + const bw6_761::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, bw6_761::scalar_t* output); + diff --git a/icicle/include/icicle/api/grumpkin.h b/icicle/include/icicle/api/grumpkin.h new file mode 100644 index 000000000..9908e492b --- /dev/null +++ b/icicle/include/icicle/api/grumpkin.h @@ -0,0 +1,59 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/curves/params/grumpkin.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" + +extern "C" bool grumpkin_eq(grumpkin::projective_t* point1, grumpkin::projective_t* point2); + +extern "C" void grumpkin_to_affine(grumpkin::projective_t* point, grumpkin::affine_t* point_out); + +extern "C" void grumpkin_generate_projective_points(grumpkin::projective_t* points, int size); + +extern "C" void grumpkin_generate_affine_points(grumpkin::affine_t* points, int size); + +extern "C" eIcicleError grumpkin_affine_convert_montgomery( + const grumpkin::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, grumpkin::affine_t* output); + +extern "C" eIcicleError grumpkin_projective_convert_montgomery( + const grumpkin::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, grumpkin::projective_t* output); + +extern "C" eIcicleError grumpkin_precompute_msm_bases( + const grumpkin::affine_t* bases, + int nof_bases, + const MSMConfig* config, + grumpkin::affine_t* output_bases); + +extern "C" eIcicleError grumpkin_msm( + const grumpkin::scalar_t* scalars, const grumpkin::affine_t* points, int msm_size, const MSMConfig* config, grumpkin::projective_t* out); + +extern "C" eIcicleError grumpkin_vector_mul( + const grumpkin::scalar_t* vec_a, const grumpkin::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, grumpkin::scalar_t* result); + +extern "C" eIcicleError grumpkin_vector_add( + const grumpkin::scalar_t* vec_a, const grumpkin::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, grumpkin::scalar_t* result); + +extern "C" eIcicleError grumpkin_vector_sub( + const grumpkin::scalar_t* vec_a, const grumpkin::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, grumpkin::scalar_t* result); + +extern "C" eIcicleError grumpkin_matrix_transpose( + const grumpkin::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + grumpkin::scalar_t* output); + +extern "C" eIcicleError grumpkin_bit_reverse( + const grumpkin::scalar_t* input, uint64_t n, const VecOpsConfig* config, grumpkin::scalar_t* output); + + +extern "C" void grumpkin_generate_scalars(grumpkin::scalar_t* scalars, int size); + +extern "C" void grumpkin_scalar_convert_montgomery( + const grumpkin::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, grumpkin::scalar_t* output); + diff --git a/icicle/include/icicle/api/stark252.h b/icicle/include/icicle/api/stark252.h new file mode 100644 index 000000000..3bbe9626f --- /dev/null +++ b/icicle/include/icicle/api/stark252.h @@ -0,0 +1,44 @@ +// WARNING: This file is auto-generated by a script. +// Any changes made to this file may be overwritten. +// Please modify the code generation script instead. +// Path to the code generation script: scripts/gen_c_api.py + +#pragma once + +#include "icicle/fields/stark_fields/stark252.h" +#include "icicle/ntt.h" +#include "icicle/vec_ops.h" + +extern "C" eIcicleError stark252_ntt_init_domain( + stark252::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError stark252_ntt( + const stark252::scalar_t* input, int size, NTTDir dir, const NTTConfig* config, stark252::scalar_t* output); + +extern "C" eIcicleError stark252_ntt_release_domain(); + +extern "C" eIcicleError stark252_vector_mul( + const stark252::scalar_t* vec_a, const stark252::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, stark252::scalar_t* result); + +extern "C" eIcicleError stark252_vector_add( + const stark252::scalar_t* vec_a, const stark252::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, stark252::scalar_t* result); + +extern "C" eIcicleError stark252_vector_sub( + const stark252::scalar_t* vec_a, const stark252::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, stark252::scalar_t* result); + +extern "C" eIcicleError stark252_matrix_transpose( + const stark252::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + stark252::scalar_t* output); + +extern "C" eIcicleError stark252_bit_reverse( + const stark252::scalar_t* input, uint64_t n, const VecOpsConfig* config, stark252::scalar_t* output); + + +extern "C" void stark252_generate_scalars(stark252::scalar_t* scalars, int size); + +extern "C" void stark252_scalar_convert_montgomery( + const stark252::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, stark252::scalar_t* output); + diff --git a/icicle/include/api/templates/curves/curve.h b/icicle/include/icicle/api/templates/curves/curve.template similarity index 50% rename from icicle/include/api/templates/curves/curve.h rename to icicle/include/icicle/api/templates/curves/curve.template index 5606051cd..e85ad2183 100644 --- a/icicle/include/api/templates/curves/curve.h +++ b/icicle/include/icicle/api/templates/curves/curve.template @@ -6,8 +6,8 @@ extern "C" void ${CURVE}_generate_projective_points(${CURVE}::projective_t* poin extern "C" void ${CURVE}_generate_affine_points(${CURVE}::affine_t* points, int size); -extern "C" cudaError_t ${CURVE}_affine_convert_montgomery( - ${CURVE}::affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); +extern "C" eIcicleError ${CURVE}_affine_convert_montgomery( + const ${CURVE}::affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, ${CURVE}::affine_t* output); -extern "C" cudaError_t ${CURVE}_projective_convert_montgomery( - ${CURVE}::projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); \ No newline at end of file +extern "C" eIcicleError ${CURVE}_projective_convert_montgomery( + const ${CURVE}::projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, ${CURVE}::projective_t* output); \ No newline at end of file diff --git a/icicle/include/api/templates/curves/curve_g2.h b/icicle/include/icicle/api/templates/curves/curve_g2.template similarity index 51% rename from icicle/include/api/templates/curves/curve_g2.h rename to icicle/include/icicle/api/templates/curves/curve_g2.template index 250aec944..d5a56db8e 100644 --- a/icicle/include/api/templates/curves/curve_g2.h +++ b/icicle/include/icicle/api/templates/curves/curve_g2.template @@ -6,8 +6,8 @@ extern "C" void ${CURVE}_g2_generate_projective_points(${CURVE}::g2_projective_t extern "C" void ${CURVE}_g2_generate_affine_points(${CURVE}::g2_affine_t* points, int size); -extern "C" cudaError_t ${CURVE}_g2_affine_convert_montgomery( - ${CURVE}::g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); +extern "C" eIcicleError ${CURVE}_g2_affine_convert_montgomery( + const ${CURVE}::g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, ${CURVE}::g2_affine_t* output); -extern "C" cudaError_t ${CURVE}_g2_projective_convert_montgomery( - ${CURVE}::g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx); \ No newline at end of file +extern "C" eIcicleError ${CURVE}_g2_projective_convert_montgomery( + const ${CURVE}::g2_projective_t* input, size_t n, bool is_into, const VecOpsConfig* config, ${CURVE}::g2_projective_t* output); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/curves/ecntt.template b/icicle/include/icicle/api/templates/curves/ecntt.template new file mode 100644 index 000000000..d2306fbf2 --- /dev/null +++ b/icicle/include/icicle/api/templates/curves/ecntt.template @@ -0,0 +1,2 @@ +extern "C" eIcicleError ${CURVE}_ecntt( + const ${CURVE}::projective_t* input, int size, NTTDir dir, const NTTConfig<${CURVE}::scalar_t>* config, ${CURVE}::projective_t* output); diff --git a/icicle/include/icicle/api/templates/curves/msm.template b/icicle/include/icicle/api/templates/curves/msm.template new file mode 100644 index 000000000..c0922782e --- /dev/null +++ b/icicle/include/icicle/api/templates/curves/msm.template @@ -0,0 +1,8 @@ +extern "C" eIcicleError ${CURVE}_precompute_msm_bases( + const ${CURVE}::affine_t* bases, + int nof_bases, + const MSMConfig* config, + ${CURVE}::affine_t* output_bases); + +extern "C" eIcicleError ${CURVE}_msm( + const ${CURVE}::scalar_t* scalars, const ${CURVE}::affine_t* points, int msm_size, const MSMConfig* config, ${CURVE}::projective_t* out); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/curves/msm_g2.template b/icicle/include/icicle/api/templates/curves/msm_g2.template new file mode 100644 index 000000000..0880aace0 --- /dev/null +++ b/icicle/include/icicle/api/templates/curves/msm_g2.template @@ -0,0 +1,8 @@ +extern "C" eIcicleError ${CURVE}_g2_precompute_msm_bases( + const ${CURVE}::g2_affine_t* bases, + int nof_bases, + const MSMConfig* config, + ${CURVE}::g2_affine_t* output_bases); + +extern "C" eIcicleError ${CURVE}_g2_msm( + const ${CURVE}::scalar_t* scalars, const ${CURVE}::g2_affine_t* points, int msm_size, const MSMConfig* config, ${CURVE}::g2_projective_t* out); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/fields/field.template b/icicle/include/icicle/api/templates/fields/field.template new file mode 100644 index 000000000..1e17151d5 --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/field.template @@ -0,0 +1,4 @@ +extern "C" void ${FIELD}_generate_scalars(${FIELD}::scalar_t* scalars, int size); + +extern "C" void ${FIELD}_scalar_convert_montgomery( + const ${FIELD}::scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, ${FIELD}::scalar_t* output); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/fields/field_ext.template b/icicle/include/icicle/api/templates/fields/field_ext.template new file mode 100644 index 000000000..7e9e7dc11 --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/field_ext.template @@ -0,0 +1,4 @@ +extern "C" void ${FIELD}_extension_generate_scalars(${FIELD}::extension_t* scalars, int size); + +extern "C" eIcicleError ${FIELD}_extension_scalar_convert_montgomery( + const ${FIELD}::extension_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, ${FIELD}::extension_t* output); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/fields/ntt.template b/icicle/include/icicle/api/templates/fields/ntt.template new file mode 100644 index 000000000..d188e2c72 --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/ntt.template @@ -0,0 +1,7 @@ +extern "C" eIcicleError ${FIELD}_ntt_init_domain( + ${FIELD}::scalar_t* primitive_root, const NTTInitDomainConfig* config); + +extern "C" eIcicleError ${FIELD}_ntt( + const ${FIELD}::scalar_t* input, int size, NTTDir dir, const NTTConfig<${FIELD}::scalar_t>* config, ${FIELD}::scalar_t* output); + +extern "C" eIcicleError ${FIELD}_ntt_release_domain(); \ No newline at end of file diff --git a/icicle/include/icicle/api/templates/fields/ntt_ext.template b/icicle/include/icicle/api/templates/fields/ntt_ext.template new file mode 100644 index 000000000..1b2819c6e --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/ntt_ext.template @@ -0,0 +1,2 @@ +extern "C" eIcicleError ${FIELD}_extension_ntt( + const ${FIELD}::extension_t* input, int size, NTTDir dir, const NTTConfig<${FIELD}::scalar_t>* config, ${FIELD}::extension_t* output); diff --git a/icicle/include/icicle/api/templates/fields/vec_ops.template b/icicle/include/icicle/api/templates/fields/vec_ops.template new file mode 100644 index 000000000..5ebbed7aa --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/vec_ops.template @@ -0,0 +1,18 @@ +extern "C" eIcicleError ${FIELD}_vector_mul( + const ${FIELD}::scalar_t* vec_a, const ${FIELD}::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::scalar_t* result); + +extern "C" eIcicleError ${FIELD}_vector_add( + const ${FIELD}::scalar_t* vec_a, const ${FIELD}::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::scalar_t* result); + +extern "C" eIcicleError ${FIELD}_vector_sub( + const ${FIELD}::scalar_t* vec_a, const ${FIELD}::scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::scalar_t* result); + +extern "C" eIcicleError ${FIELD}_matrix_transpose( + const ${FIELD}::scalar_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + ${FIELD}::scalar_t* output); + +extern "C" eIcicleError ${FIELD}_bit_reverse( + const ${FIELD}::scalar_t* input, uint64_t n, const VecOpsConfig* config, ${FIELD}::scalar_t* output); diff --git a/icicle/include/icicle/api/templates/fields/vec_ops_ext.template b/icicle/include/icicle/api/templates/fields/vec_ops_ext.template new file mode 100644 index 000000000..d79861793 --- /dev/null +++ b/icicle/include/icicle/api/templates/fields/vec_ops_ext.template @@ -0,0 +1,18 @@ +extern "C" eIcicleError ${FIELD}_extension_vector_mul( + const ${FIELD}::extension_t* vec_a, const ${FIELD}::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::extension_t* result); + +extern "C" eIcicleError ${FIELD}_extension_vector_add( + const ${FIELD}::extension_t* vec_a, const ${FIELD}::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::extension_t* result); + +extern "C" eIcicleError ${FIELD}_extension_vector_sub( + const ${FIELD}::extension_t* vec_a, const ${FIELD}::extension_t* vec_b, uint64_t n, const VecOpsConfig* config, ${FIELD}::extension_t* result); + +extern "C" eIcicleError ${FIELD}_extension_matrix_transpose( + const ${FIELD}::extension_t* input, + uint32_t nof_rows, + uint32_t nof_cols, + const VecOpsConfig* config, + ${FIELD}::extension_t* output); + +extern "C" eIcicleError ${FIELD}_extension_bit_reverse( + const ${FIELD}::extension_t* input, uint64_t n, const VecOpsConfig* config, ${FIELD}::extension_t* output); diff --git a/icicle/include/icicle/backend/ecntt_backend.h b/icicle/include/icicle/backend/ecntt_backend.h new file mode 100644 index 000000000..a56a128b6 --- /dev/null +++ b/icicle/include/icicle/backend/ecntt_backend.h @@ -0,0 +1,33 @@ +#pragma once + +#include + +#include "icicle/errors.h" +#include "icicle/runtime.h" + +#include "icicle/ntt.h" +#include "icicle/curves/curve_config.h" +#include "icicle/utils/utils.h" + +using namespace curve_config; + +namespace icicle { + /*************************** Backend registration***************************/ + using ECNttFieldImpl = std::function& config, + projective_t* output)>; + + void register_ecntt(const std::string& deviceType, ECNttFieldImpl impl); + +#define REGISTER_ECNTT_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ecntt_ext_field) = []() -> bool { \ + register_ecntt(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/backend/msm_backend.h b/icicle/include/icicle/backend/msm_backend.h new file mode 100644 index 000000000..47750bb85 --- /dev/null +++ b/icicle/include/icicle/backend/msm_backend.h @@ -0,0 +1,82 @@ +#pragma once + +#include "icicle/msm.h" +#include "icicle/curves/curve_config.h" + +using namespace curve_config; + +namespace icicle { + /*************************** Backend registration ***************************/ + + using MsmImpl = std::function; + + void register_msm(const std::string& deviceType, MsmImpl impl); + +#define REGISTER_MSM_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_msm) = []() -> bool { \ + register_msm(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using MsmPreComputeImpl = std::function; + + void register_msm_precompute_bases(const std::string& deviceType, MsmPreComputeImpl impl); + +#define REGISTER_MSM_PRE_COMPUTE_BASES_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_msm_precompute_bases) = []() -> bool { \ + register_msm_precompute_bases(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + +#ifdef G2 + using MsmG2Impl = std::function; + + void register_g2_msm(const std::string& deviceType, MsmG2Impl impl); + +#define REGISTER_MSM_G2_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_msm_g2) = []() -> bool { \ + register_g2_msm(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using MsmG2PreComputeImpl = std::function; + + void register_g2_msm_precompute_bases(const std::string& deviceType, MsmG2PreComputeImpl impl); + +#define REGISTER_MSM_G2_PRE_COMPUTE_BASES_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_msm_g2_precompute_bases) = []() -> bool { \ + register_g2_msm_precompute_bases(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +#endif // G2 +} // namespace icicle diff --git a/icicle/include/icicle/backend/msm_config.h b/icicle/include/icicle/backend/msm_config.h new file mode 100644 index 000000000..7873fa26d --- /dev/null +++ b/icicle/include/icicle/backend/msm_config.h @@ -0,0 +1,18 @@ +#pragma once + +/********* CPU Backend Configurations *********/ +namespace CpuBackendConfig { + // Backend-specific configuration flags as constexpr strings + constexpr const char* CPU_NOF_THREADS = "n_threads"; +} // namespace CpuBackendConfig +/********* CUDA Backend Configurations *********/ + +namespace CudaBackendConfig { + // Backend-specific configuration flags as constexpr strings + constexpr const char* CUDA_MSM_IS_BIG_TRIANGLE = "is_big_triangle"; + constexpr const char* CUDA_MSM_LARGE_BUCKET_FACTOR = "large_bucket_factor"; + constexpr const int CUDA_MSM_LARGE_BUCKET_FACTOR_DEFAULT_VAL = 10; + constexpr const char* CUDA_MSM_NOF_CHUNKS = "nof_chunks"; + constexpr const int CUDA_MSM_NOF_CHUNKS_DEFAULT_VAL = 0; + +} // namespace CudaBackendConfig \ No newline at end of file diff --git a/icicle/include/icicle/backend/ntt_backend.h b/icicle/include/icicle/backend/ntt_backend.h new file mode 100644 index 000000000..9d61c56d2 --- /dev/null +++ b/icicle/include/icicle/backend/ntt_backend.h @@ -0,0 +1,92 @@ +#pragma once + +#include "icicle/ntt.h" +#include "icicle/fields/field_config.h" + +using namespace field_config; + +namespace icicle { + + /*************************** Backend registration ***************************/ + + /*************************** NTT ***************************/ + using NttImpl = std::function& config, + scalar_t* output)>; + + void register_ntt(const std::string& deviceType, NttImpl impl); + +#define REGISTER_NTT_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ntt) = []() -> bool { \ + register_ntt(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + +#ifdef EXT_FIELD + using NttExtFieldImpl = std::function& config, + extension_t* output)>; + + void register_extension_ntt(const std::string& deviceType, NttExtFieldImpl impl); + +#define REGISTER_NTT_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ntt_ext_field) = []() -> bool { \ + register_extension_ntt(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +#endif // EXT_FIELD + + /*************************** INIT DOMAIN ***************************/ + using NttInitDomainImpl = std::function; + + void register_ntt_init_domain(const std::string& deviceType, NttInitDomainImpl); + +#define REGISTER_NTT_INIT_DOMAIN_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ntt_init_domain) = []() -> bool { \ + register_ntt_init_domain(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + /*************************** RELEASE DOMAIN ***************************/ + // Note: 'dummy' is a workaround for the function required per field but need to differentiate by type when + // calling. TODO Yuval: avoid this param somehow + using NttReleaseDomainImpl = std::function; + + void register_ntt_release_domain(const std::string& deviceType, NttReleaseDomainImpl); + +#define REGISTER_NTT_RELEASE_DOMAIN_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ntt_release_domain) = []() -> bool { \ + register_ntt_release_domain(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + /*************************** GET ROU FROM DOMAIN ***************************/ + using NttGetRouFromDomainImpl = std::function; + + void register_ntt_get_rou_from_domain(const std::string& deviceType, NttGetRouFromDomainImpl); + +#define REGISTER_NTT_GET_ROU_FROM_DOMAIN_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_ntt_get_rou_from_domain) = []() -> bool { \ + register_ntt_get_rou_from_domain(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/backend/ntt_config.h b/icicle/include/icicle/backend/ntt_config.h new file mode 100644 index 000000000..d7021cbb4 --- /dev/null +++ b/icicle/include/icicle/backend/ntt_config.h @@ -0,0 +1,18 @@ +#pragma once + +/********* CPU Backend Configurations *********/ + +/********* CUDA Backend Configurations *********/ + +namespace CudaBackendConfig { + // Enumeration for NTT (Number Theoretic Transform) algorithms + enum NttAlgorithm { + Auto, ///< Automatically choose the algorithm + Radix2, ///< Use Radix-2 algorithm + MixedRadix ///< Use Mixed Radix algorithm + }; + + // Backend-specific configuration flags as constexpr strings + constexpr const char* CUDA_NTT_FAST_TWIDDLES_MODE = "fast_twiddles"; ///< Enable fast twiddles mode + constexpr const char* CUDA_NTT_ALGORITHM = "ntt_algorithm"; ///< Set NTT algorithm +} // namespace CudaBackendConfig \ No newline at end of file diff --git a/icicle/include/icicle/backend/polynomial_backend.h b/icicle/include/icicle/backend/polynomial_backend.h new file mode 100644 index 000000000..7c184d0a8 --- /dev/null +++ b/icicle/include/icicle/backend/polynomial_backend.h @@ -0,0 +1,26 @@ +#pragma once + +#include "icicle/polynomials/polynomials.h" +#include "icicle/polynomials/polynomial_abstract_factory.h" +#include "icicle/fields/field_config.h" +using namespace field_config; + +namespace icicle { + + /*************************** Backend registration ***************************/ + + void register_polynomial_factory( + const std::string& deviceType, std::shared_ptr> factory); + +#define REGISTER_SCALAR_POLYNOMIAL_FACTORY_BACKEND(DEVICE_TYPE, FACTORY) \ + namespace { \ + static bool UNIQUE(_reg_polynomial_factory) = []() -> bool { \ + register_polynomial_factory(DEVICE_TYPE, std::make_shared()); \ + return true; \ + }(); \ + } + + // External template instantiation to ensure the template is compiled for specific types. + extern template class Polynomial; + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/backend/vec_ops_backend.h b/icicle/include/icicle/backend/vec_ops_backend.h new file mode 100644 index 000000000..74502a9a4 --- /dev/null +++ b/icicle/include/icicle/backend/vec_ops_backend.h @@ -0,0 +1,351 @@ +#pragma once + +#include "icicle/vec_ops.h" +#include "icicle/fields/field_config.h" +using namespace field_config; + +namespace icicle { + /*************************** Backend registration ***************************/ + + using scalarVectorOpImpl = std::function; + + using scalarVectorOpImplInplaceA = std::function; + + void register_vector_add(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_VECTOR_ADD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_add) = []() -> bool { \ + register_vector_add(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_vector_accumulate(const std::string& deviceType, scalarVectorOpImplInplaceA impl); + +#define REGISTER_VECTOR_ACCUMULATE_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_accumulate) = []() -> bool { \ + register_vector_accumulate(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_vector_sub(const std::string& deviceType, scalarVectorOpImpl impl); +#define REGISTER_VECTOR_SUB_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_sub) = []() -> bool { \ + register_vector_sub(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_vector_mul(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_VECTOR_MUL_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_mul) = []() -> bool { \ + register_vector_mul(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_vector_div(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_VECTOR_DIV_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_div) = []() -> bool { \ + register_vector_div(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_scalar_mul_vec(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_SCALAR_MUL_VEC_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_mul_vec) = []() -> bool { \ + register_scalar_mul_vec(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_scalar_add_vec(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_SCALAR_ADD_VEC_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_add_vec) = []() -> bool { \ + register_scalar_add_vec(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_scalar_sub_vec(const std::string& deviceType, scalarVectorOpImpl impl); + +#define REGISTER_SCALAR_SUB_VEC_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_sub_vec) = []() -> bool { \ + register_scalar_sub_vec(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarConvertMontgomeryImpl = std::function; + + void register_scalar_convert_montgomery(const std::string& deviceType, scalarConvertMontgomeryImpl); + +#define REGISTER_CONVERT_MONTGOMERY_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_convert_mont) = []() -> bool { \ + register_scalar_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarMatrixOpImpl = std::function; + + void register_matrix_transpose(const std::string& deviceType, scalarMatrixOpImpl impl); + +#define REGISTER_MATRIX_TRANSPOSE_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_matrix_transpose) = []() -> bool { \ + register_matrix_transpose(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarBitReverseOpImpl = std::function; + + void register_scalar_bit_reverse(const std::string& deviceType, scalarBitReverseOpImpl); + +#define REGISTER_BIT_REVERSE_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_bit_reverse) = []() -> bool { \ + register_scalar_bit_reverse(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarSliceOpImpl = std::function; + + void register_slice(const std::string& deviceType, scalarSliceOpImpl); + +#define REGISTER_SLICE_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_slice) = []() -> bool { \ + register_slice(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarHighNonZeroIdxOpImpl = std::function; + + void register_highest_non_zero_idx(const std::string& deviceType, scalarHighNonZeroIdxOpImpl); + +#define REGISTER_HIGHEST_NON_ZERO_IDX_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_highest_non_zero_idx) = []() -> bool { \ + register_highest_non_zero_idx(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + template + eIcicleError polynomial_eval( + const T* coeffs, + uint64_t coeffs_size, + const T* domain, + uint64_t domain_size, + const VecOpsConfig& config, + T* evals /*OUT*/); + + using scalarPolyEvalImpl = std::function; + + void register_poly_eval(const std::string& deviceType, scalarPolyEvalImpl); + +#define REGISTER_POLYNOMIAL_EVAL(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_poly_eval) = []() -> bool { \ + register_poly_eval(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using scalarPolyDivImpl = std::function; + + void register_poly_division(const std::string& deviceType, scalarPolyDivImpl); + +#define REGISTER_POLYNOMIAL_DIVISION(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_poly_division) = []() -> bool { \ + register_poly_division(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + +#ifdef EXT_FIELD + using extFieldVectorOpImpl = std::function; + + using extFieldVectorOpImplInplaceA = std::function; + + void register_extension_vector_add(const std::string& deviceType, extFieldVectorOpImpl impl); + +#define REGISTER_VECTOR_ADD_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_add_ext_field) = []() -> bool { \ + register_extension_vector_add(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_extension_vector_accumulate(const std::string& deviceType, extFieldVectorOpImplInplaceA impl); + +#define REGISTER_VECTOR_ACCUMULATE_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_accumulate_ext_field) = []() -> bool { \ + register_extension_vector_accumulate(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_extension_vector_sub(const std::string& deviceType, extFieldVectorOpImpl impl); +#define REGISTER_VECTOR_SUB_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_sub_ext_field) = []() -> bool { \ + register_extension_vector_sub(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + void register_extension_vector_mul(const std::string& deviceType, extFieldVectorOpImpl impl); + +#define REGISTER_VECTOR_MUL_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_vec_mul_ext_field) = []() -> bool { \ + register_extension_vector_mul(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using extFieldConvertMontgomeryImpl = std::function; + + void register_extension_scalar_convert_montgomery(const std::string& deviceType, extFieldConvertMontgomeryImpl); + +#define REGISTER_CONVERT_MONTGOMERY_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_convert_mont_ext_field) = []() -> bool { \ + register_extension_scalar_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using extFieldMatrixOpImpl = std::function; + + void register_extension_matrix_transpose(const std::string& deviceType, extFieldMatrixOpImpl impl); + +#define REGISTER_MATRIX_TRANSPOSE_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_matrix_transpose_ext_field) = []() -> bool { \ + register_extension_matrix_transpose(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using extFieldBitReverseOpImpl = std::function; + + void register_extension_bit_reverse(const std::string& deviceType, extFieldBitReverseOpImpl); + +#define REGISTER_BIT_REVERSE_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_convert_mont) = []() -> bool { \ + register_extension_bit_reverse(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using extFieldSliceOpImpl = std::function; + + void register_extension_slice(const std::string& deviceType, extFieldSliceOpImpl); + +#define REGISTER_SLICE_EXT_FIELD_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_scalar_slice) = []() -> bool { \ + register_extension_slice(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +#endif // EXT_FIELD + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/config_extension.h b/icicle/include/icicle/config_extension.h new file mode 100644 index 000000000..cee6cc7ff --- /dev/null +++ b/icicle/include/icicle/config_extension.h @@ -0,0 +1,47 @@ +#pragma once + +#include +#include +#include +#include + +#include "icicle/errors.h" + +namespace icicle { + + class ConfigExtension + { + public: + using ConfigValue = std::variant; // int can represent enums + using ConfigMap = std::unordered_map; + + ConfigExtension() = default; + + // Copy constructor + ConfigExtension(const ConfigExtension& other) : extensions_{other.extensions_} {} + + template + void set(const std::string& key, T value) + { + extensions_[key] = value; + } + + template + T get(const std::string& key) const + { + auto it = extensions_.find(key); + if (it == extensions_.end()) { + THROW_ICICLE_ERR(eIcicleError::INVALID_ARGUMENT, "Key not found or type mismatch"); + } + return std::get(it->second); + } + + bool has(const std::string& key) const { return extensions_.find(key) != extensions_.end(); } + + // Clone method + ConfigExtension* clone() const { return new ConfigExtension(*this); } + + private: + ConfigMap extensions_; + }; +}; // namespace icicle \ No newline at end of file diff --git a/icicle/include/curves/affine.cuh b/icicle/include/icicle/curves/affine.h similarity index 80% rename from icicle/include/curves/affine.cuh rename to icicle/include/icicle/curves/affine.h index 2b1253e47..f15eee773 100644 --- a/icicle/include/curves/affine.cuh +++ b/icicle/include/icicle/curves/affine.h @@ -1,7 +1,9 @@ #pragma once -#include "gpu-utils/sharedmem.cuh" -#include "gpu-utils/modifiers.cuh" +#ifdef __CUDACC__ +#include "gpu-utils/sharedmem.h" +#endif // __CUDACC__ +#include "icicle/utils/modifiers.h" #include template @@ -30,6 +32,8 @@ class Affine return (xs.x == ys.x) && (xs.y == ys.y); } + friend HOST_DEVICE_INLINE bool operator!=(const Affine& xs, const Affine& ys) { return !(xs == ys); } + friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Affine& point) { os << "x: " << point.x << "; y: " << point.y; @@ -37,6 +41,7 @@ class Affine } }; +#ifdef __CUDACC__ template struct SharedMemory> { __device__ Affine* getPointer() @@ -45,3 +50,4 @@ struct SharedMemory> { return s_affine_; } }; +#endif // __CUDACC__ diff --git a/icicle/include/curves/curve_config.cuh b/icicle/include/icicle/curves/curve_config.h similarity index 67% rename from icicle/include/curves/curve_config.cuh rename to icicle/include/icicle/curves/curve_config.h index c9fe109b6..118a0a689 100644 --- a/icicle/include/curves/curve_config.cuh +++ b/icicle/include/icicle/curves/curve_config.h @@ -1,9 +1,7 @@ #pragma once -#ifndef CURVE_CONFIG_H -#define CURVE_CONFIG_H -#include "fields/id.h" -#include "curves/projective.cuh" +#include "icicle/fields/id.h" +#include "icicle/curves/projective.h" /** * @namespace curve_config @@ -12,23 +10,22 @@ * with the `-DCURVE` env variable passed during build. */ #if CURVE_ID == BN254 -#include "curves/params/bn254.cuh" +#include "icicle/curves/params/bn254.h" namespace curve_config = bn254; #elif CURVE_ID == BLS12_381 -#include "curves/params/bls12_381.cuh" +#include "icicle/curves/params/bls12_381.h" namespace curve_config = bls12_381; #elif CURVE_ID == BLS12_377 -#include "curves/params/bls12_377.cuh" +#include "icicle/curves/params/bls12_377.h" namespace curve_config = bls12_377; #elif CURVE_ID == BW6_761 -#include "curves/params/bw6_761.cuh" +#include "icicle/curves/params/bw6_761.h" namespace curve_config = bw6_761; #elif CURVE_ID == GRUMPKIN -#include "curves/params/grumpkin.cuh" +#include "icicle/curves/params/grumpkin.h" namespace curve_config = grumpkin; -#endif #endif \ No newline at end of file diff --git a/icicle/include/icicle/curves/montgomery_conversion.h b/icicle/include/icicle/curves/montgomery_conversion.h new file mode 100644 index 000000000..9d5e9d1eb --- /dev/null +++ b/icicle/include/icicle/curves/montgomery_conversion.h @@ -0,0 +1,91 @@ +#pragma once + +#include + +#include "icicle/errors.h" +#include "icicle/runtime.h" +#include "icicle/utils/utils.h" +#include "icicle/config_extension.h" + +#include "icicle/curves/affine.h" +#include "icicle/curves/projective.h" +#include "icicle/vec_ops.h" +#include "icicle/fields/field.h" +#include "icicle/curves/curve_config.h" + +using namespace curve_config; + +namespace icicle { + + /*************************** Backend registration ***************************/ + + using AffineConvertMontImpl = std::function; + + void register_affine_convert_montgomery(const std::string& deviceType, AffineConvertMontImpl); + +#define REGISTER_AFFINE_CONVERT_MONTGOMERY_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_affine_convert_mont) = []() -> bool { \ + register_affine_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using ProjectiveConvertMontImpl = std::function; + + void register_projective_convert_montgomery(const std::string& deviceType, ProjectiveConvertMontImpl); + +#define REGISTER_PROJECTIVE_CONVERT_MONTGOMERY_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_projective_convert_mont) = []() -> bool { \ + register_projective_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + +#ifdef G2 + using AffineG2ConvertMontImpl = std::function; + + void register_g2_affine_convert_montgomery(const std::string& deviceType, AffineG2ConvertMontImpl); + +#define REGISTER_AFFINE_G2_CONVERT_MONTGOMERY_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_affine_g2_convert_mont) = []() -> bool { \ + register_g2_affine_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } + + using ProjectiveG2ConvertMontImpl = std::function; + + void register_g2_projective_convert_montgomery(const std::string& deviceType, ProjectiveG2ConvertMontImpl); + +#define REGISTER_PROJECTIVE_G2_CONVERT_MONTGOMERY_BACKEND(DEVICE_TYPE, FUNC) \ + namespace { \ + static bool UNIQUE(_reg_projective_g2_convert_mont) = []() -> bool { \ + register_g2_projective_convert_montgomery(DEVICE_TYPE, FUNC); \ + return true; \ + }(); \ + } +#endif // G2 + +}; // namespace icicle diff --git a/icicle/include/icicle/curves/params/bls12_377.h b/icicle/include/icicle/curves/params/bls12_377.h new file mode 100644 index 000000000..da19ab2bd --- /dev/null +++ b/icicle/include/icicle/curves/params/bls12_377.h @@ -0,0 +1,54 @@ +#pragma once + +#include "icicle/curves/projective.h" +#include "icicle/fields/snark_fields/bls12_377_base.h" +#include "icicle/fields/snark_fields/bls12_377_scalar.h" +#include "icicle/fields/complex_extension.h" + +namespace bls12_377 { + struct G1; + typedef Field point_field_t; + typedef Projective projective_t; + typedef Affine affine_t; + + struct G2; + typedef ComplexExtensionField g2_point_field_t; + typedef Projective g2_projective_t; + typedef Affine g2_affine_t; + + // G1 and G2 generators + struct G1 { + static constexpr point_field_t gen_x = {0xb21be9ef, 0xeab9b16e, 0xffcd394e, 0xd5481512, 0xbd37cb5c, 0x188282c8, + 0xaa9d41bb, 0x85951e2c, 0xbf87ff54, 0xc8fc6225, 0xfe740a67, 0x008848de}; + static constexpr point_field_t gen_y = {0x559c8ea6, 0xfd82de55, 0x34a9591a, 0xc2fe3d36, 0x4fb82305, 0x6d182ad4, + 0xca3e52d9, 0xbd7fb348, 0x30afeec4, 0x1f674f5d, 0xc5102eff, 0x01914a69}; + static constexpr point_field_t weierstrass_b = {0x00000001, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + }; // G1 + + struct G2 { + static constexpr point_field_t g2_gen_x_re = {0x7c005196, 0x74e3e48f, 0xbb535402, 0x71889f52, + 0x57db6b9b, 0x7ea501f5, 0x203e5031, 0xc565f071, + 0xa3841d01, 0xc89630a2, 0x71c785fe, 0x018480be}; + static constexpr point_field_t g2_gen_x_im = {0x6ea16afe, 0xb26bfefa, 0xbff76fe6, 0x5cf89984, + 0x0799c9de, 0xe7223ece, 0x6651cecb, 0x532777ee, + 0xb1b140d5, 0x70dc5a51, 0xe7004031, 0x00ea6040}; + static constexpr point_field_t g2_gen_y_re = {0x09fd4ddf, 0xf0940944, 0x6d8c7c2e, 0xf2cf8888, + 0xf832d204, 0xe458c282, 0x74b49a58, 0xde03ed72, + 0xcbb2efb4, 0xd960736b, 0x5d446f7b, 0x00690d66}; + static constexpr point_field_t g2_gen_y_im = {0x85eb8f93, 0xd9a1cdd1, 0x5e52270b, 0x4279b83f, + 0xcee304c2, 0x2463b01a, 0x3d591bf1, 0x61ef11ac, + 0x151a70aa, 0x9e549da3, 0xd2835518, 0x00f8169f}; + static constexpr point_field_t weierstrass_b_g2_re = {0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + static constexpr point_field_t weierstrass_b_g2_im = {0x9999999a, 0x1c9ed999, 0x1ccccccd, 0x0dd39e5c, + 0x3c6bf800, 0x129207b6, 0xcd5fd889, 0xdc7b4f91, + 0x7460c589, 0x43bd0373, 0xdb0fd6f3, 0x010222f6}; + + static constexpr g2_point_field_t gen_x = {g2_gen_x_re, g2_gen_x_im}; + static constexpr g2_point_field_t gen_y = {g2_gen_y_re, g2_gen_y_im}; + static constexpr g2_point_field_t weierstrass_b = {weierstrass_b_g2_re, weierstrass_b_g2_im}; + }; +} // namespace bls12_377 diff --git a/icicle/include/icicle/curves/params/bls12_381.h b/icicle/include/icicle/curves/params/bls12_381.h new file mode 100644 index 000000000..7457bd49c --- /dev/null +++ b/icicle/include/icicle/curves/params/bls12_381.h @@ -0,0 +1,56 @@ +#pragma once + +#include "icicle/curves/projective.h" +#include "icicle/fields/snark_fields/bls12_381_base.h" +#include "icicle/fields/snark_fields/bls12_381_scalar.h" +#include "icicle/fields/complex_extension.h" + +namespace bls12_381 { + struct G1; + typedef Field point_field_t; + typedef Projective projective_t; + typedef Affine affine_t; + + struct G2; + typedef ComplexExtensionField g2_point_field_t; + typedef Projective g2_projective_t; + typedef Affine g2_affine_t; + + // G1 and G2 generators + struct G1 { + static constexpr point_field_t gen_x = {0xdb22c6bb, 0xfb3af00a, 0xf97a1aef, 0x6c55e83f, 0x171bac58, 0xa14e3a3f, + 0x9774b905, 0xc3688c4f, 0x4fa9ac0f, 0x2695638c, 0x3197d794, 0x17f1d3a7}; + static constexpr point_field_t gen_y = {0x46c5e7e1, 0x0caa2329, 0xa2888ae4, 0xd03cc744, 0x2c04b3ed, 0x00db18cb, + 0xd5d00af6, 0xfcf5e095, 0x741d8ae4, 0xa09e30ed, 0xe3aaa0f1, 0x08b3f481}; + static constexpr point_field_t weierstrass_b = {0x00000004, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + }; + + struct G2 { + static constexpr point_field_t g2_gen_x_re = {0xc121bdb8, 0xd48056c8, 0xa805bbef, 0x0bac0326, + 0x7ae3d177, 0xb4510b64, 0xfa403b02, 0xc6e47ad4, + 0x2dc51051, 0x26080527, 0xf08f0a91, 0x024aa2b2}; + static constexpr point_field_t g2_gen_x_im = {0x5d042b7e, 0xe5ac7d05, 0x13945d57, 0x334cf112, + 0xdc7f5049, 0xb5da61bb, 0x9920b61a, 0x596bd0d0, + 0x88274f65, 0x7dacd3a0, 0x52719f60, 0x13e02b60}; + static constexpr point_field_t g2_gen_y_re = {0x08b82801, 0xe1935486, 0x3baca289, 0x923ac9cc, + 0x5160d12c, 0x6d429a69, 0x8cbdd3a7, 0xadfd9baa, + 0xda2e351a, 0x8cc9cdc6, 0x727d6e11, 0x0ce5d527}; + static constexpr point_field_t g2_gen_y_im = {0xf05f79be, 0xaaa9075f, 0x5cec1da1, 0x3f370d27, + 0x572e99ab, 0x267492ab, 0x85a763af, 0xcb3e287e, + 0x2bc28b99, 0x32acd2b0, 0x2ea734cc, 0x0606c4a0}; + + static constexpr point_field_t weierstrass_b_g2_re = {0x00000004, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + static constexpr point_field_t weierstrass_b_g2_im = {0x00000004, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + + static constexpr g2_point_field_t gen_x = {g2_gen_x_re, g2_gen_x_im}; + static constexpr g2_point_field_t gen_y = {g2_gen_y_re, g2_gen_y_im}; + static constexpr g2_point_field_t weierstrass_b = {weierstrass_b_g2_re, weierstrass_b_g2_im}; + }; + +} // namespace bls12_381 diff --git a/icicle/include/icicle/curves/params/bn254.h b/icicle/include/icicle/curves/params/bn254.h new file mode 100644 index 000000000..b8095b53f --- /dev/null +++ b/icicle/include/icicle/curves/params/bn254.h @@ -0,0 +1,48 @@ +#pragma once + +#include "icicle/curves/projective.h" +#include "icicle/fields/snark_fields/bn254_base.h" +#include "icicle/fields/snark_fields/bn254_scalar.h" +#include "icicle/fields/complex_extension.h" + +namespace bn254 { + struct G1; + typedef Field point_field_t; + typedef Projective projective_t; + typedef Affine affine_t; + + struct G2; + typedef ComplexExtensionField g2_point_field_t; + typedef Projective g2_projective_t; + typedef Affine g2_affine_t; + + // G1 and G2 generators + struct G1 { + static constexpr point_field_t gen_x = {0x00000001, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + static constexpr point_field_t gen_y = {0x00000002, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + static constexpr point_field_t weierstrass_b = {0x00000003, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + }; // G1 + + struct G2 { + static constexpr point_field_t g2_gen_x_re = {0xd992f6ed, 0x46debd5c, 0xf75edadd, 0x674322d4, + 0x5e5c4479, 0x426a0066, 0x121f1e76, 0x1800deef}; + static constexpr point_field_t g2_gen_x_im = {0xaef312c2, 0x97e485b7, 0x35a9e712, 0xf1aa4933, + 0x31fb5d25, 0x7260bfb7, 0x920d483a, 0x198e9393}; + static constexpr point_field_t g2_gen_y_re = {0x66fa7daa, 0x4ce6cc01, 0x0c43d37b, 0xe3d1e769, + 0x8dcb408f, 0x4aab7180, 0xdb8c6deb, 0x12c85ea5}; + static constexpr point_field_t g2_gen_y_im = {0xd122975b, 0x55acdadc, 0x70b38ef3, 0xbc4b3133, + 0x690c3395, 0xec9e99ad, 0x585ff075, 0x090689d0}; + static constexpr point_field_t weierstrass_b_g2_re = {0x24a138e5, 0x3267e6dc, 0x59dbefa3, 0xb5b4c5e5, + 0x1be06ac3, 0x81be1899, 0xceb8aaae, 0x2b149d40}; + static constexpr point_field_t weierstrass_b_g2_im = {0x85c315d2, 0xe4a2bd06, 0xe52d1852, 0xa74fa084, + 0xeed8fdf4, 0xcd2cafad, 0x3af0fed4, 0x009713b0}; + + static constexpr g2_point_field_t gen_x = {g2_gen_x_re, g2_gen_x_im}; + static constexpr g2_point_field_t gen_y = {g2_gen_y_re, g2_gen_y_im}; + static constexpr g2_point_field_t weierstrass_b = {weierstrass_b_g2_re, weierstrass_b_g2_im}; + }; + +} // namespace bn254 diff --git a/icicle/include/icicle/curves/params/bw6_761.h b/icicle/include/icicle/curves/params/bw6_761.h new file mode 100644 index 000000000..c0a1fcf5e --- /dev/null +++ b/icicle/include/icicle/curves/params/bw6_761.h @@ -0,0 +1,48 @@ +#pragma once + +#include "icicle/curves/projective.h" +#include "icicle/fields/snark_fields/bw6_761_base.h" +#include "icicle/fields/snark_fields/bw6_761_scalar.h" + +namespace bw6_761 { + struct G1; + typedef Field point_field_t; + typedef Projective projective_t; + typedef Affine affine_t; + + struct G2; + typedef Field g2_point_field_t; + typedef Projective g2_projective_t; + typedef Affine g2_affine_t; + + // G1 and G2 generators + struct G1 { + static constexpr point_field_t gen_x = {0x66e5b43d, 0x4088f3af, 0xa6af603f, 0x055928ac, 0x56133e82, 0x6750dd03, + 0x280ca27f, 0x03758f9a, 0xc9ea0971, 0x5bd71fa0, 0x47729b90, 0xa17a54ce, + 0x94c2e746, 0x11dbfcd2, 0xc15520ac, 0x79017ffa, 0x85f56fc7, 0xee05c54b, + 0x551b27f0, 0xe6a0cfb7, 0xa477beae, 0xb277ce98, 0x0ea190c8, 0x01075b02}; + static constexpr point_field_t gen_y = {0xb4e95363, 0xbafc8f2d, 0x0b20d2a1, 0xad1cb2be, 0xcad0fb93, 0xb2b08119, + 0xb3053253, 0x9f9df141, 0x6fc2cdd4, 0xbe3fb90b, 0x717a4c55, 0xcc685d31, + 0x71b5b806, 0xc5b8fa17, 0xaf7e0dba, 0x265909f1, 0xa2e573a3, 0x1a7348d2, + 0x884c9ec6, 0x0f952589, 0x45cc2a42, 0xe6fd637b, 0x0a6fc574, 0x0058b84e}; + static constexpr point_field_t weierstrass_b = { + 0x0000008a, 0xf49d0000, 0x70000082, 0xe6913e68, 0xeaf0a437, 0x160cf8ae, 0x5667a8f8, 0x98a116c2, + 0x73ebff2e, 0x71dcd3dc, 0x12f9fd90, 0x8689c8ed, 0x25b42304, 0x03cebaff, 0xe584e919, 0x707ba638, + 0x8087be41, 0x528275ef, 0x81d14688, 0xb926186a, 0x04faff3e, 0xd187c940, 0xfb83ce0a, 0x0122e824}; + }; + + struct G2 { + static constexpr g2_point_field_t gen_x = {0xcd025f1c, 0xa830c194, 0xe1bf995b, 0x6410cf4f, 0xc2ad54b0, 0x00e96efb, + 0x3cd208d7, 0xce6948cb, 0x00e1b6ba, 0x963317a3, 0xac70e7c7, 0xc5bbcae9, + 0xf09feb58, 0x734ec3f1, 0xab3da268, 0x26b41c5d, 0x13890f6d, 0x4c062010, + 0xc5a7115f, 0xd61053aa, 0x69d660f9, 0xc852a82e, 0x41d9b816, 0x01101332}; + static constexpr g2_point_field_t gen_y = {0x28c73b61, 0xeb70a167, 0xf9eac689, 0x91ec0594, 0x3c5a02a5, 0x58aa2d3a, + 0x504affc7, 0x3ea96fcd, 0xffa82300, 0x8906c170, 0xd2c712b8, 0x64f293db, + 0x33293fef, 0x94c97eb7, 0x0b95a59c, 0x0a1d86c8, 0x53ffe316, 0x81a78e27, + 0xcec2181c, 0x26b7cf9a, 0xe4b6d2dc, 0x8179eb10, 0x7761369f, 0x0017c335}; + static constexpr g2_point_field_t weierstrass_b = { + 0x00000004, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + }; +} // namespace bw6_761 diff --git a/icicle/include/icicle/curves/params/grumpkin.h b/icicle/include/icicle/curves/params/grumpkin.h new file mode 100644 index 000000000..a468fd911 --- /dev/null +++ b/icicle/include/icicle/curves/params/grumpkin.h @@ -0,0 +1,22 @@ +#pragma once + +#include "icicle/curves/projective.h" +#include "icicle/fields/snark_fields/grumpkin_base.h" +#include "icicle/fields/snark_fields/grumpkin_scalar.h" + +namespace grumpkin { + struct G1; + typedef Field point_field_t; + typedef Projective projective_t; + typedef Affine affine_t; + // G1 generator + struct G1 { + static constexpr point_field_t gen_x = {0x00000001, 0x00000000, 0x00000000, 0x00000000, + 0x00000000, 0x00000000, 0x00000000, 0x00000000}; + static constexpr point_field_t gen_y = {0x823f272c, 0x833fc48d, 0xf1181294, 0x2d270d45, + 0x6a45d63, 0xcf135e75, 0x00000002, 0x00000000}; + + static constexpr point_field_t weierstrass_b = {0xeffffff0, 0x43e1f593, 0x79b97091, 0x2833e848, + 0x8181585d, 0xb85045b6, 0xe131a029, 0x30644e72}; + }; // G1 +} // namespace grumpkin diff --git a/icicle/include/icicle/curves/projective.h b/icicle/include/icicle/curves/projective.h new file mode 100644 index 000000000..c9ea1b898 --- /dev/null +++ b/icicle/include/icicle/curves/projective.h @@ -0,0 +1,249 @@ +#pragma once + +#ifdef __CUDACC__ +#include "gpu-utils/sharedmem.h" +#endif // __CUDACC__ + +#include "icicle/utils/modifiers.h" +#include "icicle/curves/affine.h" + +template +class Projective +{ + friend Affine; + +public: + typedef Affine Aff; + typedef SCALAR_FF Scalar; + + static constexpr unsigned SCALAR_FF_NBITS = SCALAR_FF::NBITS; + static constexpr unsigned FF_NBITS = FF::NBITS; + + FF x; + FF y; + FF z; + static HOST_DEVICE_INLINE Projective zero() { return {FF::zero(), FF::one(), FF::zero()}; } + + static HOST_DEVICE_INLINE Affine to_affine(const Projective& point) + { + FF denom = FF::inverse(point.z); + return {point.x * denom, point.y * denom}; + } + HOST_DEVICE_INLINE Affine to_affine() { return to_affine(*this); } + + static HOST_DEVICE_INLINE Projective from_affine(const Affine& point) + { + return point == Affine::zero() ? zero() : Projective{point.x, point.y, FF::one()}; + } + + static HOST_DEVICE_INLINE Projective to_montgomery(const Projective& point) + { + return {FF::to_montgomery(point.x), FF::to_montgomery(point.y), FF::to_montgomery(point.z)}; + } + + static HOST_DEVICE_INLINE Projective from_montgomery(const Projective& point) + { + return {FF::from_montgomery(point.x), FF::from_montgomery(point.y), FF::from_montgomery(point.z)}; + } + + static HOST_DEVICE_INLINE Projective generator() { return {Gen::gen_x, Gen::gen_y, FF::one()}; } + + static HOST_DEVICE_INLINE Projective neg(const Projective& point) { return {point.x, FF::neg(point.y), point.z}; } + + static HOST_DEVICE_INLINE Projective dbl(const Projective& point) + { + const FF X = point.x; + const FF Y = point.y; + const FF Z = point.z; + + // TODO: Change to efficient dbl once implemented for field.cuh + FF t0 = FF::sqr(Y); // 1. t0 ← Y · Y + FF Z3 = t0 + t0; // 2. Z3 ← t0 + t0 + Z3 = Z3 + Z3; // 3. Z3 ← Z3 + Z3 + Z3 = Z3 + Z3; // 4. Z3 ← Z3 + Z3 + FF t1 = Y * Z; // 5. t1 ← Y · Z + FF t2 = FF::sqr(Z); // 6. t2 ← Z · Z + t2 = FF::template mul_unsigned<3>(FF::template mul_const(t2)); // 7. t2 ← b3 · t2 + FF X3 = t2 * Z3; // 8. X3 ← t2 · Z3 + FF Y3 = t0 + t2; // 9. Y3 ← t0 + t2 + Z3 = t1 * Z3; // 10. Z3 ← t1 · Z3 + t1 = t2 + t2; // 11. t1 ← t2 + t2 + t2 = t1 + t2; // 12. t2 ← t1 + t2 + t0 = t0 - t2; // 13. t0 ← t0 − t2 + Y3 = t0 * Y3; // 14. Y3 ← t0 · Y3 + Y3 = X3 + Y3; // 15. Y3 ← X3 + Y3 + t1 = X * Y; // 16. t1 ← X · Y + X3 = t0 * t1; // 17. X3 ← t0 · t1 + X3 = X3 + X3; // 18. X3 ← X3 + X3 + return {X3, Y3, Z3}; + } + + friend HOST_DEVICE_INLINE Projective operator+(Projective p1, const Projective& p2) + { + const FF X1 = p1.x; // < 2 + const FF Y1 = p1.y; // < 2 + const FF Z1 = p1.z; // < 2 + const FF X2 = p2.x; // < 2 + const FF Y2 = p2.y; // < 2 + const FF Z2 = p2.z; // < 2 + const FF t00 = X1 * X2; // t00 ← X1 · X2 < 2 + const FF t01 = Y1 * Y2; // t01 ← Y1 · Y2 < 2 + const FF t02 = Z1 * Z2; // t02 ← Z1 · Z2 < 2 + const FF t03 = X1 + Y1; // t03 ← X1 + Y1 < 4 + const FF t04 = X2 + Y2; // t04 ← X2 + Y2 < 4 + const FF t05 = t03 * t04; // t03 ← t03 · t04 < 3 + const FF t06 = t00 + t01; // t06 ← t00 + t01 < 4 + const FF t07 = t05 - t06; // t05 ← t05 − t06 < 2 + const FF t08 = Y1 + Z1; // t08 ← Y1 + Z1 < 4 + const FF t09 = Y2 + Z2; // t09 ← Y2 + Z2 < 4 + const FF t10 = t08 * t09; // t10 ← t08 · t09 < 3 + const FF t11 = t01 + t02; // t11 ← t01 + t02 < 4 + const FF t12 = t10 - t11; // t12 ← t10 − t11 < 2 + const FF t13 = X1 + Z1; // t13 ← X1 + Z1 < 4 + const FF t14 = X2 + Z2; // t14 ← X2 + Z2 < 4 + const FF t15 = t13 * t14; // t15 ← t13 · t14 < 3 + const FF t16 = t00 + t02; // t16 ← t00 + t02 < 4 + const FF t17 = t15 - t16; // t17 ← t15 − t16 < 2 + const FF t18 = t00 + t00; // t18 ← t00 + t00 < 2 + const FF t19 = t18 + t00; // t19 ← t18 + t00 < 2 + const FF t20 = + FF::template mul_unsigned<3>(FF::template mul_const(t02)); // t20 ← b3 · t02 < 2 + const FF t21 = t01 + t20; // t21 ← t01 + t20 < 2 + const FF t22 = t01 - t20; // t22 ← t01 − t20 < 2 + const FF t23 = + FF::template mul_unsigned<3>(FF::template mul_const(t17)); // t23 ← b3 · t17 < 2 + const auto t24 = FF::mul_wide(t12, t23); // t24 ← t12 · t23 < 2 + const auto t25 = FF::mul_wide(t07, t22); // t25 ← t07 · t22 < 2 + const FF X3 = FF::reduce(t25 - t24); // X3 ← t25 − t24 < 2 + const auto t27 = FF::mul_wide(t23, t19); // t27 ← t23 · t19 < 2 + const auto t28 = FF::mul_wide(t22, t21); // t28 ← t22 · t21 < 2 + const FF Y3 = FF::reduce(t28 + t27); // Y3 ← t28 + t27 < 2 + const auto t30 = FF::mul_wide(t19, t07); // t30 ← t19 · t07 < 2 + const auto t31 = FF::mul_wide(t21, t12); // t31 ← t21 · t12 < 2 + const FF Z3 = FF::reduce(t31 + t30); // Z3 ← t31 + t30 < 2 + return {X3, Y3, Z3}; + } + + friend HOST_DEVICE_INLINE Projective operator-(Projective p1, const Projective& p2) { return p1 + neg(p2); } + + friend HOST_DEVICE_INLINE Projective operator+(Projective p1, const Affine& p2) + { + const FF X1 = p1.x; // < 2 + const FF Y1 = p1.y; // < 2 + const FF Z1 = p1.z; // < 2 + const FF X2 = p2.x; // < 2 + const FF Y2 = p2.y; // < 2 + const FF t00 = X1 * X2; // t00 ← X1 · X2 < 2 + const FF t01 = Y1 * Y2; // t01 ← Y1 · Y2 < 2 + const FF t02 = Z1; // t02 ← Z1 < 2 + const FF t03 = X1 + Y1; // t03 ← X1 + Y1 < 4 + const FF t04 = X2 + Y2; // t04 ← X2 + Y2 < 4 + const FF t05 = t03 * t04; // t03 ← t03 · t04 < 3 + const FF t06 = t00 + t01; // t06 ← t00 + t01 < 4 + const FF t07 = t05 - t06; // t05 ← t05 − t06 < 2 + const FF t08 = Y1 + Z1; // t08 ← Y1 + Z1 < 4 + const FF t09 = Y2 + FF::one(); // t09 ← Y2 + 1 < 4 + const FF t10 = t08 * t09; // t10 ← t08 · t09 < 3 + const FF t11 = t01 + t02; // t11 ← t01 + t02 < 4 + const FF t12 = t10 - t11; // t12 ← t10 − t11 < 2 + const FF t13 = X1 + Z1; // t13 ← X1 + Z1 < 4 + const FF t14 = X2 + FF::one(); // t14 ← X2 + 1 < 4 + const FF t15 = t13 * t14; // t15 ← t13 · t14 < 3 + const FF t16 = t00 + t02; // t16 ← t00 + t02 < 4 + const FF t17 = t15 - t16; // t17 ← t15 − t16 < 2 + const FF t18 = t00 + t00; // t18 ← t00 + t00 < 2 + const FF t19 = t18 + t00; // t19 ← t18 + t00 < 2 + const FF t20 = + FF::template mul_unsigned<3>(FF::template mul_const(t02)); // t20 ← b3 · t02 < 2 + const FF t21 = t01 + t20; // t21 ← t01 + t20 < 2 + const FF t22 = t01 - t20; // t22 ← t01 − t20 < 2 + const FF t23 = + FF::template mul_unsigned<3>(FF::template mul_const(t17)); // t23 ← b3 · t17 < 2 + const auto t24 = FF::mul_wide(t12, t23); // t24 ← t12 · t23 < 2 + const auto t25 = FF::mul_wide(t07, t22); // t25 ← t07 · t22 < 2 + const FF X3 = FF::reduce(t25 - t24); // X3 ← t25 − t24 < 2 + const auto t27 = FF::mul_wide(t23, t19); // t27 ← t23 · t19 < 2 + const auto t28 = FF::mul_wide(t22, t21); // t28 ← t22 · t21 < 2 + const FF Y3 = FF::reduce(t28 + t27); // Y3 ← t28 + t27 < 2 + const auto t30 = FF::mul_wide(t19, t07); // t30 ← t19 · t07 < 2 + const auto t31 = FF::mul_wide(t21, t12); // t31 ← t21 · t12 < 2 + const FF Z3 = FF::reduce(t31 + t30); // Z3 ← t31 + t30 < 2 + return {X3, Y3, Z3}; + } + + friend HOST_DEVICE_INLINE Projective operator-(Projective p1, const Affine& p2) + { + return p1 + Affine::neg(p2); + } + + friend HOST_DEVICE_INLINE Projective operator*(SCALAR_FF scalar, const Projective& point) + { + Projective res = zero(); +#ifdef __CUDA_ARCH__ + UNROLL +#endif + for (int i = 0; i < SCALAR_FF::NBITS; i++) { + if (i > 0) { res = dbl(res); } + if (scalar.get_scalar_digit(SCALAR_FF::NBITS - i - 1, 1)) { res = res + point; } + } + return res; + } + + friend HOST_DEVICE_INLINE Projective operator*(const Projective& point, SCALAR_FF scalar) { return scalar * point; } + + friend HOST_DEVICE_INLINE bool operator==(const Projective& p1, const Projective& p2) + { + return (p1.x * p2.z == p2.x * p1.z) && (p1.y * p2.z == p2.y * p1.z); + } + + friend HOST_DEVICE_INLINE bool operator!=(const Projective& p1, const Projective& p2) { return !(p1 == p2); } + + friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Projective& point) + { + os << "Point { x: " << point.x << "; y: " << point.y << "; z: " << point.z << " }"; + return os; + } + + static HOST_DEVICE_INLINE bool is_zero(const Projective& point) + { + return point.x == FF::zero() && point.y != FF::zero() && point.z == FF::zero(); + } + + static HOST_DEVICE_INLINE bool is_on_curve(const Projective& point) + { + if (is_zero(point)) return true; + bool eq_holds = + (FF::template mul_const(FF::sqr(point.z) * point.z) + FF::sqr(point.x) * point.x == + point.z * FF::sqr(point.y)); + return point.z != FF::zero() && eq_holds; + } + + static HOST_INLINE Projective rand_host() + { + SCALAR_FF rand_scalar = SCALAR_FF::rand_host(); + return rand_scalar * generator(); + } + + static void rand_host_many(Projective* out, int size) + { + for (int i = 0; i < size; i++) + out[i] = (i % size < 100) ? rand_host() : out[i - 100]; + } + + static void rand_host_many(Affine* out, int size) + { + for (int i = 0; i < size; i++) + out[i] = (i % size < 100) ? to_affine(rand_host()) : out[i - 100]; + } +}; + +#ifdef __CUDACC__ +template +struct SharedMemory> { + __device__ Projective* getPointer() + { + extern __shared__ Projective s_projective_[]; + return s_projective_; + } +}; +#endif // __CUDACC__ diff --git a/icicle/include/icicle/device.h b/icicle/include/icicle/device.h new file mode 100644 index 000000000..3128d13c9 --- /dev/null +++ b/icicle/include/icicle/device.h @@ -0,0 +1,58 @@ +#pragma once + +#include + +namespace icicle { + +#define MAX_TYPE_LEN 64 + + /** + * @brief Structure representing a device. + */ + struct Device { + char type[MAX_TYPE_LEN]; // Type of the device, such as "CPU" or "CUDA" + int id; // Unique identifier for the device (e.g., GPU-2) + + Device(const char* _type, int _id = 0) : id{_id} { copy_str(type, _type); } + Device(const std::string& _type, int _id = 0) : Device(_type.c_str(), _id) {} + void copy(const Device& other) + { + copy_str(type, other.type); + id = other.id; + } + void copy_str(char* dst, const char* src) + { + std::strncpy(dst, src, sizeof(type) - 1); + dst[sizeof(type) - 1] = 0; + } + const Device& operator=(const Device& other) + { + copy(other); + return *this; + } + Device(const Device& other) { copy(other); } + Device(const Device&&) = delete; + bool operator==(const Device& other) const + { + return (id == other.id) && (0 == strncmp(type, other.type, MAX_TYPE_LEN)); + } + bool operator!=(const Device& other) const { return !(*this == other); } + + friend std::ostream& operator<<(std::ostream& os, const Device& device) + { + os << "Device(type: " << device.type << ", id: " << device.id << ")"; + return os; + } + }; + + /** + * @brief Structure to hold device properties. + */ + struct DeviceProperties { + bool using_host_memory; // Indicates if the device uses host memory + int num_memory_regions; // Number of memory regions available on the device + bool supports_pinned_memory; // Indicates if the device supports pinned memory + // Add more properties as needed + }; + +} // namespace icicle diff --git a/icicle/include/icicle/device_api.h b/icicle/include/icicle/device_api.h new file mode 100644 index 000000000..b4356b0f0 --- /dev/null +++ b/icicle/include/icicle/device_api.h @@ -0,0 +1,246 @@ +#pragma once + +#include +#include +#include +#include + +#include "icicle/utils/utils.h" +#include "icicle/device.h" +#include "icicle/errors.h" +#include "icicle/memory_tracker.h" + +namespace icicle { + + /** + * @brief Enum for specifying the direction of data copy. + */ + enum eCopyDirection { HostToDevice, DeviceToHost, DeviceToDevice }; + + /** + * @brief Typedef for an abstract stream used for asynchronous operations. + */ + typedef void* icicleStreamHandle; + + /** + * @brief Abstract class representing the API for device operations. + */ + class DeviceAPI + { + public: + /** + * @brief Virtual destructor for DeviceAPI. + */ + virtual ~DeviceAPI() {} + + /** + * @brief Set active device for thread + * + * @param device The device + * @return eIcicleError Status of the device set + */ + virtual eIcicleError set_device(const Device& device) = 0; + + /** + * @brief Get number of devices for this type + * + * @param device_count number of available devices of this type (output param) + * @return eIcicleError Status of the device set + */ + virtual eIcicleError get_device_count(int& device_count) const = 0; + + // Memory management + + /** + * @brief Allocates memory on the specified device. + * + * @param ptr Pointer to the allocated memory. + * @param size Size of the memory to allocate. + * @return eIcicleError Status of the memory allocation. + */ + virtual eIcicleError allocate_memory(void** ptr, size_t size) const = 0; + + /** + * @brief Asynchronously allocates memory on the specified device. + * + * @param ptr Pointer to the allocated memory. + * @param size Size of the memory to allocate. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory allocation. + */ + virtual eIcicleError allocate_memory_async(void** ptr, size_t size, icicleStreamHandle stream) const = 0; + + /** + * @brief Frees memory on the specified device. + * + * @param ptr Pointer to the memory to free. + * @return eIcicleError Status of the memory deallocation. + */ + virtual eIcicleError free_memory(void* ptr) const = 0; + + /** + * @brief Asynchronously frees memory on the specified device. + * + * @param ptr Pointer to the memory to free. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory deallocation. + */ + virtual eIcicleError free_memory_async(void* ptr, icicleStreamHandle stream) const = 0; + + /** + * @brief Gets the total and available memory on the specified device. + * + * @param total Total memory available on the device (output parameter). + * @param free Available memory on the device (output parameter). + * @return eIcicleError Status of the memory query. + */ + virtual eIcicleError get_available_memory(size_t& total /*OUT*/, size_t& free /*OUT*/) const = 0; + + /** + * @brief Sets memory on the specified device to a given value. + * + * @param ptr Pointer to the memory. + * @param value Value to set. + * @param size Size of the memory to set. + * @return eIcicleError Status of the memory set. + */ + virtual eIcicleError memset(void* ptr, int value, size_t size) const = 0; + + /** + * @brief Asynchronously sets memory on the specified device to a given value. + * + * @param ptr Pointer to the memory. + * @param value Value to set. + * @param size Size of the memory to set. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory set. + */ + virtual eIcicleError memset_async(void* ptr, int value, size_t size, icicleStreamHandle stream) const = 0; + + // Data transfer + + /** + * @brief Copies data between host and device or between devices. + * + * @param dst Destination pointer. + * @param src Source pointer. + * @param size Size of the data to copy. + * @param direction Direction of the data copy (HostToDevice, DeviceToHost, DeviceToDevice). + * @return eIcicleError Status of the data copy. + */ + virtual eIcicleError copy(void* dst, const void* src, size_t size, eCopyDirection direction) const = 0; + + /** + * @brief Asynchronously copies data between host and device or between devices. + * + * @param dst Destination pointer. + * @param src Source pointer. + * @param size Size of the data to copy. + * @param direction Direction of the data copy (HostToDevice, DeviceToHost, DeviceToDevice). + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the data copy. + */ + virtual eIcicleError + copy_async(void* dst, const void* src, size_t size, eCopyDirection direction, icicleStreamHandle stream) const = 0; + + // Synchronization + + /** + * @brief Synchronizes the specified device or stream. + * + * @param stream The stream to synchronize (nullptr for device synchronization). + * @return eIcicleError Status of the synchronization. + */ + virtual eIcicleError synchronize(icicleStreamHandle stream = nullptr) const = 0; + + // Stream management + + /** + * @brief Creates a stream on the specified device. + * + * @param stream Pointer to the created stream. + * @return eIcicleError Status of the stream creation. + */ + virtual eIcicleError create_stream(icicleStreamHandle* stream) const = 0; + + /** + * @brief Destroys a stream on the specified device. + * + * @param stream The stream to destroy. + * @return eIcicleError Status of the stream destruction. + */ + virtual eIcicleError destroy_stream(icicleStreamHandle stream) const = 0; + + /** + * @brief Retrieves the properties of the specified device. + * + * @param properties Structure to be filled with device properties. + * @return eIcicleError Status of the properties query. + */ + virtual eIcicleError get_device_properties(DeviceProperties& properties) const = 0; + + private: + static MemoryTracker sMemTracker; // tracks memory allocations and can find device given address + thread_local static Device sCurDevice; // device that is currently active for this thread + thread_local static const DeviceAPI* sCurDeviceAPI; // API for the currently active device of this thread + + public: + static eIcicleError set_thread_local_device(const Device& device); + static const Device& get_thread_local_device(); + static const DeviceAPI* get_thread_local_deviceAPI(); + static MemoryTracker& get_global_memory_tracker() { return sMemTracker; } + }; + + /** + * @brief Retrieve a DeviceAPI instance based on the device. + * + * @param deviceType The device type to register the DeviceAPI for. + * @param api An instance of the derived api type to be used as deviceAPI interface. + */ + void register_deviceAPI(const std::string& deviceType, std::shared_ptr api); + + /** + * @brief Register DeviceAPI instance for a device type. + * + * @param device The device to create the DeviceAPI instance for. + * @return DeviceAPI* Pointer to the created DeviceAPI instance. + */ + DeviceAPI* get_deviceAPI(const Device& device); + + /** + * @brief Retrieve a vector of registered device types. + * + * @return A vector of registered device types. + */ + std::vector get_registered_devices_list(); + + // Function to get registered devices as a comma-separated string + eIcicleError get_registered_devices(char* output, size_t output_size); + + /** + * @brief Check if a given device type is registered. + * + * @param device_type The device type to check. + * @return true if the device type is registered, false otherwise. + */ + bool is_device_registered(const char* device_type); + + /** + * Device API registration macro. + * Usage: + * (1) implement the interface: + * Class MyDeviceAPI : public icicle::DeviceAPI {...} + * (1) register: + * REGISTER_DEVICE_API("MyDevice", MyDeviceAPI); + */ + +#define REGISTER_DEVICE_API(DEVICE_TYPE, API_CLASS) \ + namespace { \ + static bool UNIQUE(_reg_device_##API_CLASS) = []() -> bool { \ + std::shared_ptr apiInstance = std::make_shared(); \ + register_deviceAPI(DEVICE_TYPE, apiInstance); \ + return true; \ + }(); \ + } + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/dispatcher.h b/icicle/include/icicle/dispatcher.h new file mode 100644 index 000000000..0e28a8346 --- /dev/null +++ b/icicle/include/icicle/dispatcher.h @@ -0,0 +1,123 @@ +#pragma once + +#include "icicle/utils/log.h" +#include "icicle/errors.h" +#include "icicle/device.h" +#include "icicle/device_api.h" +#include +#include +#include +#include + +using namespace icicle; + +// Template function to demangle the name of the type +template +std::string demangle() +{ + int status = -4; + std::unique_ptr res{ + abi::__cxa_demangle(typeid(T).name(), nullptr, nullptr, &status), std::free}; + + return (status == 0) ? res.get() : typeid(T).name(); +} + +// Generalized executor-dispatcher template +template +class tIcicleExecuteDispatcher +{ + std::unordered_map apiMap; + +public: + static tIcicleExecuteDispatcher& Global() + { + static tIcicleExecuteDispatcher instance; + return instance; + } + + void _register(const std::string& deviceType, FuncType func) + { + if (apiMap.find(deviceType) != apiMap.end()) { + THROW_ICICLE_ERR( + eIcicleError::INVALID_DEVICE, + std::string("Attempting to register a duplicate ") + api_name + " operation for device type: " + deviceType); + } + apiMap[deviceType] = func; + } + + template + static auto execute(Args&&... args) -> decltype(auto) + { + const Device& device = DeviceAPI::get_thread_local_device(); + auto& apiMap = Global().apiMap; + auto it = apiMap.find(device.type); + if (it != apiMap.end()) { + return it->second(device, std::forward(args)...); + } else { + THROW_ICICLE_ERR( + eIcicleError::INVALID_DEVICE, std::string(api_name) + " operation not supported on device " + device.type); + } + return eIcicleError::SUCCESS; + } +}; + +// Instantiate a dispatcher class and the corresponding registration function +#define ICICLE_DISPATCHER_INST(dispatcher_class_name, api_name, type) \ + constexpr char ST_name_##api_name[]{#api_name}; \ + using dispatcher_class_name = tIcicleExecuteDispatcher; \ + void register_##api_name(const std::string& deviceType, type impl) \ + { \ + ICICLE_LOG_VERBOSE << " Registering API: device=" << deviceType << ", api=" << #api_name << "<" \ + << demangle() << ">"; \ + dispatcher_class_name::Global()._register(deviceType, impl); \ + } + +/********************************************************************************/ + +// Generalized constructor-dispatcher type template +template +class tIcicleObjectDispatcher +{ + std::unordered_map> apiMap; + +public: + static tIcicleObjectDispatcher& Global() + { + static tIcicleObjectDispatcher instance; + return instance; + } + + void _register(const std::string& deviceType, std::shared_ptr factory) + { + if (apiMap.find(deviceType) != apiMap.end()) { + THROW_ICICLE_ERR( + eIcicleError::INVALID_DEVICE, + std::string("Attempting to register a duplicate ") + api_name + " operation for device type: " + deviceType); + } + apiMap[deviceType] = factory; + } + + static const std::shared_ptr get_factory() + { + const Device& device = DeviceAPI::get_thread_local_device(); + auto& apiMap = Global().apiMap; + auto it = apiMap.find(device.type); + if (it != apiMap.end()) { + return it->second; + } else { + THROW_ICICLE_ERR(eIcicleError::INVALID_DEVICE, std::string(api_name) + " not supported on device " + device.type); + } + return nullptr; + } +}; + +// Instantiate a dispatcher class and the corresponding registration function +#define ICICLE_OBJECT_DISPATCHER_INST(dispatcher_class_name, api_name, type) \ + constexpr char ST_name_##api_name[]{#api_name}; \ + using dispatcher_class_name = tIcicleObjectDispatcher; \ + void register_##api_name(const std::string& deviceType, std::shared_ptr factory) \ + { \ + ICICLE_LOG_VERBOSE << " Registering API: device=" << deviceType << ", api=" << #api_name << "<" \ + << demangle << ">"; \ + dispatcher_class_name::Global()._register(deviceType, factory); \ + } diff --git a/icicle/include/icicle/errors.h b/icicle/include/icicle/errors.h new file mode 100644 index 000000000..b0bb50444 --- /dev/null +++ b/icicle/include/icicle/errors.h @@ -0,0 +1,127 @@ +#pragma once + +#include +#include +#include +#include "icicle/utils/log.h" + +namespace icicle { + + /** + * @brief Enum representing various error codes for Icicle library operations. + */ + enum class eIcicleError { + SUCCESS = 0, ///< Operation completed successfully + INVALID_DEVICE, ///< The specified device is invalid + OUT_OF_MEMORY, ///< Memory allocation failed due to insufficient memory + INVALID_POINTER, ///< The specified pointer is invalid + ALLOCATION_FAILED, ///< Memory allocation failed + DEALLOCATION_FAILED, ///< Memory deallocation failed + COPY_FAILED, ///< Data copy operation failed + SYNCHRONIZATION_FAILED, ///< Device synchronization failed + STREAM_CREATION_FAILED, ///< Stream creation failed + STREAM_DESTRUCTION_FAILED, ///< Stream destruction failed + API_NOT_IMPLEMENTED, ///< The API is not implemented for a device + INVALID_ARGUMENT, ///< Invalid argument passed + BACKEND_LOAD_FAILED, ///< Failed to load the backend + LICENSE_CHECK_ERROR, ////< Failed to check license or invalid license + UNKNOWN_ERROR, ///< An unknown error occurred + }; + + /** + * @brief Returns a human-readable string representation of an eIcicleError. + * + * @param error The eIcicleError to get the string representation for. + * @return const char* A string describing the error. + */ + static const char* get_error_string(eIcicleError error) + { + switch (error) { + case eIcicleError::SUCCESS: + return "eIcicleError::SUCCESS"; + case eIcicleError::INVALID_DEVICE: + return "eIcicleError::INVALID_DEVICE"; + case eIcicleError::OUT_OF_MEMORY: + return "eIcicleError::OUT_OF_MEMORY"; + case eIcicleError::INVALID_POINTER: + return "eIcicleError::INVALID_POINTER"; + case eIcicleError::ALLOCATION_FAILED: + return "eIcicleError::ALLOCATION_FAILED"; + case eIcicleError::DEALLOCATION_FAILED: + return "eIcicleError::DEALLOCATION_FAILED"; + case eIcicleError::COPY_FAILED: + return "eIcicleError::COPY_FAILED"; + case eIcicleError::SYNCHRONIZATION_FAILED: + return "eIcicleError::SYNCHRONIZATION_FAILED"; + case eIcicleError::STREAM_CREATION_FAILED: + return "eIcicleError::STREAM_CREATION_FAILED"; + case eIcicleError::STREAM_DESTRUCTION_FAILED: + return "eIcicleError::STREAM_DESTRUCTION_FAILED"; + case eIcicleError::API_NOT_IMPLEMENTED: + return "eIcicleError::API_NOT_IMPLEMENTED"; + case eIcicleError::INVALID_ARGUMENT: + return "eIcicleError::INVALID_ARGUMENT"; + case eIcicleError::BACKEND_LOAD_FAILED: + return "eIcicleError::BACKEND_LOAD_FAILED"; + case eIcicleError::LICENSE_CHECK_ERROR: + return "eIcicleError::LICENSE_CHECK_ERROR"; + case eIcicleError::UNKNOWN_ERROR: + default: + return "eIcicleError::UNKNOWN_ERROR"; + } + } + +#define ICICLE_CHECK(api_call) \ + do { \ + using namespace icicle; \ + eIcicleError rv = (api_call); \ + if (rv != eIcicleError::SUCCESS) { \ + throw std::runtime_error( \ + "Icicle API fails with code " + std::string(get_error_string(rv)) + " in " + __FILE__ + ":" + \ + std::to_string(__LINE__)); \ + } \ + } while (0) + + void inline throw_icicle_error( + eIcicleError err, const char* const reason, const char* const func, const char* const file, const int line) + { + std::string err_msg = std::string{get_error_string(err)} + " : by: " + func + " at: " + file + ":" + + std::to_string(line) + " error: " + reason; + ICICLE_LOG_ERROR << err_msg; // TODO: Logging + throw std::runtime_error(err_msg); + } + + void inline throw_icicle_error( + eIcicleError err, const std::string& reason, const char* const func, const char* const file, const int line) + { + throw_icicle_error(err, reason.c_str(), func, file, line); + } + +#define THROW_ICICLE_ERR(val, reason) throw_icicle_error(val, reason, __FUNCTION__, __FILE__, __LINE__) + + class AssertHelper + { + public: + AssertHelper(const char* condition, const char* function, const char* file, int line) : os() + { + os << "Assertion failed: (" << condition << "), function " << function << ", file " << file << ", line " << line + << ". "; + } + + template + AssertHelper& operator<<(const T& msg) + { + os << msg; + return *this; + } + + ~AssertHelper() noexcept(false) { throw std::runtime_error(os.str()); } + + private: + std::ostringstream os; + }; + +#define ICICLE_ASSERT(condition) \ + if (!(condition)) AssertHelper(#condition, __FUNCTION__, __FILE__, __LINE__) + +} // namespace icicle diff --git a/icicle/include/icicle/fields/complex_extension.h b/icicle/include/icicle/fields/complex_extension.h new file mode 100644 index 000000000..9b4d35d24 --- /dev/null +++ b/icicle/include/icicle/fields/complex_extension.h @@ -0,0 +1,210 @@ +#pragma once + +#include "field.h" +#include "icicle/utils/modifiers.h" +#ifdef __CUDACC__ +#include "gpu-utils/sharedmem.h" +#endif // __CUDACC__ + +template +class ComplexExtensionField +{ +private: + friend T; + + typedef typename T::Wide FWide; + + struct ExtensionWide { + FWide real; + FWide imaginary; + + friend HOST_DEVICE_INLINE ExtensionWide operator+(ExtensionWide xs, const ExtensionWide& ys) + { + return ExtensionWide{xs.real + ys.real, xs.imaginary + ys.imaginary}; + } + + friend HOST_DEVICE_INLINE ExtensionWide operator-(ExtensionWide xs, const ExtensionWide& ys) + { + return ExtensionWide{xs.real - ys.real, xs.imaginary - ys.imaginary}; + } + }; + +public: + typedef T FF; + static constexpr unsigned TLC = 2 * CONFIG::limbs_count; + + FF real; + FF imaginary; + + static constexpr HOST_DEVICE_INLINE ComplexExtensionField zero() { return ComplexExtensionField{FF::zero(), FF::zero()}; } + + static constexpr HOST_DEVICE_INLINE ComplexExtensionField one() { return ComplexExtensionField{FF::one(), FF::zero()}; } + + static constexpr HOST_DEVICE_INLINE ComplexExtensionField to_montgomery(const ComplexExtensionField& xs) + { + return ComplexExtensionField{xs.real * FF{CONFIG::montgomery_r}, xs.imaginary * FF{CONFIG::montgomery_r}}; + } + + static constexpr HOST_DEVICE_INLINE ComplexExtensionField from_montgomery(const ComplexExtensionField& xs) + { + return ComplexExtensionField{xs.real * FF{CONFIG::montgomery_r_inv}, xs.imaginary * FF{CONFIG::montgomery_r_inv}}; + } + + static HOST_INLINE ComplexExtensionField rand_host() { return ComplexExtensionField{FF::rand_host(), FF::rand_host()}; } + + static void rand_host_many(ComplexExtensionField* out, int size) + { + for (int i = 0; i < size; i++) + out[i] = rand_host(); + } + + template + static constexpr HOST_DEVICE_INLINE ComplexExtensionField sub_modulus(const ComplexExtensionField& xs) + { + return ComplexExtensionField{FF::sub_modulus(&xs.real), FF::sub_modulus(&xs.imaginary)}; + } + + friend std::ostream& operator<<(std::ostream& os, const ComplexExtensionField& xs) + { + os << "{ Real: " << xs.real << " }; { Imaginary: " << xs.imaginary << " }"; + return os; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator+(ComplexExtensionField xs, const ComplexExtensionField& ys) + { + return ComplexExtensionField{xs.real + ys.real, xs.imaginary + ys.imaginary}; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator-(ComplexExtensionField xs, const ComplexExtensionField& ys) + { + return ComplexExtensionField{xs.real - ys.real, xs.imaginary - ys.imaginary}; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator+(FF xs, const ComplexExtensionField& ys) + { + return ComplexExtensionField{xs + ys.real, ys.imaginary}; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator-(FF xs, const ComplexExtensionField& ys) + { + return ComplexExtensionField{xs - ys.real, FF::neg(ys.imaginary)}; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator+(ComplexExtensionField xs, const FF& ys) + { + return ComplexExtensionField{xs.real + ys, xs.imaginary}; + } + + friend HOST_DEVICE_INLINE ComplexExtensionField operator-(ComplexExtensionField xs, const FF& ys) + { + return ComplexExtensionField{xs.real - ys, xs.imaginary}; + } + + template + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ComplexExtensionField& xs, const ComplexExtensionField& ys) + { + FWide real_prod = FF::mul_wide(xs.real, ys.real); + FWide imaginary_prod = FF::mul_wide(xs.imaginary, ys.imaginary); + FWide prod_of_sums = FF::mul_wide(xs.real + xs.imaginary, ys.real + ys.imaginary); + FWide nonresidue_times_im = FF::template mul_unsigned(imaginary_prod); + nonresidue_times_im = CONFIG::nonresidue_is_negative ? FWide::neg(nonresidue_times_im) : nonresidue_times_im; + return ExtensionWide{real_prod + nonresidue_times_im, prod_of_sums - real_prod - imaginary_prod}; + } + + template + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ComplexExtensionField& xs, const FF& ys) + { + return ExtensionWide{FF::mul_wide(xs.real, ys), FF::mul_wide(xs.imaginary, ys)}; + } + + template + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const FF& xs, const ComplexExtensionField& ys) + { + return mul_wide(ys, xs); + } + + template + static constexpr HOST_DEVICE_INLINE ComplexExtensionField reduce(const ExtensionWide& xs) + { + return ComplexExtensionField{ + FF::template reduce(xs.real), FF::template reduce(xs.imaginary)}; + } + + template + friend HOST_DEVICE_INLINE ComplexExtensionField operator*(const T1& xs, const T2& ys) + { + ExtensionWide xy = mul_wide(xs, ys); + return reduce(xy); + } + + friend HOST_DEVICE_INLINE bool operator==(const ComplexExtensionField& xs, const ComplexExtensionField& ys) + { + return (xs.real == ys.real) && (xs.imaginary == ys.imaginary); + } + + friend HOST_DEVICE_INLINE bool operator!=(const ComplexExtensionField& xs, const ComplexExtensionField& ys) { return !(xs == ys); } + + template + static HOST_DEVICE_INLINE ComplexExtensionField mul_const(const ComplexExtensionField& xs) + { + static constexpr FF mul_real = multiplier.real; + static constexpr FF mul_imaginary = multiplier.imaginary; + const FF xs_real = xs.real; + const FF xs_imaginary = xs.imaginary; + FF real_prod = FF::template mul_const(xs_real); + FF imaginary_prod = FF::template mul_const(xs_imaginary); + FF re_im = FF::template mul_const(xs_imaginary); + FF im_re = FF::template mul_const(xs_real); + FF nonresidue_times_im = FF::template mul_unsigned(imaginary_prod); + nonresidue_times_im = CONFIG::nonresidue_is_negative ? FF::neg(nonresidue_times_im) : nonresidue_times_im; + return ComplexExtensionField{real_prod + nonresidue_times_im, re_im + im_re}; + } + + template + static constexpr HOST_DEVICE_INLINE ComplexExtensionField mul_unsigned(const ComplexExtensionField& xs) + { + return {FF::template mul_unsigned(xs.real), FF::template mul_unsigned(xs.imaginary)}; + } + + template + static constexpr HOST_DEVICE_INLINE ExtensionWide sqr_wide(const ComplexExtensionField& xs) + { + // TODO: change to a more efficient squaring + return mul_wide(xs, xs); + } + + template + static constexpr HOST_DEVICE_INLINE ComplexExtensionField sqr(const ComplexExtensionField& xs) + { + // TODO: change to a more efficient squaring + return xs * xs; + } + + template + static constexpr HOST_DEVICE_INLINE ComplexExtensionField neg(const ComplexExtensionField& xs) + { + return ComplexExtensionField{FF::neg(xs.real), FF::neg(xs.imaginary)}; + } + + // inverse of zero is set to be zero which is what we want most of the time + static constexpr HOST_DEVICE_INLINE ComplexExtensionField inverse(const ComplexExtensionField& xs) + { + ComplexExtensionField xs_conjugate = {xs.real, FF::neg(xs.imaginary)}; + FF nonresidue_times_im = FF::template mul_unsigned(FF::sqr(xs.imaginary)); + nonresidue_times_im = CONFIG::nonresidue_is_negative ? FF::neg(nonresidue_times_im) : nonresidue_times_im; + // TODO: wide here + FF xs_norm_squared = FF::sqr(xs.real) - nonresidue_times_im; + return xs_conjugate * ComplexExtensionField{FF::inverse(xs_norm_squared), FF::zero()}; + } +}; + +#ifdef __CUDACC__ +template +struct SharedMemory> { + __device__ ComplexExtensionField* getPointer() + { + extern __shared__ ComplexExtensionField s_ext2_scalar_[]; + return s_ext2_scalar_; + } +}; +#endif //__CUDAC__ diff --git a/icicle/include/fields/field.cuh b/icicle/include/icicle/fields/field.h similarity index 97% rename from icicle/include/fields/field.cuh rename to icicle/include/icicle/fields/field.h index 6fe3e069a..231e83eff 100644 --- a/icicle/include/fields/field.cuh +++ b/icicle/include/icicle/fields/field.h @@ -1,7 +1,9 @@ +#pragma once + /** * This file contains methods for working with elements of a prime field. It is based on and evolved from Matter Labs' * [Zprize - * submission](https://github.com/matter-labs/z-prize-msm-gpu/blob/main/bellman-cuda-rust/bellman-cuda-sys/native/ff_dispatch_st.cuh). + * submission](https://github.com/matter-labs/z-prize-msm-gpu/blob/main/bellman-cuda-rust/bellman-cuda-sys/native/ff_dispatch_st.h). * * TODO: DmytroTym: current version needs refactoring (e.g. there's no reason to have different classes Field and * ff_storage among other issues). But because this is an internal file and correctness and performance are unaffected, @@ -16,14 +18,14 @@ * Which is where `even` and `odd` arrays across the codebase come from. */ -#pragma once +#ifdef __CUDACC__ +#include "gpu-utils/sharedmem.h" +#include "ptx.h" +#endif // __CUDACC__ -#include "gpu-utils/error_handler.cuh" -#include "gpu-utils/modifiers.cuh" -#include "gpu-utils/sharedmem.cuh" -#include "host_math.cuh" -#include "ptx.cuh" -#include "storage.cuh" +#include "icicle/errors.h" +#include "host_math.h" +#include "storage.h" #include #include @@ -31,6 +33,8 @@ #include #include +using namespace icicle; + template class Field { @@ -56,7 +60,7 @@ class Field { if (logn == 0) { return Field{CONFIG::one}; } - if (logn > CONFIG::omegas_count) { THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Field: Invalid omega index"); } + if (logn > CONFIG::omegas_count) { THROW_ICICLE_ERR(eIcicleError::INVALID_ARGUMENT, "Field: Invalid omega index"); } Field omega = Field{CONFIG::rou}; for (int i = 0; i < CONFIG::omegas_count - logn; i++) @@ -69,7 +73,7 @@ class Field if (logn == 0) { return Field{CONFIG::one}; } if (logn > CONFIG::omegas_count) { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Field: Invalid omega_inv index"); + THROW_ICICLE_ERR(eIcicleError::INVALID_ARGUMENT, "Field: Invalid omega_inv index"); } Field omega = inverse(Field{CONFIG::rou}); @@ -82,12 +86,11 @@ class Field { if (logn == 0) { return Field{CONFIG::one}; } #ifndef __CUDA_ARCH__ - if (logn > CONFIG::omegas_count) THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Field: Invalid inv index"); + if (logn > CONFIG::omegas_count) THROW_ICICLE_ERR(eIcicleError::INVALID_ARGUMENT, "Field: Invalid inv index"); #else if (logn > CONFIG::omegas_count) { printf( - "CUDA ERROR: field.cuh: error on inv_log_size(logn): logn(=%u) > omegas_count (=%u)", logn, - CONFIG::omegas_count); + "CUDA ERROR: field.h: error on inv_log_size(logn): logn(=%u) > omegas_count (=%u)", logn, CONFIG::omegas_count); assert(false); } #endif // __CUDA_ARCH__ @@ -251,6 +254,7 @@ class Field } } +#ifdef __CUDACC__ template static constexpr DEVICE_INLINE uint32_t add_sub_u32_device(const uint32_t* x, const uint32_t* y, uint32_t* r) { @@ -273,7 +277,7 @@ class Field uint32_t* r = rs.limbs; return add_sub_u32_device(x, y, r); } - +#endif // __CUDACC__ template static constexpr HOST_DEVICE_INLINE uint32_t add_limbs(const storage& xs, const storage& ys, storage& rs) @@ -296,6 +300,7 @@ class Field #endif } +#ifdef __CUDACC__ static DEVICE_INLINE void mul_n(uint32_t* acc, const uint32_t* a, uint32_t bi, size_t n = TLC) { UNROLL @@ -635,6 +640,7 @@ class Field } } +#endif // __CUDACC__ static HOST_DEVICE_INLINE void multiply_raw(const ff_storage& as, const ff_storage& bs, ff_wide_storage& rs) { #ifdef __CUDA_ARCH__ @@ -784,7 +790,7 @@ class Field // `xs + l \cdot (2^{32 \cdot TLC}-p)` which is the same as original (up to higher limbs which we don't care about). multiply_and_add_lsb_neg_modulus_raw(l_hi.limbs_storage, xs_lo.limbs_storage, r.limbs_storage); ff_storage r_reduced = {}; - uint32_t carry; + uint32_t carry = 0; // As mentioned, either 2 or 1 reduction can be performed depending on the field in question. if (num_of_reductions() == 2) { carry = sub_limbs(r.limbs_storage, get_modulus<2>(), r_reduced); @@ -858,7 +864,7 @@ class Field rs = is_zero ? temp : (rs + temp); is_zero = false; } - if (multiplier & ((1 << (31 - i) - 1) << (i + 1))) break; + if (multiplier & ((1 << (31 - i - 1)) << (i + 1))) break; temp = temp + temp; } return rs; @@ -984,6 +990,7 @@ struct std::hash> { } }; +#ifdef __CUDACC__ template struct SharedMemory> { __device__ Field* getPointer() @@ -991,4 +998,6 @@ struct SharedMemory> { extern __shared__ Field s_scalar_[]; return s_scalar_; } -}; \ No newline at end of file +}; + +#endif // __CUDACC__ \ No newline at end of file diff --git a/icicle/include/fields/field_config.cuh b/icicle/include/icicle/fields/field_config.h similarity index 59% rename from icicle/include/fields/field_config.cuh rename to icicle/include/icicle/fields/field_config.h index d9ec18c0c..3587f8c41 100644 --- a/icicle/include/fields/field_config.cuh +++ b/icicle/include/icicle/fields/field_config.h @@ -1,9 +1,7 @@ #pragma once -#ifndef FIELD_CONFIG_H -#define FIELD_CONFIG_H -#include "fields/id.h" -#include "fields/field.cuh" +#include "icicle/fields/id.h" +#include "icicle/fields/field.h" /** * @namespace field_config @@ -11,31 +9,29 @@ * with the `-DFIELD` env variable passed during build. */ #if FIELD_ID == BN254 -#include "fields/snark_fields/bn254_scalar.cuh" +#include "icicle/fields/snark_fields/bn254_scalar.h" namespace field_config = bn254; #elif FIELD_ID == BLS12_381 -#include "fields/snark_fields/bls12_381_scalar.cuh" +#include "icicle/fields/snark_fields/bls12_381_scalar.h" using bls12_381::fp_config; namespace field_config = bls12_381; #elif FIELD_ID == BLS12_377 -#include "fields/snark_fields/bls12_377_scalar.cuh" +#include "icicle/fields/snark_fields/bls12_377_scalar.h" namespace field_config = bls12_377; #elif FIELD_ID == BW6_761 -#include "fields/snark_fields/bw6_761_scalar.cuh" +#include "icicle/fields/snark_fields/bw6_761_scalar.h" namespace field_config = bw6_761; #elif FIELD_ID == GRUMPKIN -#include "fields/snark_fields/grumpkin_scalar.cuh" +#include "icicle/fields/snark_fields/grumpkin_scalar.h" namespace field_config = grumpkin; #elif FIELD_ID == BABY_BEAR -#include "fields/stark_fields/babybear.cuh" +#include "icicle/fields/stark_fields/babybear.h" namespace field_config = babybear; #elif FIELD_ID == STARK_252 -#include "fields/stark_fields/stark252.cuh" +#include "icicle/fields/stark_fields/stark252.h" namespace field_config = stark252; #elif FIELD_ID == M31 -#include "fields/stark_fields/m31.cuh" +#include "icicle/fields/stark_fields/m31.h" namespace field_config = m31; #endif - -#endif \ No newline at end of file diff --git a/icicle/include/icicle/fields/host_math.h b/icicle/include/icicle/fields/host_math.h new file mode 100644 index 000000000..e256aa922 --- /dev/null +++ b/icicle/include/icicle/fields/host_math.h @@ -0,0 +1,302 @@ +// Note: this optimization generates invalid code (using gcc) when storage class has a union for both u32 and u64 so +// disabling it. +#if defined(__GNUC__) && !defined(__NVCC__) && !defined(__clang__) +#pragma GCC optimize("no-strict-aliasing") +#endif + +#pragma once + +#include +#include "icicle/utils/modifiers.h" +#include "icicle/fields/storage.h" +namespace host_math { + + // return x + y with T operands + template + static constexpr __host__ T add(const T x, const T y) + { + return x + y; + } + + // return x + y + carry with T operands + template + static constexpr __host__ T addc(const T x, const T y, const T carry) + { + return x + y + carry; + } + + // return x + y and carry out with T operands + template + static constexpr __host__ T add_cc(const T x, const T y, T& carry) + { + T result = x + y; + carry = x > result; + return result; + } + + // return x + y + carry and carry out with T operands + template + static constexpr __host__ T addc_cc(const T x, const T y, T& carry) + { + const T result = x + y + carry; + carry = carry && x >= result || !carry && x > result; + return result; + } + + // return x - y with T operands + template + static constexpr __host__ T sub(const T x, const T y) + { + return x - y; + } + + // return x - y - borrow with T operands + template + static constexpr __host__ T subc(const T x, const T y, const T borrow) + { + return x - y - borrow; + } + + // return x - y and borrow out with T operands + template + static constexpr __host__ T sub_cc(const T x, const T y, T& borrow) + { + T result = x - y; + borrow = x < result; + return result; + } + + // return x - y - borrow and borrow out with T operands + template + static constexpr __host__ T subc_cc(const T x, const T y, T& borrow) + { + const T result = x - y - borrow; + borrow = borrow && x <= result || !borrow && x < result; + return result; + } + + // return x * y + z + carry and carry out with uint32_t operands + static constexpr __host__ uint32_t madc_cc(const uint32_t x, const uint32_t y, const uint32_t z, uint32_t& carry) + { + uint64_t r = static_cast(x) * y + z + carry; + carry = (uint32_t)(r >> 32); + uint32_t result = r & 0xffffffff; + return result; + } + + static constexpr __host__ uint64_t madc_cc_64(const uint64_t x, const uint64_t y, const uint64_t z, uint64_t& carry) + { + __uint128_t r = static_cast<__uint128_t>(x) * y + z + carry; + carry = (uint64_t)(r >> 64); + uint64_t result = r & 0xffffffffffffffff; + return result; + } + + template + struct carry_chain { + unsigned index; + + constexpr HOST_INLINE carry_chain() : index(0) {} + + template + constexpr HOST_INLINE T add(const T x, const T y, T& carry) + { + index++; + if (index == 1 && OPS_COUNT == 1 && !CARRY_IN && !CARRY_OUT) + return host_math::add(x, y); + else if (index == 1 && !CARRY_IN) + return host_math::add_cc(x, y, carry); + else if (index < OPS_COUNT || CARRY_OUT) + return host_math::addc_cc(x, y, carry); + else + return host_math::addc(x, y, carry); + } + + template + constexpr HOST_INLINE T sub(const T x, const T y, T& carry) + { + index++; + if (index == 1 && OPS_COUNT == 1 && !CARRY_IN && !CARRY_OUT) + return host_math::sub(x, y); + else if (index == 1 && !CARRY_IN) + return host_math::sub_cc(x, y, carry); + else if (index < OPS_COUNT || CARRY_OUT) + return host_math::subc_cc(x, y, carry); + else + return host_math::subc(x, y, carry); + } + }; + + template + static constexpr HOST_INLINE uint32_t add_sub_limbs_32(const uint32_t* x, const uint32_t* y, uint32_t* r) + { + uint32_t carry = 0; + carry_chain chain; + for (unsigned i = 0; i < NLIMBS; i++) + r[i] = SUBTRACT ? chain.sub(x[i], y[i], carry) : chain.add(x[i], y[i], carry); + return CARRY_OUT ? carry : 0; + } + + template + static constexpr HOST_INLINE uint64_t add_sub_limbs_64(const uint64_t* x, const uint64_t* y, uint64_t* r) + { + uint64_t carry = 0; + carry_chain chain; + for (unsigned i = 0; i < NLIMBS / 2; i++) + r[i] = SUBTRACT ? chain.sub(x[i], y[i], carry) : chain.add(x[i], y[i], carry); + return CARRY_OUT ? carry : 0; + } + + template < + unsigned NLIMBS, + bool SUBTRACT, + bool CARRY_OUT, + bool USE_32 = true> // for now we use only the 32 add/sub because the casting of the carry causes problems when + // compiling in release. to solve this we need to entirely split the field functions between a + // host version and a device version. + static constexpr HOST_INLINE uint32_t // 32 is enough for the carry + add_sub_limbs(const storage& xs, const storage& ys, storage& rs) + { + if constexpr (USE_32 || NLIMBS < 2) { + const uint32_t* x = xs.limbs; + const uint32_t* y = ys.limbs; + uint32_t* r = rs.limbs; + return add_sub_limbs_32(x, y, r); + } else { + const uint64_t* x = xs.limbs64; + const uint64_t* y = ys.limbs64; + uint64_t* r = rs.limbs64; + // Note: returns uint64 but uint 32 is enough. + uint64_t result = add_sub_limbs_64(x, y, r); + uint32_t carry = result == 1; + return carry; + } + } + + template + static constexpr HOST_INLINE void + multiply_raw_32(const storage& as, const storage& bs, storage& rs) + { + const uint32_t* a = as.limbs; + const uint32_t* b = bs.limbs; + uint32_t* r = rs.limbs; + for (unsigned i = 0; i < NLIMBS_B; i++) { + uint32_t carry = 0; + for (unsigned j = 0; j < NLIMBS_A; j++) + r[j + i] = host_math::madc_cc(a[j], b[i], r[j + i], carry); + r[NLIMBS_A + i] = carry; + } + } + + template + static HOST_INLINE void multiply_raw_64(const uint64_t* a, const uint64_t* b, uint64_t* r) + { + for (unsigned i = 0; i < NLIMBS_B / 2; i++) { + uint64_t carry = 0; + for (unsigned j = 0; j < NLIMBS_A / 2; j++) + r[j + i] = host_math::madc_cc_64(a[j], b[i], r[j + i], carry); + r[NLIMBS_A / 2 + i] = carry; + } + } + + template + static HOST_INLINE void + multiply_raw_64(const storage& as, const storage& bs, storage& rs) + { + const uint64_t* a = as.limbs64; + const uint64_t* b = bs.limbs64; + uint64_t* r = rs.limbs64; + multiply_raw_64(a, b, r); + } + + template + static constexpr HOST_INLINE void + multiply_raw(const storage& as, const storage& bs, storage& rs) + { + static_assert( + (NLIMBS_A % 2 == 0 || NLIMBS_A == 1) && (NLIMBS_B % 2 == 0 || NLIMBS_B == 1), + "odd number of limbs is not supported\n"); + if constexpr (USE_32) { + multiply_raw_32(as, bs, rs); + return; + } else if constexpr ((NLIMBS_A == 1 && NLIMBS_B == 2) || (NLIMBS_A == 2 && NLIMBS_B == 1)) { + multiply_raw_32(as, bs, rs); + return; + } else if constexpr (NLIMBS_A == 1 && NLIMBS_B == 1) { + rs.limbs[1] = 0; + rs.limbs[0] = host_math::madc_cc(as.limbs[0], bs.limbs[0], 0, rs.limbs[1]); + return; + } else if constexpr (NLIMBS_A == 2 && NLIMBS_B == 2) { + const uint64_t* a = as.limbs64; // nof limbs should be even + const uint64_t* b = bs.limbs64; + uint64_t* r = rs.limbs64; + r[1] = 0; + r[0] = host_math::madc_cc_64(a[0], b[0], 0, r[1]); + return; + } else { + multiply_raw_64(as, bs, rs); + } + } + + template + static constexpr HOST_INLINE storage left_shift(const storage& xs) + { + if constexpr (BITS == 0) + return xs; + else { + constexpr unsigned BITS32 = BITS % 32; + constexpr unsigned LIMBS_GAP = BITS / 32; + storage out{}; + if constexpr (LIMBS_GAP < NLIMBS) { + out.limbs[LIMBS_GAP] = xs.limbs[0] << BITS32; + for (unsigned i = 1; i < NLIMBS - LIMBS_GAP; i++) + out.limbs[i + LIMBS_GAP] = (xs.limbs[i] << BITS32) + (xs.limbs[i - 1] >> (32 - BITS32)); + } + return out; + } + } + + template + static constexpr HOST_INLINE storage right_shift(const storage& xs) + { + if constexpr (BITS == 0) + return xs; + else { + constexpr unsigned BITS32 = BITS % 32; + constexpr unsigned LIMBS_GAP = BITS / 32; + storage out{}; + if constexpr (LIMBS_GAP < NLIMBS - 1) { + for (unsigned i = 0; i < NLIMBS - LIMBS_GAP - 1; i++) + out.limbs[i] = (xs.limbs[i + LIMBS_GAP] >> BITS32) + (xs.limbs[i + LIMBS_GAP + 1] << (32 - BITS32)); + } + if constexpr (LIMBS_GAP < NLIMBS) out.limbs[NLIMBS - LIMBS_GAP - 1] = (xs.limbs[NLIMBS - 1] >> BITS32); + return out; + } + } + + template < + unsigned NLIMBS_NUM, + unsigned NLIMBS_DENOM, + unsigned NLIMBS_Q = (NLIMBS_NUM - NLIMBS_DENOM), + bool USE_32 = false> + static constexpr HOST_INLINE void integer_division( + const storage& num, const storage& denom, storage& q, storage& r) + { + storage temp = {}; + for (int limb_idx = NLIMBS_NUM - 1; limb_idx >= 0; limb_idx--) { + for (int bit_idx = 31; bit_idx >= 0; bit_idx--) { + r = left_shift(r); + r.limbs[0] |= ((num.limbs[limb_idx] >> bit_idx) & 1); + uint32_t c = add_sub_limbs(r, denom, temp); + if (limb_idx < NLIMBS_Q & !c) { + r = temp; + q.limbs[limb_idx] |= 1 << bit_idx; + } + } + } + } +} // namespace host_math + +#if defined(__GNUC__) && !defined(__NVCC__) && !defined(__clang__) +#pragma GCC reset_options +#endif diff --git a/icicle/include/fields/id.h b/icicle/include/icicle/fields/id.h similarity index 100% rename from icicle/include/fields/id.h rename to icicle/include/icicle/fields/id.h diff --git a/icicle/include/fields/params_gen.cuh b/icicle/include/icicle/fields/params_gen.h similarity index 88% rename from icicle/include/fields/params_gen.cuh rename to icicle/include/icicle/fields/params_gen.h index 6b21c5e0e..3626971b5 100644 --- a/icicle/include/fields/params_gen.cuh +++ b/icicle/include/icicle/fields/params_gen.h @@ -1,16 +1,14 @@ #pragma once -#ifndef PARAMS_GEN_H -#define PARAMS_GEN_H -#include "storage.cuh" -#include "host_math.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/host_math.h" namespace params_gen { template static constexpr HOST_INLINE storage<2 * NLIMBS> get_square(const storage& xs) { storage<2 * NLIMBS> rs = {}; - host_math::template multiply_raw(xs, xs, rs); + host_math::template multiply_raw(xs, xs, rs); return host_math::template left_shift<2 * NLIMBS, BIT_SHIFT>(rs); } @@ -19,7 +17,7 @@ namespace params_gen { get_difference_no_carry(const storage& xs, const storage& ys) { storage rs = {}; - host_math::template add_sub_limbs(xs, ys, rs); + host_math::template add_sub_limbs(xs, ys, rs); return rs; } @@ -30,7 +28,7 @@ namespace params_gen { storage qs = {}; storage<2 * NLIMBS> wide_one = {1}; storage<2 * NLIMBS> pow_of_2 = host_math::template left_shift<2 * NLIMBS, EXP>(wide_one); - host_math::template integer_division<2 * NLIMBS, NLIMBS>(pow_of_2, modulus, qs, rs); + host_math::template integer_division<2 * NLIMBS, NLIMBS, NLIMBS, true>(pow_of_2, modulus, qs, rs); return qs; } @@ -40,12 +38,12 @@ namespace params_gen { storage rs = {1}; for (int i = 0; i < 32 * NLIMBS; i++) { if (INV) { - if (rs.limbs[0] & 1) host_math::template add_sub_limbs(rs, modulus, rs); + if (rs.limbs[0] & 1) host_math::template add_sub_limbs(rs, modulus, rs); rs = host_math::template right_shift(rs); } else { rs = host_math::template left_shift(rs); storage temp = {}; - rs = host_math::template add_sub_limbs(rs, modulus, temp) ? rs : temp; + rs = host_math::template add_sub_limbs(rs, modulus, temp) ? rs : temp; } } return rs; @@ -59,12 +57,12 @@ namespace params_gen { storage<2 * NLIMBS> x1 = {}; storage<3 * NLIMBS> x2 = {}; storage<3 * NLIMBS> x3 = {}; - host_math::template multiply_raw(modulus, m, x1); - host_math::template multiply_raw(modulus, x1, x2); + host_math::template multiply_raw(modulus, m, x1); + host_math::template multiply_raw(modulus, x1, x2); storage<2 * NLIMBS> one = {1}; storage<2 * NLIMBS> pow_of_2 = host_math::template left_shift<2 * NLIMBS, NBITS>(one); - host_math::template multiply_raw(modulus, pow_of_2, x3); - host_math::template add_sub_limbs<3 * NLIMBS, true, false>(x3, x2, x2); + host_math::template multiply_raw(modulus, pow_of_2, x3); + host_math::template add_sub_limbs<3 * NLIMBS, true, false, true>(x3, x2, x2); double err = (double)x2.limbs[2 * NLIMBS - 1] / pow_of_2.limbs[2 * NLIMBS - 1]; err += (double)m.limbs[NLIMBS - 1] / 0xffffffff; err += (double)NLIMBS / 0x80000000; @@ -89,7 +87,7 @@ namespace params_gen { storage_array invs = {}; storage rs = {1}; for (int i = 0; i < TWO_ADICITY; i++) { - if (rs.limbs[0] & 1) host_math::template add_sub_limbs(rs, modulus, rs); + if (rs.limbs[0] & 1) host_math::template add_sub_limbs(rs, modulus, rs); rs = host_math::template right_shift(rs); invs.storages[i] = rs; } @@ -125,5 +123,3 @@ namespace params_gen { static constexpr unsigned omegas_count = params_gen::template two_adicity(modulus); \ static constexpr storage_array inv = \ params_gen::template get_invs(modulus); - -#endif \ No newline at end of file diff --git a/icicle/include/fields/quartic_extension.cuh b/icicle/include/icicle/fields/quartic_extension.h similarity index 65% rename from icicle/include/fields/quartic_extension.cuh rename to icicle/include/icicle/fields/quartic_extension.h index 8fead58c5..923b31f3a 100644 --- a/icicle/include/fields/quartic_extension.cuh +++ b/icicle/include/icicle/fields/quartic_extension.h @@ -1,11 +1,14 @@ #pragma once -#include "field.cuh" -#include "gpu-utils/modifiers.cuh" -#include "gpu-utils/sharedmem.cuh" +#include "icicle/fields/field.h" +#include "icicle/utils/modifiers.h" + +#if __CUDACC__ +#include "gpu-utils/sharedmem.h" +#endif // __CUDACC__ template -class ExtensionField +class QuarticExtensionField { private: typedef typename T::Wide FWide; @@ -36,87 +39,87 @@ class ExtensionField FF im2; FF im3; - static constexpr HOST_DEVICE_INLINE ExtensionField zero() + static constexpr HOST_DEVICE_INLINE QuarticExtensionField zero() { - return ExtensionField{FF::zero(), FF::zero(), FF::zero(), FF::zero()}; + return QuarticExtensionField{FF::zero(), FF::zero(), FF::zero(), FF::zero()}; } - static constexpr HOST_DEVICE_INLINE ExtensionField one() + static constexpr HOST_DEVICE_INLINE QuarticExtensionField one() { - return ExtensionField{FF::one(), FF::zero(), FF::zero(), FF::zero()}; + return QuarticExtensionField{FF::one(), FF::zero(), FF::zero(), FF::zero()}; } - static constexpr HOST_DEVICE_INLINE ExtensionField to_montgomery(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField to_montgomery(const QuarticExtensionField& xs) { - return ExtensionField{ + return QuarticExtensionField{ FF::to_montgomery(xs.real), FF::to_montgomery(xs.im1), FF::to_montgomery(xs.im2), FF::to_montgomery(xs.im3)}; } - static constexpr HOST_DEVICE_INLINE ExtensionField from_montgomery(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField from_montgomery(const QuarticExtensionField& xs) { - return ExtensionField{ + return QuarticExtensionField{ FF::from_montgomery(xs.real), FF::from_montgomery(xs.im1), FF::from_montgomery(xs.im2), FF::from_montgomery(xs.im3)}; } - static HOST_INLINE ExtensionField rand_host() + static HOST_INLINE QuarticExtensionField rand_host() { - return ExtensionField{FF::rand_host(), FF::rand_host(), FF::rand_host(), FF::rand_host()}; + return QuarticExtensionField{FF::rand_host(), FF::rand_host(), FF::rand_host(), FF::rand_host()}; } - static void rand_host_many(ExtensionField* out, int size) + static void rand_host_many(QuarticExtensionField* out, int size) { for (int i = 0; i < size; i++) out[i] = rand_host(); } template - static constexpr HOST_DEVICE_INLINE ExtensionField sub_modulus(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField sub_modulus(const QuarticExtensionField& xs) { - return ExtensionField{ + return QuarticExtensionField{ FF::sub_modulus(&xs.real), FF::sub_modulus(&xs.im1), FF::sub_modulus(&xs.im2), FF::sub_modulus(&xs.im3)}; } - friend std::ostream& operator<<(std::ostream& os, const ExtensionField& xs) + friend std::ostream& operator<<(std::ostream& os, const QuarticExtensionField& xs) { os << "{ Real: " << xs.real << " }; { Im1: " << xs.im1 << " }; { Im2: " << xs.im2 << " }; { Im3: " << xs.im3 << " };"; return os; } - friend HOST_DEVICE_INLINE ExtensionField operator+(ExtensionField xs, const ExtensionField& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator+(QuarticExtensionField xs, const QuarticExtensionField& ys) { - return ExtensionField{xs.real + ys.real, xs.im1 + ys.im1, xs.im2 + ys.im2, xs.im3 + ys.im3}; + return QuarticExtensionField{xs.real + ys.real, xs.im1 + ys.im1, xs.im2 + ys.im2, xs.im3 + ys.im3}; } - friend HOST_DEVICE_INLINE ExtensionField operator-(ExtensionField xs, const ExtensionField& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator-(QuarticExtensionField xs, const QuarticExtensionField& ys) { - return ExtensionField{xs.real - ys.real, xs.im1 - ys.im1, xs.im2 - ys.im2, xs.im3 - ys.im3}; + return QuarticExtensionField{xs.real - ys.real, xs.im1 - ys.im1, xs.im2 - ys.im2, xs.im3 - ys.im3}; } - friend HOST_DEVICE_INLINE ExtensionField operator+(FF xs, const ExtensionField& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator+(FF xs, const QuarticExtensionField& ys) { - return ExtensionField{xs + ys.real, ys.im1, ys.im2, ys.im3}; + return QuarticExtensionField{xs + ys.real, ys.im1, ys.im2, ys.im3}; } - friend HOST_DEVICE_INLINE ExtensionField operator-(FF xs, const ExtensionField& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator-(FF xs, const QuarticExtensionField& ys) { - return ExtensionField{xs - ys.real, FF::neg(ys.im1), FF::neg(ys.im2), FF::neg(ys.im3)}; + return QuarticExtensionField{xs - ys.real, FF::neg(ys.im1), FF::neg(ys.im2), FF::neg(ys.im3)}; } - friend HOST_DEVICE_INLINE ExtensionField operator+(ExtensionField xs, const FF& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator+(QuarticExtensionField xs, const FF& ys) { - return ExtensionField{xs.real + ys, xs.im1, xs.im2, xs.im3}; + return QuarticExtensionField{xs.real + ys, xs.im1, xs.im2, xs.im3}; } - friend HOST_DEVICE_INLINE ExtensionField operator-(ExtensionField xs, const FF& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator-(QuarticExtensionField xs, const FF& ys) { - return ExtensionField{xs.real - ys, xs.im1, xs.im2, xs.im3}; + return QuarticExtensionField{xs.real - ys, xs.im1, xs.im2, xs.im3}; } template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ExtensionField& xs, const ExtensionField& ys) + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const QuarticExtensionField& xs, const QuarticExtensionField& ys) { if (CONFIG::nonresidue_is_negative) return ExtensionWide{ @@ -143,43 +146,43 @@ class ExtensionField } template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const ExtensionField& xs, const FF& ys) + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const QuarticExtensionField& xs, const FF& ys) { return ExtensionWide{ FF::mul_wide(xs.real, ys), FF::mul_wide(xs.im1, ys), FF::mul_wide(xs.im2, ys), FF::mul_wide(xs.im3, ys)}; } template - static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const FF& xs, const ExtensionField& ys) + static constexpr HOST_DEVICE_INLINE ExtensionWide mul_wide(const FF& xs, const QuarticExtensionField& ys) { return ExtensionWide{ FF::mul_wide(xs, ys.real), FF::mul_wide(xs, ys.im1), FF::mul_wide(xs, ys.im2), FF::mul_wide(xs, ys.im3)}; } template - static constexpr HOST_DEVICE_INLINE ExtensionField reduce(const ExtensionWide& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField reduce(const ExtensionWide& xs) { - return ExtensionField{ + return QuarticExtensionField{ FF::template reduce(xs.real), FF::template reduce(xs.im1), FF::template reduce(xs.im2), FF::template reduce(xs.im3)}; } template - friend HOST_DEVICE_INLINE ExtensionField operator*(const T1& xs, const T2& ys) + friend HOST_DEVICE_INLINE QuarticExtensionField operator*(const T1& xs, const T2& ys) { ExtensionWide xy = mul_wide(xs, ys); return reduce(xy); } - friend HOST_DEVICE_INLINE bool operator==(const ExtensionField& xs, const ExtensionField& ys) + friend HOST_DEVICE_INLINE bool operator==(const QuarticExtensionField& xs, const QuarticExtensionField& ys) { return (xs.real == ys.real) && (xs.im1 == ys.im1) && (xs.im2 == ys.im2) && (xs.im3 == ys.im3); } - friend HOST_DEVICE_INLINE bool operator!=(const ExtensionField& xs, const ExtensionField& ys) { return !(xs == ys); } + friend HOST_DEVICE_INLINE bool operator!=(const QuarticExtensionField& xs, const QuarticExtensionField& ys) { return !(xs == ys); } template - static constexpr HOST_DEVICE_INLINE ExtensionField mul_unsigned(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField mul_unsigned(const QuarticExtensionField& xs) { return { FF::template mul_unsigned(xs.real), FF::template mul_unsigned(xs.im1), @@ -187,27 +190,27 @@ class ExtensionField } template - static constexpr HOST_DEVICE_INLINE ExtensionWide sqr_wide(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE ExtensionWide sqr_wide(const QuarticExtensionField& xs) { // TODO: change to a more efficient squaring return mul_wide(xs, xs); } template - static constexpr HOST_DEVICE_INLINE ExtensionField sqr(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField sqr(const QuarticExtensionField& xs) { // TODO: change to a more efficient squaring return xs * xs; } template - static constexpr HOST_DEVICE_INLINE ExtensionField neg(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField neg(const QuarticExtensionField& xs) { return {FF::neg(xs.real), FF::neg(xs.im1), FF::neg(xs.im2), FF::neg(xs.im3)}; } // inverse of zero is set to be zero which is what we want most of the time - static constexpr HOST_DEVICE_INLINE ExtensionField inverse(const ExtensionField& xs) + static constexpr HOST_DEVICE_INLINE QuarticExtensionField inverse(const QuarticExtensionField& xs) { FF x, x0, x2; if (CONFIG::nonresidue_is_negative) { @@ -245,12 +248,13 @@ class ExtensionField }; } }; - +#if __CUDACC__ template -struct SharedMemory> { - __device__ ExtensionField* getPointer() +struct SharedMemory> { + __device__ QuarticExtensionField* getPointer() { - extern __shared__ ExtensionField s_ext4_scalar_[]; + extern __shared__ QuarticExtensionField s_ext4_scalar_[]; return s_ext4_scalar_; } -}; \ No newline at end of file +}; +#endif // __CUDACC__ diff --git a/icicle/include/fields/snark_fields/bls12_377_base.cuh b/icicle/include/icicle/fields/snark_fields/bls12_377_base.h similarity index 85% rename from icicle/include/fields/snark_fields/bls12_377_base.cuh rename to icicle/include/icicle/fields/snark_fields/bls12_377_base.h index dc7a13feb..ec540a7de 100644 --- a/icicle/include/fields/snark_fields/bls12_377_base.cuh +++ b/icicle/include/icicle/fields/snark_fields/bls12_377_base.h @@ -1,9 +1,7 @@ #pragma once -#ifndef BLS12_377_BASE_PARAMS_H -#define BLS12_377_BASE_PARAMS_H -#include "fields/storage.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/params_gen.h" namespace bls12_377 { struct fq_config { @@ -21,5 +19,3 @@ namespace bls12_377 { static constexpr bool nonresidue_is_negative = true; }; } // namespace bls12_377 - -#endif \ No newline at end of file diff --git a/icicle/include/fields/snark_fields/bls12_377_scalar.cuh b/icicle/include/icicle/fields/snark_fields/bls12_377_scalar.h similarity index 77% rename from icicle/include/fields/snark_fields/bls12_377_scalar.cuh rename to icicle/include/icicle/fields/snark_fields/bls12_377_scalar.h index 3924f59f4..54a56db69 100644 --- a/icicle/include/fields/snark_fields/bls12_377_scalar.cuh +++ b/icicle/include/icicle/fields/snark_fields/bls12_377_scalar.h @@ -1,10 +1,8 @@ #pragma once -#ifndef BLS12_377_SCALAR_PARAMS_H -#define BLS12_377_SCALAR_PARAMS_H -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/params_gen.h" namespace bls12_377 { struct fp_config { @@ -22,5 +20,3 @@ namespace bls12_377 { */ typedef Field scalar_t; } // namespace bls12_377 - -#endif \ No newline at end of file diff --git a/icicle/include/fields/snark_fields/bls12_381_base.cuh b/icicle/include/icicle/fields/snark_fields/bls12_381_base.h similarity index 88% rename from icicle/include/fields/snark_fields/bls12_381_base.cuh rename to icicle/include/icicle/fields/snark_fields/bls12_381_base.h index 54fc0ab57..b88a723ac 100644 --- a/icicle/include/fields/snark_fields/bls12_381_base.cuh +++ b/icicle/include/icicle/fields/snark_fields/bls12_381_base.h @@ -2,8 +2,8 @@ #ifndef BLS12_381_BASE_PARAMS_H #define BLS12_381_BASE_PARAMS_H -#include "fields/storage.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/params_gen.h" namespace bls12_381 { struct fq_config { @@ -18,4 +18,4 @@ namespace bls12_381 { }; } // namespace bls12_381 -#endif \ No newline at end of file +#endif diff --git a/icicle/include/fields/snark_fields/bls12_381_scalar.cuh b/icicle/include/icicle/fields/snark_fields/bls12_381_scalar.h similarity index 86% rename from icicle/include/fields/snark_fields/bls12_381_scalar.cuh rename to icicle/include/icicle/fields/snark_fields/bls12_381_scalar.h index c6bd12fe5..ae0bfe2cd 100644 --- a/icicle/include/fields/snark_fields/bls12_381_scalar.cuh +++ b/icicle/include/icicle/fields/snark_fields/bls12_381_scalar.h @@ -2,9 +2,9 @@ #ifndef BLS12_381_SCALAR_PARAMS_H #define BLS12_381_SCALAR_PARAMS_H -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/params_gen.h" namespace bls12_381 { struct fp_config { diff --git a/icicle/include/fields/snark_fields/bn254_base.cuh b/icicle/include/icicle/fields/snark_fields/bn254_base.h similarity index 78% rename from icicle/include/fields/snark_fields/bn254_base.cuh rename to icicle/include/icicle/fields/snark_fields/bn254_base.h index 95f18ed3e..6ff23e1bc 100644 --- a/icicle/include/fields/snark_fields/bn254_base.cuh +++ b/icicle/include/icicle/fields/snark_fields/bn254_base.h @@ -1,9 +1,7 @@ #pragma once -#ifndef BN254_BASE_PARAMS_H -#define BN254_BASE_PARAMS_H -#include "fields/storage.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/params_gen.h" namespace bn254 { struct fq_config { @@ -17,5 +15,3 @@ namespace bn254 { static constexpr bool nonresidue_is_negative = true; }; } // namespace bn254 - -#endif diff --git a/icicle/include/fields/snark_fields/bn254_scalar.cuh b/icicle/include/icicle/fields/snark_fields/bn254_scalar.h similarity index 78% rename from icicle/include/fields/snark_fields/bn254_scalar.cuh rename to icicle/include/icicle/fields/snark_fields/bn254_scalar.h index 67478e2d7..edaecee20 100644 --- a/icicle/include/fields/snark_fields/bn254_scalar.cuh +++ b/icicle/include/icicle/fields/snark_fields/bn254_scalar.h @@ -1,10 +1,8 @@ #pragma once -#ifndef BN254_SCALAR_PARAMS_H -#define BN254_SCALAR_PARAMS_H -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/params_gen.h" namespace bn254 { struct fp_config { @@ -22,5 +20,3 @@ namespace bn254 { */ typedef Field scalar_t; } // namespace bn254 - -#endif diff --git a/icicle/include/icicle/fields/snark_fields/bw6_761_base.h b/icicle/include/icicle/fields/snark_fields/bw6_761_base.h new file mode 100644 index 000000000..48cb5d8eb --- /dev/null +++ b/icicle/include/icicle/fields/snark_fields/bw6_761_base.h @@ -0,0 +1,25 @@ +#pragma once + +#include "icicle/fields/storage.h" +#include "icicle/fields/params_gen.h" + +namespace bw6_761 { + struct fq_config { + static constexpr storage<24> modulus = {0x0000008b, 0xf49d0000, 0x70000082, 0xe6913e68, 0xeaf0a437, 0x160cf8ae, + 0x5667a8f8, 0x98a116c2, 0x73ebff2e, 0x71dcd3dc, 0x12f9fd90, 0x8689c8ed, + 0x25b42304, 0x03cebaff, 0xe584e919, 0x707ba638, 0x8087be41, 0x528275ef, + 0x81d14688, 0xb926186a, 0x04faff3e, 0xd187c940, 0xfb83ce0a, 0x0122e824}; + PARAMS(modulus) + }; + + // Note: this fq_config_g2 is a workaround to have different types for G1 and G2. + // Otherwise, they have the same types, thus APIs have the same type, thus we don't know which to call when + // specializing g2 templates. + struct fq_config_g2 { + static constexpr storage<24> modulus = {0x0000008b, 0xf49d0000, 0x70000082, 0xe6913e68, 0xeaf0a437, 0x160cf8ae, + 0x5667a8f8, 0x98a116c2, 0x73ebff2e, 0x71dcd3dc, 0x12f9fd90, 0x8689c8ed, + 0x25b42304, 0x03cebaff, 0xe584e919, 0x707ba638, 0x8087be41, 0x528275ef, + 0x81d14688, 0xb926186a, 0x04faff3e, 0xd187c940, 0xfb83ce0a, 0x0122e824}; + PARAMS(modulus) + }; +} // namespace bw6_761 diff --git a/icicle/include/fields/snark_fields/bw6_761_scalar.cuh b/icicle/include/icicle/fields/snark_fields/bw6_761_scalar.h similarity index 68% rename from icicle/include/fields/snark_fields/bw6_761_scalar.cuh rename to icicle/include/icicle/fields/snark_fields/bw6_761_scalar.h index 304595a70..bb0d98c02 100644 --- a/icicle/include/fields/snark_fields/bw6_761_scalar.cuh +++ b/icicle/include/icicle/fields/snark_fields/bw6_761_scalar.h @@ -2,9 +2,9 @@ #ifndef BW6_761_SCALAR_PARAMS_H #define BW6_761_SCALAR_PARAMS_H -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/snark_fields/bls12_377_base.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/snark_fields/bls12_377_base.h" namespace bw6_761 { typedef bls12_377::fq_config fp_config; diff --git a/icicle/include/fields/snark_fields/grumpkin_base.cuh b/icicle/include/icicle/fields/snark_fields/grumpkin_base.h similarity index 62% rename from icicle/include/fields/snark_fields/grumpkin_base.cuh rename to icicle/include/icicle/fields/snark_fields/grumpkin_base.h index 355f2f300..c65e9beeb 100644 --- a/icicle/include/fields/snark_fields/grumpkin_base.cuh +++ b/icicle/include/icicle/fields/snark_fields/grumpkin_base.h @@ -2,8 +2,8 @@ #ifndef GRUMPKIN_BASE_PARAMS_H #define GRUMPKIN_BASE_PARAMS_H -#include "fields/storage.cuh" -#include "fields/snark_fields/bn254_scalar.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/snark_fields/bn254_scalar.h" namespace grumpkin { typedef bn254::fp_config fq_config; diff --git a/icicle/include/fields/snark_fields/grumpkin_scalar.cuh b/icicle/include/icicle/fields/snark_fields/grumpkin_scalar.h similarity index 69% rename from icicle/include/fields/snark_fields/grumpkin_scalar.cuh rename to icicle/include/icicle/fields/snark_fields/grumpkin_scalar.h index 4354c8de2..60420c1b4 100644 --- a/icicle/include/fields/snark_fields/grumpkin_scalar.cuh +++ b/icicle/include/icicle/fields/snark_fields/grumpkin_scalar.h @@ -2,9 +2,9 @@ #ifndef GRUMPKIN_SCALAR_PARAMS_H #define GRUMPKIN_SCALAR_PARAMS_H -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/snark_fields/bn254_base.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/snark_fields/bn254_base.h" namespace grumpkin { typedef bn254::fq_config fp_config; diff --git a/icicle/include/fields/stark_fields/babybear.cuh b/icicle/include/icicle/fields/stark_fields/babybear.h similarity index 58% rename from icicle/include/fields/stark_fields/babybear.cuh rename to icicle/include/icicle/fields/stark_fields/babybear.h index 57f3f06e9..8000431aa 100644 --- a/icicle/include/fields/stark_fields/babybear.cuh +++ b/icicle/include/icicle/fields/stark_fields/babybear.h @@ -1,9 +1,9 @@ #pragma once -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/quartic_extension.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/quartic_extension.h" +#include "icicle/fields/params_gen.h" namespace babybear { struct fp_config { @@ -25,7 +25,12 @@ namespace babybear { typedef Field scalar_t; /** - * Extension field of `scalar_t` enabled if `-DEXT_FIELD` env variable is. + * Quartic extension field of `scalar_t` enabled if `-DEXT_FIELD` env variable is. */ - typedef ExtensionField extension_t; + typedef QuarticExtensionField q_extension_t; + + /** + * The default extension type + */ + typedef q_extension_t extension_t; } // namespace babybear diff --git a/icicle/include/fields/stark_fields/m31.cuh b/icicle/include/icicle/fields/stark_fields/m31.h similarity index 90% rename from icicle/include/fields/stark_fields/m31.cuh rename to icicle/include/icicle/fields/stark_fields/m31.h index b45592cec..d2b831a00 100644 --- a/icicle/include/fields/stark_fields/m31.cuh +++ b/icicle/include/icicle/fields/stark_fields/m31.h @@ -1,8 +1,10 @@ #pragma once -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/quartic_extension.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/complex_extension.h" +#include "icicle/fields/quartic_extension.h" +#include "icicle/fields/params_gen.h" namespace m31 { template @@ -14,9 +16,9 @@ namespace m31 { HOST_DEVICE_INLINE MersenneField(storage x) : Field{x} {} HOST_DEVICE_INLINE MersenneField(const Field& other) : Field(other) {} - static constexpr HOST_DEVICE_INLINE MersenneField zero() { return MersenneField(CONFIG::zero); } + static constexpr HOST_DEVICE_INLINE MersenneField zero() { return MersenneField{CONFIG::zero}; } - static constexpr HOST_DEVICE_INLINE MersenneField one() { return MersenneField(CONFIG::one.limbs[0]); } + static constexpr HOST_DEVICE_INLINE MersenneField one() { return MersenneField{CONFIG::one}; } static constexpr HOST_DEVICE_INLINE MersenneField from(uint32_t value) { return MersenneField(value); } @@ -94,7 +96,7 @@ namespace m31 { { const uint32_t modulus = MersenneField::get_modulus().limbs[0]; uint32_t tmp = (xs.storage >> 31) + (xs.storage & modulus); // max: 1 + 2^31-1 = 2^31 - tmp = (xs.storage >> 31) + (xs.storage & modulus); // max: 1 + 0 = 1 + tmp = (tmp >> 31) + (tmp & modulus); // max: 1 + 0 = 1 return MersenneField{{tmp == modulus ? 0 : tmp}}; } @@ -207,9 +209,9 @@ namespace m31 { static constexpr storage_array inv = {{{0x40000000}}}; // nonresidue to generate the extension field - static constexpr uint32_t nonresidue = 11; + static constexpr uint32_t nonresidue = 1; // true if nonresidue is negative. - static constexpr bool nonresidue_is_negative = false; + static constexpr bool nonresidue_is_negative = true; }; /** @@ -218,7 +220,17 @@ namespace m31 { typedef MersenneField scalar_t; /** - * Extension field of `scalar_t` enabled if `-DEXT_FIELD` env variable is. + * Quartic extension field of `scalar_t` enabled if `-DEXT_FIELD` env variable is. */ - typedef ExtensionField extension_t; -} // namespace m31 + typedef ComplexExtensionField c_extension_t; + + /** + * Quartic extension field of `scalar_t` enabled if `-DEXT_FIELD` env variable is. + */ + typedef QuarticExtensionField q_extension_t; + + /** + * The default extension type + */ + typedef q_extension_t extension_t; +} // namespace m31 \ No newline at end of file diff --git a/icicle/include/fields/stark_fields/stark252.cuh b/icicle/include/icicle/fields/stark_fields/stark252.h similarity index 86% rename from icicle/include/fields/stark_fields/stark252.cuh rename to icicle/include/icicle/fields/stark_fields/stark252.h index d61f757e0..6fb3da62e 100644 --- a/icicle/include/fields/stark_fields/stark252.cuh +++ b/icicle/include/icicle/fields/stark_fields/stark252.h @@ -1,10 +1,11 @@ #pragma once -#include "fields/storage.cuh" -#include "fields/field.cuh" -#include "fields/params_gen.cuh" +#include "icicle/fields/storage.h" +#include "icicle/fields/field.h" +#include "icicle/fields/params_gen.h" // modulus = 3618502788666131213697322783095070105623107215331596699973092056135872020481 (2^251+17*2^192+1) + namespace stark252 { struct fp_config { static constexpr storage<8> modulus = {0x00000001, 0x00000000, 0x00000000, 0x00000000, diff --git a/icicle/include/icicle/fields/storage.h b/icicle/include/icicle/fields/storage.h new file mode 100644 index 000000000..76245db16 --- /dev/null +++ b/icicle/include/icicle/fields/storage.h @@ -0,0 +1,61 @@ +#pragma once +#include + +#define LIMBS_ALIGNMENT(x) ((x) % 4 == 0 ? 16 : ((x) % 2 == 0 ? 8 : 4)) + +template +struct +#ifdef __CUDA_ARCH__ + __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) +#endif + storage; + +// Specialization for LIMBS_COUNT == 1 +template <> +struct +#ifdef __CUDA_ARCH__ + __align__(LIMBS_ALIGNMENT(1)) +#endif + storage<1> +{ + static constexpr unsigned LC = 1; + uint32_t limbs[1]; +}; + +// Specialization for LIMBS_COUNT == 3 +template <> +struct +#ifdef __CUDA_ARCH__ + __align__(LIMBS_ALIGNMENT(1)) +#endif + storage<3> +{ + static constexpr unsigned LC = 3; + uint32_t limbs[3]; +}; + +// General template for LIMBS_COUNT > 1 +template +struct +#ifdef __CUDA_ARCH__ + __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) +#endif + storage +{ + static_assert(LIMBS_COUNT % 2 == 0, "odd number of limbs is not supported\n"); + static constexpr unsigned LC = LIMBS_COUNT; + union { // works only with even LIMBS_COUNT + uint32_t limbs[LIMBS_COUNT]; + uint64_t limbs64[LIMBS_COUNT / 2]; + }; +}; + +template +struct +#ifdef __CUDA_ARCH__ + __align__(LIMBS_ALIGNMENT(LIMBS_COUNT)) +#endif + storage_array +{ + storage storages[OMEGAS_COUNT]; +}; \ No newline at end of file diff --git a/icicle/include/icicle/memory_tracker.h b/icicle/include/icicle/memory_tracker.h new file mode 100644 index 000000000..944ec9221 --- /dev/null +++ b/icicle/include/icicle/memory_tracker.h @@ -0,0 +1,57 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include "icicle/device.h" + +namespace icicle { + + class MemoryTracker + { + public: + // Add a new allocation + void add_allocation(void* address, size_t size, const Device& device) + { + std::lock_guard lock(mutex_); + allocations_.insert(std::make_pair(address, AllocationInfo{size, device})); + } + + // Remove an allocation + void remove_allocation(void* address) + { + std::lock_guard lock(mutex_); + allocations_.erase(address); + } + + // Check if an address is allocated by this tracker and get the device + std::optional identify_device(const void* address) + { + std::lock_guard lock(mutex_); + auto it = allocations_.upper_bound(address); + if (it == allocations_.begin()) { return std::nullopt; } + --it; + const char* start = static_cast(it->first); + const char* end = start + it->second.size_; + if (start <= static_cast(address) && static_cast(address) < end) { + return &it->second.device_; + } + return std::nullopt; + } + + private: + struct AllocationInfo { + size_t size_; + const Device device_; + + AllocationInfo(size_t size, const Device& device) : size_(size), device_(device) {} + }; + + std::map allocations_; + std::mutex mutex_; + }; + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/msm.h b/icicle/include/icicle/msm.h new file mode 100644 index 000000000..d42f4ba57 --- /dev/null +++ b/icicle/include/icicle/msm.h @@ -0,0 +1,109 @@ +#pragma once + +#include + +#include "icicle/errors.h" +#include "icicle/runtime.h" +#include "icicle/utils/utils.h" +#include "icicle/config_extension.h" + +#include "icicle/curves/affine.h" +#include "icicle/curves/projective.h" +#include "icicle/fields/field.h" + +namespace icicle { + + /*************************** Frontend APIs ***************************/ + /** + * @struct MSMConfig + * @brief Configuration for Multi-Scalar Multiplication (MSM). + */ + struct MSMConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + int precompute_factor; /**< Number of extra points to pre-compute for each point. See the + * precompute_msm_bases function; precompute_factor passed there needs to be equal to the + * one used here. Larger values decrease the number of computations to make, on-line memory + * footprint, but increase the static memory footprint. Default value: 1 (i.e., don't + * pre-compute). */ + int c; /**< \f$ c \f$ value, or "window bitsize", which is the main parameter of the "bucket method" used to solve + * the MSM problem. Larger value means more on-line memory footprint but also more parallelism and less + * computational complexity (up to a certain point). Default value: 0 (the optimal value of \f$ c \f$ is + * chosen automatically). */ + int bitsize; /**< Number of bits of the largest scalar. Typically equals the bitsize of scalar field, + * but if a different (better) upper bound is known, it should be reflected in this variable. + * Default value: 0 (set to the bitsize of scalar field). */ + int batch_size; /**< Number of MSMs to compute. Default value: 1. */ + bool are_points_shared_in_batch; /**< Bases are shared for batch. Set to true if all MSMs use the same bases. + Otherwise, the number of bases and number of scalars are expected to be equal. Default + value: true. */ + bool are_scalars_on_device; /**< True if scalars are on device, false if they're on host. Default value: false. */ + bool + are_scalars_montgomery_form; /**< True if scalars are in Montgomery form, false otherwise. Default value: true. */ + bool are_points_on_device; /**< True if points are on device, false if they're on host. Default value: false. */ + bool are_points_montgomery_form; /**< True if coordinates of points are in Montgomery form, false otherwise. Default + value: true. */ + bool are_results_on_device; /**< True if the results should be on device, false if they should be on host. If set + * to false, is_async won't take effect because a synchronization is needed to + * transfer results to the host. Default value: false. */ + bool is_async; /**< Whether to run the MSM asynchronously. If set to true, the MSM function will be + * non-blocking and you'd need to synchronize it explicitly by running + * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the MSM + * function will block the current CPU thread. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ + }; + + /** + * @brief Returns the default value of MSMConfig for the MSM function. + * + * @return Default value of MSMConfig. + */ + static MSMConfig default_msm_config() + { + MSMConfig config = { + nullptr, // stream + 1, // precompute_factor + 0, // c + 0, // bitsize + 1, // batch_size + true, // are_points_shared_in_batch + false, // are_scalars_on_device + false, // are_scalars_montgomery_form + false, // are_points_on_device + false, // are_points_montgomery_form + false, // are_results_on_device + false, // is_async + nullptr, // ext + }; + return config; + } + + /** + * @brief Computes MSM: \f$ MSM(s_i, P_i) = \sum_{i=1}^N s_i \cdot P_i \f$. + * + * @tparam S Scalar field type. + * @tparam A Type of bases \f$ \{P_i\} \f$ (typically an affine Weierstrass point). + * @tparam P Output type (typically a projective Weierstrass point). + * @param scalars Scalars \f$ s_i \f$. In case of batch MSM, the scalars from all MSMs are concatenated. + * @param bases Bases \f$ P_i \f$. In case of batch MSM, all unique points are concatenated, or shared. + * @param msm_size MSM size \f$ N \f$. If a batch of MSMs is computed, this is the size of one MSM. + * @param config Configuration for the MSM operation. + * @param results Buffer for the result (or results in the case of batch MSM). + * @return `SUCCESS` if the execution was successful, and an error code otherwise. + */ + template + eIcicleError msm(const S* scalars, const A* bases, int msm_size, const MSMConfig& config, P* results); + + /** + * @brief Precomputes bases for MSM. + * + * @tparam A Type of points (typically an affine Weierstrass point). + * @param input_bases Input bases for MSM precomputation. + * @param bases_size Number of input bases. + * @param config Configuration for the MSM precomputation. + * @param output_bases Buffer to store the precomputed bases. + * @return `SUCCESS` if the execution was successful, and an error code otherwise. + */ + template + eIcicleError msm_precompute_bases(const A* input_bases, int bases_size, const MSMConfig& config, A* output_bases); + +}; // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/ntt.h b/icicle/include/icicle/ntt.h new file mode 100644 index 000000000..ca71e5373 --- /dev/null +++ b/icicle/include/icicle/ntt.h @@ -0,0 +1,168 @@ +#pragma once + +#include + +#include "icicle/errors.h" +#include "icicle/runtime.h" + +#include "icicle/fields/field.h" +#include "icicle/utils/utils.h" +#include "icicle/config_extension.h" + +namespace icicle { + + /*************************** Frontend APIs ***************************/ + + /** + * @enum NTTDir + * @brief Specifies whether to perform forward NTT or inverse NTT (iNTT). + * + * Forward NTT computes polynomial evaluations from coefficients while inverse NTT computes coefficients from + * evaluations. + */ + enum class NTTDir { + kForward, /**< Perform forward NTT. */ + kInverse /**< Perform inverse NTT (iNTT). */ + }; + + /** + * @enum Ordering + * @brief Specifies the ordering of inputs and outputs for the NTT. + * + * Note: kNM, kMN are efficient when using the Mixed-Radix NTT algorithm. For Radix2, kNM==kNR, kMN==kRN. + * M stands for 'mixed' order. More precisely the vector is in digit-reverse order but the digits are internal to the + * implementation, thus should be considered mixed. + * This is useful when multiplying polynomials or doing element-wise operations such that the order is agnostic. + */ + enum class Ordering { + kNN, /**< Inputs and outputs are in natural-order. */ + kNR, /**< Inputs are in natural-order and outputs are in bit-reversed-order. */ + kRN, /**< Inputs are in bit-reversed-order and outputs are in natural-order. */ + kRR, /**< Inputs and outputs are in bit-reversed-order. */ + kNM, /**< Inputs are in natural-order and outputs are in digit-reversed-order. */ + kMN /**< Inputs are in digit-reversed-order and outputs are in natural-order. */ + }; + + /** + * @struct NTTConfig + * @brief Encodes NTT parameters to be passed into the NTT function. + * + * @tparam S Type of the coset generator. + */ + template + struct NTTConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + S coset_gen; /**< Coset generator. Default value is `S::one()` (no coset). */ + int batch_size; /**< Number of NTTs to compute. Default value is 1. */ + bool + columns_batch; /**< True if batches are columns of an input matrix (strided in memory). Default value is false. */ + Ordering ordering; /**< Ordering of inputs and outputs. Default value is `Ordering::kNN`. */ + bool are_inputs_on_device; /**< True if inputs are on device, false if on host. Default value is false. */ + bool are_outputs_on_device; /**< True if outputs are on device, false if on host. Default value is false. */ + bool is_async; /**< True if operation is asynchronous. Default value is false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ + }; + + /** + * @brief Returns the default value of NTTConfig for the NTT function. + * + * @tparam S Type of the coset generator. + * @return Default value of NTTConfig. + */ + template + static NTTConfig default_ntt_config() + { + NTTConfig config = { + nullptr, // stream + S::one(), // coset_gen + 1, // batch_size + false, // columns_batch + Ordering::kNN, // ordering + false, // are_inputs_on_device + false, // are_outputs_on_device + false, // is_async + }; + return config; + } + + /** + * @struct NTTInitDomainConfig + * @brief Configuration for initializing the NTT domain. + */ + struct NTTInitDomainConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_async; /**< True if operation is asynchronous. Default value is false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ + }; + + /** + * @brief Returns the default value of NTTInitDomainConfig. + * + * @return Default value of NTTInitDomainConfig. + */ + static NTTInitDomainConfig default_ntt_init_domain_config() + { + NTTInitDomainConfig config = { + nullptr, // stream + false // is_async + }; + return config; + } + + /** + * @brief Performs the Number Theoretic Transform (NTT). + * + * @tparam S Type of the coset generator. + * @tparam E Type of the elements. + * @param input Pointer to the input array. + * @param size Size of the input array. + * @param dir Direction of the NTT (forward or inverse). + * @param config Configuration for the NTT operation. + * @param output Pointer to the output array. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError ntt(const E* input, int size, NTTDir dir, const NTTConfig& config, E* output); + + /** + * @brief Initializes the NTT domain. + * + * @tparam S Type of the primitive root. + * @param primitive_root Primitive root of unity. + * @param config Configuration for initializing the NTT domain. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError ntt_init_domain(const S& primitive_root, const NTTInitDomainConfig& config); + + /** + * @brief Releases the NTT domain resources. + * + * @tparam S Type of the domain. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError ntt_release_domain(); + + /** + * @brief Gets the root of unity for a given maximum size. + * + * @tparam S Type of the domain. + * @param max_size Maximum size for the root of unity. + * @return S Root of unity. + */ + template + eIcicleError get_root_of_unity(uint64_t max_size, S* rou); + + /** + * @brief Gets the root of unity from the NTT domain for a given logarithmic size. + * + * @tparam S Type of the domain. + * @param logn Logarithmic size. + * @param rou Pointer to store the root of unity. This is an output parameter. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError get_root_of_unity_from_domain(uint64_t logn, S* rou /*OUT*/); + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/polynomials/default_backend/default_poly_backend.h b/icicle/include/icicle/polynomials/default_backend/default_poly_backend.h new file mode 100644 index 000000000..f0643f978 --- /dev/null +++ b/icicle/include/icicle/polynomials/default_backend/default_poly_backend.h @@ -0,0 +1,633 @@ + + +#include +#include "icicle/runtime.h" +#include "icicle/errors.h" +#include "icicle/ntt.h" +#include "icicle/polynomials/polynomial_context.h" +#include "icicle/vec_ops.h" + +namespace icicle { + + template + class DefaultPolynomialBackend : public IPolynomialBackend + { + typedef std::shared_ptr> PolyContext; + typedef typename IPolynomialContext::State State; + + int64_t* d_degree = nullptr; // used to avoid alloc/release every time + + public: + icicleStreamHandle m_stream; + DefaultPolynomialBackend(const icicleStreamHandle stream) : m_stream{stream} + { + ICICLE_CHECK(icicle_malloc_async((void**)&d_degree, sizeof(int64_t), m_stream)); + } + ~DefaultPolynomialBackend() noexcept { icicle_free_async(d_degree, m_stream); } + + void from_coefficients(PolyContext p, uint64_t nof_coefficients, const C* coefficients) override + { + p->from_coefficients(nof_coefficients, coefficients); + } + + void from_rou_evaluations(PolyContext p, uint64_t nof_evaluations, const I* evaluations) override + { + p->from_rou_evaluations(nof_evaluations, evaluations); + } + + void clone(PolyContext out, PolyContext in) override { out->clone(*in); } + + template + T* get_context_storage_mutable(PolyContext p) + { + return static_cast(IPolynomialBackend::get_context_storage_mutable(p)); + } + + template + const T* get_context_storage_immutable(PolyContext p) const + { + return static_cast(IPolynomialBackend::get_context_storage_immutable(p)); + } + + void slice(PolyContext out, PolyContext in, uint64_t offset, uint64_t stride, uint64_t size) override + { + assert_device_compatability({in}); + auto [in_coeffs, in_size] = in->get_coefficients(); + // size=0 means take as much as elements as there are to take + uint64_t out_size = (size > 0) ? size : (1 + (in_size - 1 - offset) / stride); + + out->allocate(out_size, State::Coefficients, false /*=memset zeros*/); + auto out_coeffs = get_context_storage_mutable(out); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + + ICICLE_CHECK(icicle::slice(in_coeffs, offset, stride, out_size, config, out_coeffs)); + } + + void add_sub(PolyContext& res, PolyContext a, PolyContext b, bool add1_sub0) + { + assert_device_compatability({a, b}); + + // add/sub can be done in both coefficients or evaluations, but operands must be in the same state. + // For evaluations, same state also means same number of evaluations (and on same domain). + // If not same state, compute in coefficients since computing in evaluations may require to interpolate a large + // size. Consider a+b where a is degree 128 and b degree 4. In coefficients b has 4 elements but in evaluations + // need 128. + const bool is_same_size = a->get_nof_elements() == b->get_nof_elements(); + const bool is_same_state = a->get_state() == b->get_state(); + const auto output_state = (is_same_size && is_same_state) ? a->get_state() : State::Coefficients; + const auto output_size = std::max(a->get_nof_elements(), b->get_nof_elements()); + + if (State::Coefficients == output_state) { + a->transform_to_coefficients(); + b->transform_to_coefficients(); + } + const auto a_mem_p = get_context_storage_immutable(a); + const auto b_mem_p = get_context_storage_immutable(b); + + res->allocate(output_size, output_state); + auto res_mem_p = get_context_storage_mutable(res); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + + const bool a_is_larger_than_b = a->get_nof_elements() > b->get_nof_elements(); + const bool b_is_larger_than_a = a->get_nof_elements() < b->get_nof_elements(); + + uint64_t min_op_size = std::min(a->get_nof_elements(), b->get_nof_elements()); + if (add1_sub0) { + ICICLE_CHECK(icicle::vector_add(a_mem_p, b_mem_p, min_op_size, config, res_mem_p)); + + if (a_is_larger_than_b) { + ICICLE_CHECK(icicle_copy( + res_mem_p + min_op_size, a_mem_p + min_op_size, sizeof(C) * (a->get_nof_elements() - min_op_size))); + } else if (b_is_larger_than_a) { + ICICLE_CHECK(icicle_copy( + res_mem_p + min_op_size, b_mem_p + min_op_size, sizeof(C) * (b->get_nof_elements() - min_op_size))); + } + return; + } + + // sub case + ICICLE_CHECK(icicle::vector_sub(a_mem_p, b_mem_p, min_op_size, config, res_mem_p)); + + if (a_is_larger_than_b) { + ICICLE_CHECK(icicle_copy( + res_mem_p + min_op_size, a_mem_p + min_op_size, sizeof(C) * (a->get_nof_elements() - min_op_size))); + } else if (b_is_larger_than_a) { + C zero = C::zero(); + config.is_a_on_device = false; + ICICLE_CHECK( + scalar_sub_vec(&zero, b_mem_p + min_op_size, b->get_nof_elements() - min_op_size, config, res_mem_p)); + } + } + + void add(PolyContext& res, PolyContext a, PolyContext b) override { add_sub(res, a, b, true /*=add*/); } + void subtract(PolyContext res, PolyContext a, PolyContext b) override { add_sub(res, a, b, false /*=sub*/); } + + void multiply(PolyContext c, PolyContext a, PolyContext b) override + { + assert_device_compatability({a, b}); + + const bool is_a_scalar = a->get_nof_elements() == 1; + const bool is_b_scalar = b->get_nof_elements() == 1; + + // TODO: can add kernel that takes the scalar as device memory + if (is_a_scalar) { + return multiply(c, b, get_coeff(a, 0)); + } else if (is_b_scalar) { + return multiply(c, a, get_coeff(b, 0)); + } + + const bool is_multiply_with_cosets = true; // TODO Yuval: check when faster to do so. + if (is_multiply_with_cosets) { return multiply_with_cosets(c, a, b); } + return multiply_with_padding(c, a, b); + } + + void multiply(PolyContext out, PolyContext p, D scalar) override + { + assert_device_compatability({p}); + + // element wise multiplication is similar both in coefficients and evaluations (regardless of order too) + const auto state = p->get_state(); + const auto N = p->get_nof_elements(); + + auto p_elements_p = + state == State::Coefficients ? get_context_storage_immutable(p) : get_context_storage_immutable(p); + + out->allocate(N, state, false /*=memset zeros*/); + auto out_evals_p = + state == State::Coefficients ? get_context_storage_mutable(out) : get_context_storage_mutable(out); + + auto config = default_vec_ops_config(); + config.is_a_on_device = false; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + icicle::scalar_mul_vec(&scalar, p_elements_p, N, config, out_evals_p); + } + + void multiply_with_padding(PolyContext c, PolyContext a, PolyContext b) + { + // TODO Yuval: by using the degree I can optimize the memory size and avoid redundant computations too + const uint64_t a_N_orig = a->get_nof_elements(); + const uint64_t b_N_orig = b->get_nof_elements(); + const uint64_t N = std::max(a_N_orig, b_N_orig); + const uint64_t c_N = 2 * N; + + // (1) transform a,b to 2N evaluations + a->transform_to_evaluations(c_N, true /*=reversed*/); + b->transform_to_evaluations(c_N, true /*=reversed*/); + auto [a_evals_p, a_N] = a->get_rou_evaluations(); + auto [b_evals_p, b_N] = b->get_rou_evaluations(); + + // (2) allocate c (c=a*b) and compute element-wise multiplication on evaluations + c->allocate(c_N, State::EvaluationsOnRou_Reversed, false /*=memset zeros*/); + auto c_evals_p = get_context_storage_mutable(c); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + ICICLE_CHECK(icicle::vector_mul(a_evals_p, b_evals_p, c_N, config, c_evals_p)); + } + + void multiply_with_cosets(PolyContext c, PolyContext a, PolyContext b) + { + const uint64_t a_N = a->get_nof_elements(); + const uint64_t b_N = b->get_nof_elements(); + const uint64_t N = std::max(a_N, b_N); + + // (1) transform a,b to coefficients such that both have N coefficients + a->transform_to_coefficients(N); + b->transform_to_coefficients(N); + auto [a_coeff_p, _] = a->get_coefficients(); + auto [b_coeff_p, __] = b->get_coefficients(); + // (2) allocate c (c=a*b) + const uint64_t c_N = 2 * N; + c->allocate(c_N, State::EvaluationsOnRou_Reversed, false /*=memset zeros*/); + auto c_evals_low_p = get_context_storage_mutable(c); + I* c_evals_high_p = c_evals_low_p + N; + + // (3) compute NTT of a,b on coset and write to c + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + ntt_config.ordering = Ordering::kNR; + ICICLE_CHECK(get_root_of_unity_from_domain((uint64_t)log2(c_N), &ntt_config.coset_gen)); + + ICICLE_CHECK(ntt(a_coeff_p, N, NTTDir::kForward, ntt_config, c_evals_low_p)); // a_H1 + ICICLE_CHECK(ntt(b_coeff_p, N, NTTDir::kForward, ntt_config, c_evals_high_p)); // b_H1 + + // (4) compute a_H1 * b_H1 inplace + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + ICICLE_CHECK(icicle::vector_mul(c_evals_low_p, c_evals_high_p, N, config, c_evals_high_p)); + + // (5) transform a,b to evaluations + a->transform_to_evaluations(N, true /*=reversed*/); + b->transform_to_evaluations(N, true /*=reversed*/); + auto [a_evals_p, a_nof_evals] = a->get_rou_evaluations(); + auto [b_evals_p, b_nof_evals] = b->get_rou_evaluations(); + + // (6) compute a_H0 * b_H0 + ICICLE_CHECK(icicle::vector_mul(a_evals_p, b_evals_p, N, config, c_evals_low_p)); + } + + void divide(PolyContext Q /*OUT*/, PolyContext R /*OUT*/, PolyContext a, PolyContext b) override + { + assert_device_compatability({a, b}); + + auto [a_coeffs, a_N] = a->get_coefficients(); + auto [b_coeffs, b_N] = b->get_coefficients(); + + const int64_t deg_a = degree(a); + const int64_t deg_b = degree(b); + ICICLE_ASSERT(deg_b >= 0) << "Polynomial division: divide by zero polynomial"; + + // init: Q=0, R=a + Q->allocate(deg_a - deg_b + 1, State::Coefficients, true /*=memset zeros*/); + auto Q_coeffs = get_context_storage_mutable(Q); + + // TODO Yuval: Can do better in terms of memory allocation? deg(R) <= deg(b) by definition but it starts from + // deg(a) + R->allocate(a_N, State::Coefficients, false /*=memset_zeros*/); + auto R_coeffs = get_context_storage_mutable(R); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = true; + config.is_async = true; + config.stream = m_stream; + config.is_result_on_device = true; + + ICICLE_CHECK(icicle::polynomial_division( + a_coeffs, deg_a, b_coeffs, deg_b, config, Q_coeffs, deg_a - deg_b + 1, R_coeffs, a_N)); + } + + void quotient(PolyContext Q, PolyContext op_a, PolyContext op_b) override + { + // TODO: can implement more efficiently? + auto R = std::make_shared>(m_stream); + divide(Q, R, op_a, op_b); + } + + void remainder(PolyContext R, PolyContext op_a, PolyContext op_b) override + { + // TODO: can implement more efficiently? + auto Q = std::make_shared>(m_stream); + divide(Q, R, op_a, op_b); + } + + void divide_by_vanishing_polynomial(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) override + { + assert_device_compatability({numerator}); + + // vanishing polynomial of degree N is the polynomial V(x) such that V(r)=0 for r Nth root-of-unity. + // For example for N=4 it vanishes on the group [1,W,W^2,W^3] where W is the 4th root of unity. In that + // case V(x)=(x-1)(x-w)(x-w^2)(x-w^3). It can be easily shown that V(x)=x^N-1. This holds since x^N=1 on this + // domain (since x is the Nth root of unity). + + // Note that we always represent polynomials with N elements for N a power of two. This is required for NTTs. + // In addition we consider deg(P) to be this number of elements N even though the real degree may be lower. for + // example 1+x-2x^2 is degree 2 but we store 4 elements and consider it degree 3. + + // when dividing a polynomial P(x)/V(x) (The vanishing polynomial) the output is of degree deg(P)-deg(V). There + // are three cases where V(x) divides P(x) (this is assumed since otherwise the output polynomial does not + // exist!): + // (1) deg(P)=2*deg(V): in that case deg(P/V)=deg(V)=N. This is an efficient case since on a domain of size N, + // the vanishing polynomial evaluates to a constant value. (2) deg(P)=deg(V)=N: in that case the output is a + // degree 0 polynomial. polynomial (i.e. scalar). (3) general case: deg(P)>2*deg(V): in that case deg(P) is a + // least 4*deg(V) since N is a power of two. This means that deg(P/V)=deg(P). For example deg(P)=16, deg(V)=4 + // --> deg(P/V)=12 ceiled to 16. + + // When computing we want to divide P(x)'s evals by V(x)'s evals. Since V(x)=0 on this domain we have to compute + // on a coset. + // for case (3) we must evaluate V(x) on deg(P) domain size and compute elementwise division on a coset. + // case (1) is more efficient because we need N evaluations of V(x) on a coset. Note that V(x)=constant on a + // coset of rou. This is because V(wu)=(wu)^N-1=W^N*u^N-1 = 1*u^N-1 (as w^N=1 for w Nth root of unity). case (2) + // can be computed like case (1). + + const bool is_case_2N = numerator->get_nof_elements() == 2 * vanishing_poly_degree; + const bool is_case_N = numerator->get_nof_elements() == vanishing_poly_degree; + if (is_case_2N) { + divide_by_vanishing_case_2N(out, numerator, vanishing_poly_degree); + } else if (is_case_N) { + divide_by_vanishing_case_N(out, numerator, vanishing_poly_degree); + } else { + divide_by_vanishing_general_case(out, numerator, vanishing_poly_degree); + } + } + + void divide_by_vanishing_general_case(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) + { + // General case: P(x)/V(x) where v is of degree N and p of any degree>N + + // (1) allocate vanishing polynomial in coefficients form + // TODO Yuval: maybe instead of taking numerator memory and modiyfing it diretcly add a state for evaluations + // on coset of rou. In that case I can remain in this state and also won't need to access input memory + // directly + numerator->transform_to_coefficients(); + auto numerator_coeffs = get_context_storage_mutable(numerator); + const auto N = numerator->get_nof_elements(); + ICICLE_ASSERT(vanishing_poly_degree <= N) << "divide_by_vanishing_polynomial(): degree is too large"; + + out->allocate(N, State::Coefficients, true /*=set zeros*/); + add_monomial_inplace(out, C::zero() - C::one(), 0); //-1 + add_monomial_inplace(out, C::one(), vanishing_poly_degree); //+x^n + + // (2) NTT on coset. Note that NTT on ROU evaluates to zeros for vanihsing polynomials by definition. + // Therefore evaluation on coset is required to compute non-zero evaluations, which make element-wise division + // possible + auto out_coeffs = get_context_storage_mutable(out); + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + ntt_config.ordering = Ordering::kNM; + ICICLE_CHECK(get_root_of_unity_from_domain((uint64_t)log2(2 * N), &ntt_config.coset_gen)); + + ICICLE_CHECK(ntt(out_coeffs, N, NTTDir::kForward, ntt_config, out_coeffs)); + ICICLE_CHECK(ntt(numerator_coeffs, N, NTTDir::kForward, ntt_config, numerator_coeffs)); + + // (3) element wise division + auto config = default_vec_ops_config(); + config.is_a_on_device = false; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + ICICLE_CHECK(icicle::vector_div(numerator_coeffs, out_coeffs, N, config, out_coeffs)); + + // (4) INTT back both numerator and out + ntt_config.ordering = Ordering::kMN; + ICICLE_CHECK(ntt(out_coeffs, N, NTTDir::kInverse, ntt_config, out_coeffs)); + ICICLE_CHECK(ntt(numerator_coeffs, N, NTTDir::kInverse, ntt_config, numerator_coeffs)); + } + + void divide_by_vanishing_case_2N(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) + { + // in that special case the numertaor has 2N elements and output will be N elements + ICICLE_ASSERT(numerator->get_nof_elements() == 2 * vanishing_poly_degree) + << "invalid input size. Expecting numerator to be of size 2N"; + + // In the case where deg(P)=2N, I can transform numerator to Reversed-evals -> The second half is + // a reversed-coset of size N with coset-gen the 2N-th root of unity. + const int N = vanishing_poly_degree; + numerator->transform_to_evaluations(2 * N, true /*=reversed*/); + // allocate output in coeffs because it will be calculated on a coset but I don't have such a state so will have + // to INTT back to coeffs + auto numerator_evals_reversed_p = get_context_storage_immutable(numerator); + out->allocate(N, State::Coefficients, false /*=set zeros*/); + auto out_evals_reversed_p = get_context_storage_mutable(out); + + auto ntt_config = default_ntt_config(); + ICICLE_CHECK(get_root_of_unity_from_domain((uint64_t)log2(2 * N), &ntt_config.coset_gen)); + // compute inv(u^N-1); + D v_coset_eval = D::inverse(D::pow(ntt_config.coset_gen, N) - D::one()); + + auto config = default_vec_ops_config(); + config.is_a_on_device = false; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + icicle::scalar_mul_vec( + &v_coset_eval, numerator_evals_reversed_p + N /*second half is the reversed coset*/, N, config, + out_evals_reversed_p); + + // INTT back from reversed evals on coset to coeffs + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + ntt_config.ordering = Ordering::kRN; + ntt(out_evals_reversed_p, N, NTTDir::kInverse, ntt_config, out_evals_reversed_p); + } + + void divide_by_vanishing_case_N(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) + { + // in that special case the numertaor has N elements and output will be N elements + ICICLE_ASSERT(numerator->get_nof_elements() == vanishing_poly_degree) + << "invalid input size. Expecting numerator to be of size N"; + + const int N = vanishing_poly_degree; + numerator->transform_to_coefficients(N); + auto numerator_evals_reversed_p = get_context_storage_immutable(numerator); + out->allocate(N, State::Coefficients, false /*=set zeros*/); + auto out_evals_reversed_p = get_context_storage_mutable(out); + + // (1) NTT numerator to coset evals (directly to out) + auto ntt_config = default_ntt_config(); + ICICLE_CHECK(get_root_of_unity_from_domain((uint64_t)log2(2 * N), &ntt_config.coset_gen)); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + ntt_config.ordering = Ordering::kNM; + ntt(numerator_evals_reversed_p, N, NTTDir::kForward, ntt_config, out_evals_reversed_p); + + // (2) divide by constant value (that V(x) evaluates to on the coset) + D v_coset_eval = D::inverse(D::pow(ntt_config.coset_gen, N) - D::one()); + + auto config = default_vec_ops_config(); + config.is_a_on_device = false; + config.is_b_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + icicle::scalar_mul_vec(&v_coset_eval, out_evals_reversed_p, N, config, out_evals_reversed_p); + + // (3) INTT back from coset to coeffs + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + ntt_config.ordering = Ordering::kMN; + ntt(out_evals_reversed_p, N, NTTDir::kInverse, ntt_config, out_evals_reversed_p); + } + + // arithmetic with monomials + void add_monomial_inplace(PolyContext& poly, C monomial_coeff, uint64_t monomial) override + { + const uint64_t new_nof_elements = std::max(poly->get_nof_elements(), monomial + 1); + poly->transform_to_coefficients(new_nof_elements); + auto coeffs = get_context_storage_mutable(poly); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = false; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + + ICICLE_CHECK(icicle::vector_add(coeffs + monomial, &monomial_coeff, 1, config, coeffs + monomial)); + } + + void sub_monomial_inplace(PolyContext& poly, C monomial_coeff, uint64_t monomial) override + { + add_monomial_inplace(poly, C::zero() - monomial_coeff, monomial); + } + + int64_t degree(PolyContext p) override { return degree_internal(p, p->get_nof_elements()); } + + // search degree starting from len, searching down (towards coeff0) + int64_t degree_internal(PolyContext p, uint64_t len) + { + auto [coeff, _] = p->get_coefficients(); + + int64_t h_degree; + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + + ICICLE_CHECK(icicle::highest_non_zero_idx(coeff, len, config, d_degree)); + // sync copy to make sure return value is copied to host + ICICLE_CHECK(icicle_copy_async(&h_degree, d_degree, sizeof(int64_t), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); // sync to make sure return value is copied to host + + return h_degree; + } + + public: + void evaluate(PolyContext p, const D* x, I* eval) override { evaluate_on_domain(p, x, 1, eval); } + + void evaluate_on_domain(PolyContext p, const D* domain, uint64_t size, I* evaluations /*OUT*/) override + { + auto [coeff, nof_coeff] = p->get_coefficients(); + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_b_on_device = !is_host_ptr(domain); + config.is_result_on_device = !is_host_ptr(evaluations); + config.is_async = true; + config.stream = m_stream; + ICICLE_CHECK(icicle::polynomial_eval(coeff, nof_coeff, domain, size, config, evaluations)); + } + + void evaluate_on_rou_domain(PolyContext p, uint64_t domain_log_size, I* evals /*OUT*/) override + { + const uint64_t poly_size = p->get_nof_elements(); + const uint64_t domain_size = 1 << domain_log_size; + const bool is_evals_on_host = is_host_ptr(evals); + + I* d_evals = evals; + // if evals on host, allocate memory + if (is_evals_on_host) { ICICLE_CHECK(icicle_malloc_async((void**)&d_evals, domain_size * sizeof(I), m_stream)); } + + // If domain size is smaller the polynomial size -> transform to evals and copy the evals with stride. + // Else, if in coeffs copy coeffs to evals mem and NTT inplace to compute the evals, else INTT to d_evals and + // back inplace to larger domain + const bool is_domain_size_smaller_than_poly_size = domain_size <= poly_size; + if (is_domain_size_smaller_than_poly_size) { + // TODO Yuval: in reversed evals, can reverse the first 'domain_size' elements to d_evals instead of + // transforming back to evals. + p->transform_to_evaluations(); + const auto stride = poly_size / domain_size; + + auto config = default_vec_ops_config(); + config.is_a_on_device = true; + config.is_result_on_device = true; + config.is_async = true; + config.stream = m_stream; + ICICLE_CHECK( + icicle::slice(get_context_storage_immutable(p), 0 /*offset*/, stride, domain_size, config, d_evals)); + } else { + ICICLE_CHECK(icicle_memset(d_evals, 0, domain_size * sizeof(I))); + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + // TODO Yuval: in evals I can NTT directly to d_evals without changing my state + switch (p->get_state()) { + case State::Coefficients: { + // copy to evals memory and inplace NTT of domain size + ICICLE_CHECK(icicle_copy(d_evals, get_context_storage_immutable(p), poly_size * sizeof(I))); + ntt_config.ordering = Ordering::kNN; + ntt(d_evals, domain_size, NTTDir::kForward, ntt_config, d_evals); + } break; + case State::EvaluationsOnRou_Natural: + case State::EvaluationsOnRou_Reversed: { + const bool is_from_natrual = p->get_state() == State::EvaluationsOnRou_Natural; + // INTT to coeffs and back to evals + ntt_config.ordering = is_from_natrual ? Ordering::kNM : Ordering::kRN; + ntt(get_context_storage_immutable(p), poly_size, NTTDir::kInverse, ntt_config, d_evals); + ntt_config.ordering = is_from_natrual ? Ordering::kMN : Ordering::kNN; + ntt(d_evals, poly_size, NTTDir::kForward, ntt_config, d_evals); + } break; + default: + ICICLE_ASSERT(false) << "Invalid state to compute evaluations"; + break; + } + } + + // release memory if allocated + if (is_evals_on_host) { + ICICLE_CHECK(icicle_copy_async(evals, d_evals, domain_size * sizeof(I), m_stream)); + ICICLE_CHECK(icicle_free_async(d_evals, m_stream)); + } + + // sync since user cannot reuse this stream so need to make sure evals are computed + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); // sync to make sure return value is copied to host + } + + uint64_t copy_coeffs(PolyContext op, C* out_coeffs, uint64_t start_idx, uint64_t end_idx) override + { + const uint64_t nof_coeffs = op->get_nof_elements(); + if (nullptr == out_coeffs) { return nof_coeffs; } // no allocated memory + + const bool is_valid_start_idx = start_idx < nof_coeffs; + const bool is_valid_end_idx = end_idx < nof_coeffs && end_idx >= start_idx; + const bool is_valid_indices = is_valid_start_idx && is_valid_end_idx; + if (!is_valid_indices) { + // return -1 instead? I could but 'get_coeff()' cannot with its current declaration + ICICLE_ASSERT(false) << "copy_coeffs() invalid indices"; + } + + op->transform_to_coefficients(); + auto [device_coeffs, _] = op->get_coefficients(); + const size_t nof_coeffs_to_copy = end_idx - start_idx + 1; + ICICLE_CHECK(icicle_copy_async(out_coeffs, device_coeffs + start_idx, nof_coeffs_to_copy * sizeof(C), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); // sync to make sure return value is copied + + return nof_coeffs_to_copy; + } + + // read coefficients to host + C get_coeff(PolyContext op, uint64_t coeff_idx) override + { + C host_coeff; + copy_coeffs(op, &host_coeff, coeff_idx, coeff_idx); + return host_coeff; + } + + std::tuple, uint64_t /*size*/> get_coefficients_view(PolyContext p) override + { + return p->get_coefficients_view(); + } + + inline void assert_device_compatability(const std::list& polys) const + { + for (const PolyContext& p : polys) { + ICICLE_CHECK(icicle_is_active_device_memory(get_context_storage_immutable(p))); + } + } + }; + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/icicle/polynomials/default_backend/default_poly_context.h b/icicle/include/icicle/polynomials/default_backend/default_poly_context.h new file mode 100644 index 000000000..bdd03bb5e --- /dev/null +++ b/icicle/include/icicle/polynomials/default_backend/default_poly_context.h @@ -0,0 +1,341 @@ + + +#include "icicle/runtime.h" +#include "icicle/errors.h" +#include "icicle/ntt.h" +#include "icicle/polynomials/polynomial_context.h" + +namespace icicle { + + // TODO Yuval : CPU polynomials don't need need to be copied to device + // actually it's true for any device that shares memory with host + + static uint64_t ceil_to_power_of_two(uint64_t x) { return 1ULL << uint64_t(ceil(log2(x))); } + /*============================== Polynomial Default-context ==============================*/ + + // checking whether a pointer is on host or device and asserts device matches the polynmoial device + static bool is_host_ptr(const void* ptr) + { + if (eIcicleError::SUCCESS == icicle_is_host_memory(ptr)) return true; + + ICICLE_ASSERT(eIcicleError::SUCCESS == icicle_is_active_device_memory(ptr)); + return false; + } + + template + class DefaultPolynomialContext : public IPolynomialContext + { + typedef IPolynomialContext PolyContext; + using typename IPolynomialContext::State; + using IPolynomialContext::ElementSize; + + protected: + State m_state = State::Invalid; // Current state of the polynomial context. + uint64_t m_nof_elements = 0; // Number of elements managed by the context. + + public: + icicleStreamHandle m_stream; + + DefaultPolynomialContext(const icicleStreamHandle stream) : m_stream{stream} + { + m_integrity_counter = std::make_shared(0); + } + ~DefaultPolynomialContext() { release(); } + + void allocate(uint64_t nof_elements, State init_state, bool is_memset_zeros) override + { + const bool is_already_allocated = this->m_nof_elements >= nof_elements; + this->set_state(init_state); + + if (is_already_allocated) { + // zero the extra elements, if exist + memset_zeros(this->m_storage, nof_elements, this->m_nof_elements); + return; + } + + release(); // in case allocated mem is too small and need to reallocate + this->m_nof_elements = allocate_mem(nof_elements, &this->m_storage, is_memset_zeros); + modified(); + } + + void memset_zeros(void* storage, uint64_t element_start_idx, uint64_t element_end_idx) + { + const uint64_t size = (element_end_idx - element_start_idx) * ElementSize; + if (0 == size) { return; } + + modified(); + + const auto offset = (void*)((uint64_t)storage + element_start_idx * ElementSize); + ICICLE_CHECK(icicle_memset_async(offset, 0, size, m_stream)); + } + + uint64_t allocate_mem(uint64_t nof_elements, void** storage /*OUT*/, bool is_memset_zeros) + { + const uint64_t nof_elements_nearset_power_of_two = ceil_to_power_of_two(nof_elements); + const uint64_t mem_size = nof_elements_nearset_power_of_two * ElementSize; + + ICICLE_CHECK(icicle_malloc_async((void**)storage, mem_size, m_stream)); + + if (is_memset_zeros) { + memset_zeros(*storage, 0, nof_elements_nearset_power_of_two); + } else { + // if allocating more memory than requested, memset only the pad area to avoid higher invalid coefficients + memset_zeros(*storage, nof_elements, nof_elements_nearset_power_of_two); + } + + return nof_elements_nearset_power_of_two; + } + + void set_storage(void* storage, uint64_t nof_elements) + { + release(); + m_storage = storage; + this->m_nof_elements = nof_elements; + + modified(); + } + + // Note: this is protected and only backend can call + void* get_storage_mutable() override + { + // since giving access to internal memory, cannot know if modified or not + // backend should not take it mutable if not mutating + modified(); + return m_storage; + } + const void* get_storage_immutable() override { return m_storage; } + + void extend_mem_and_pad(uint64_t nof_elements) + { + void* new_storage = nullptr; + const uint64_t new_nof_elements = allocate_mem(nof_elements, &new_storage, true /*=memset zeros*/); + const uint64_t old_mem_size = this->m_nof_elements * ElementSize; + + ICICLE_CHECK(icicle_copy_async(new_storage, m_storage, old_mem_size, m_stream)); + + set_storage(new_storage, new_nof_elements); + } + + void release() override + { + if (m_storage != nullptr) { ICICLE_CHECK(icicle_free_async(m_storage, m_stream)); } + + m_storage = nullptr; + this->m_nof_elements = 0; + + modified(); + } + + State get_state() const override { return m_state; } + void set_state(State state) { m_state = state; } + uint64_t get_nof_elements() const override { return m_nof_elements; } + + void from_coefficients(uint64_t nof_coefficients, const C* coefficients) override + { + const bool is_memset_zeros = coefficients == nullptr; + allocate(nof_coefficients, State::Coefficients, is_memset_zeros); + if (coefficients) { + ICICLE_CHECK(icicle_copy_async(m_storage, coefficients, nof_coefficients * sizeof(C), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); // protect against coefficients being released too soon + } + } + + void from_rou_evaluations(uint64_t nof_evaluations, const I* evaluations) override + { + const bool is_memset_zeros = evaluations == nullptr; + allocate(nof_evaluations, State::EvaluationsOnRou_Natural, is_memset_zeros); + if (evaluations) { + ICICLE_CHECK(icicle_copy_async(m_storage, evaluations, nof_evaluations * sizeof(C), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); // protect against evaluations being released too soon + } + } + + void clone(IPolynomialContext& from) override + { + switch (from.get_state()) { + case State::Coefficients: { + auto [coeffs, N_coeffs] = from.get_coefficients(); + from_coefficients(N_coeffs, coeffs); + } break; + case State::EvaluationsOnRou_Natural: { + auto [evals, N_evals] = from.get_rou_evaluations(); + from_rou_evaluations(N_evals, evals); + } break; + default: + ICICLE_ASSERT(false) << "clone() from non implemented polynomial state"; + } + + this->set_state(from.get_state()); // to handle both reversed evaluations case + } + + std::pair get_coefficients() override + { + transform_to_coefficients(); + return std::make_pair(static_cast(m_storage), this->m_nof_elements); + } + + std::tuple, uint64_t> get_coefficients_view() override + { + auto [coeffs, N] = get_coefficients(); + // when reading the pointer, if the counter was modified, the pointer is invalid + IntegrityPointer integrity_pointer(coeffs, m_integrity_counter, *m_integrity_counter); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); + return {std::move(integrity_pointer), N}; + } + + std::pair get_rou_evaluations() override + { + const bool is_reversed = this->m_state == State::EvaluationsOnRou_Reversed; + transform_to_evaluations(0, is_reversed); + return std::make_pair(static_cast(m_storage), this->m_nof_elements); + } + + void transform_to_coefficients(uint64_t nof_coefficients = 0) override + { + // cannot really get more coefficients but sometimes want to pad for NTT. In that case + // nof_coefficients>m_nof_elements + nof_coefficients = (nof_coefficients == 0) ? this->m_nof_elements : ceil_to_power_of_two(nof_coefficients); + const bool is_same_nof_coefficients = this->m_nof_elements == nof_coefficients; + const bool is_already_in_state = this->m_state == State::Coefficients && is_same_nof_coefficients; + if (is_already_in_state) { return; } + + ICICLE_ASSERT(nof_coefficients >= this->m_nof_elements) + << "polynomial shrinking not supported. Probably encountered a bug"; + + modified(); + + const bool is_already_in_coeffs = this->m_state == State::Coefficients; + // case 1: already in coefficients. Need to allocate larger memory and zero pad + if (is_already_in_coeffs) { + extend_mem_and_pad(nof_coefficients); + return; + } + + // case 2: transform from evaluations. May need to allocate larger memory + I* evals = static_cast(m_storage); + C* coeffs = static_cast(m_storage); + const bool is_allocate_new_mem = nof_coefficients > this->m_nof_elements; + if (is_allocate_new_mem) { + void* new_mem = nullptr; + nof_coefficients = allocate_mem(nof_coefficients, &new_mem, true /*=memset zeros*/); + coeffs = static_cast(new_mem); + } + + // transform from evaluations to coefficients + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + + ntt_config.ordering = (this->m_state == State::EvaluationsOnRou_Natural) ? Ordering::kNN : Ordering::kRN; + // Note: it is important to do the NTT with old size because padding in evaluations form is computing another + // (higher order) polynomial + ICICLE_CHECK(ntt(evals, this->m_nof_elements, NTTDir::kInverse, ntt_config, coeffs)); + this->set_state(State::Coefficients); + + if (is_allocate_new_mem) { set_storage(coeffs, nof_coefficients); } // release old memory and use new + } + + void transform_to_evaluations(uint64_t nof_evaluations = 0, bool is_reversed = false) override + { + // TODO Yuval: can maybe optimize this + nof_evaluations = (nof_evaluations == 0) ? this->m_nof_elements : ceil_to_power_of_two(nof_evaluations); + const bool is_same_nof_evaluations = nof_evaluations == this->m_nof_elements; + const bool is_same_order = is_reversed && this->m_state == State::EvaluationsOnRou_Reversed || + (!is_reversed && this->m_state == State::EvaluationsOnRou_Natural); + const bool is_already_in_state = is_same_nof_evaluations && is_same_order; + if (is_already_in_state) { return; } + + ICICLE_ASSERT(nof_evaluations >= this->m_nof_elements) + << "polynomial shrinking not supported. Probably encountered a bug"; + + modified(); + + // TODO Yuval: evaluations->evaluations with different ordering can be implemented via inplace reorder more + // efficiently than it is now + + // There are 3 cases: + // (1) coefficients to evaluations + // (1a) same size -> NTT (NR or NN) + // (1b) different_size -> alloc new mem, copy coeffs and NTT inplace + // (2) evaluations to evaluations (interpolation) + // transform to coefficients, extend memory, then NTT back to evals (NR or NN) + + const bool is_eval_to_eval = this->m_state != State::Coefficients; + // interpolating more points requires going back to coefficients first. Note that it muse be done with the + // original size. INTT after padding computes a higher degree polynomial + if (is_eval_to_eval) { transform_to_coefficients(); } + + // reaching this point means polynomial is in coefficient form + const bool is_allocate_new_mem = nof_evaluations > this->m_nof_elements; + // allocate more memory and copy+pad + if (is_allocate_new_mem) { extend_mem_and_pad(nof_evaluations); } + + C* coeffs = static_cast(m_storage); + I* evals = static_cast(m_storage); + auto ntt_config = default_ntt_config(); + ntt_config.are_inputs_on_device = true; + ntt_config.are_outputs_on_device = true; + ntt_config.is_async = true; + // already copied the coefficients with padding. Now computing evaluations. + ntt_config.ordering = is_reversed ? Ordering::kNR : Ordering::kNN; + ICICLE_CHECK(ntt(coeffs, nof_evaluations, NTTDir::kForward, ntt_config, evals)); + + this->set_state(is_reversed ? State::EvaluationsOnRou_Reversed : State::EvaluationsOnRou_Natural); + } + + void print(std::ostream& os) override + { + if (this->get_state() == State::Coefficients) { + print_coeffs(os); + } else { + print_evals(os); + } + } + + void print_coeffs(std::ostream& os) + { + transform_to_coefficients(); + auto host_coeffs = std::make_unique(this->m_nof_elements); + // using stream since previous ops may still be in progress. Sync stream before reading CPU mem + ICICLE_CHECK(icicle_copy_async(host_coeffs.get(), m_storage, this->m_nof_elements * sizeof(C), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); + + os << "(id=" << PolyContext::m_id << ")["; + for (size_t i = 0; i < this->m_nof_elements; ++i) { + os << host_coeffs[i]; + if (i < this->m_nof_elements - 1) { os << ", "; } + } + os << "] (state=coefficients)" << std::endl; + } + + void print_evals(std::ostream& os) + { + transform_to_evaluations(); + auto host_evals = std::make_unique(this->m_nof_elements); + // using stream since previous ops may still be in progress. Sync stream before reading CPU mem + ICICLE_CHECK(icicle_copy_async(host_evals.get(), m_storage, this->m_nof_elements * sizeof(I), m_stream)); + ICICLE_CHECK(icicle_stream_synchronize(m_stream)); + + os << "(id=" << PolyContext::m_id << ")["; + for (size_t i = 0; i < this->m_nof_elements; ++i) { + os << host_evals[i]; + if (i < this->m_nof_elements - 1) { os << ", "; } + } + + if (this->get_state() == State::EvaluationsOnRou_Reversed) { + os << "] (state=rou evaluations Reversed)" << std::endl; + } else { + os << "] (state=rou evaluations )" << std::endl; + } + } + + private: + // Members + void* m_storage = nullptr; + std::shared_ptr m_integrity_counter; // used to implement integrity of coefficients pointer + + void modified() { (*m_integrity_counter)++; } + }; + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/polynomials/polynomial_abstract_factory.h b/icicle/include/icicle/polynomials/polynomial_abstract_factory.h similarity index 94% rename from icicle/include/polynomials/polynomial_abstract_factory.h rename to icicle/include/icicle/polynomials/polynomial_abstract_factory.h index 528e393bd..6d0fd5549 100644 --- a/icicle/include/polynomials/polynomial_abstract_factory.h +++ b/icicle/include/icicle/polynomials/polynomial_abstract_factory.h @@ -4,7 +4,7 @@ #include "polynomial_backend.h" #include // For std::shared_ptr -namespace polynomials { +namespace icicle { /** * @brief Abstract factory for creating polynomial contexts and backends. @@ -20,7 +20,7 @@ namespace polynomials { * @tparam D Domain type, representing the input space of the polynomial. * @tparam I Image type, representing the output space of the polynomial. */ - template + template class AbstractPolynomialFactory { public: @@ -46,4 +46,4 @@ namespace polynomials { virtual ~AbstractPolynomialFactory() = default; }; -} // namespace polynomials +} // namespace icicle diff --git a/icicle/include/polynomials/polynomial_backend.h b/icicle/include/icicle/polynomials/polynomial_backend.h similarity index 91% rename from icicle/include/polynomials/polynomial_backend.h rename to icicle/include/icicle/polynomials/polynomial_backend.h index 45d27b49a..b85b45bc7 100644 --- a/icicle/include/polynomials/polynomial_backend.h +++ b/icicle/include/icicle/polynomials/polynomial_backend.h @@ -2,7 +2,7 @@ #include // for uint64_t, int64_t -namespace polynomials { +namespace icicle { /** * @brief Interface for the polynomial computational backend. @@ -51,7 +51,7 @@ namespace polynomials { // Method to access mutable storage within the context void* get_context_storage_mutable(PolyContext ctxt) { return ctxt->get_storage_mutable(); } - const void* get_context_storage_immutable(PolyContext ctxt) { return ctxt->get_storage_immutable(); } + const void* get_context_storage_immutable(PolyContext ctxt) const { return ctxt->get_storage_immutable(); } // Evaluation methods virtual void evaluate(PolyContext op, const D* domain_x, I* eval /*OUT*/) = 0; @@ -62,9 +62,8 @@ namespace polynomials { virtual C get_coeff(PolyContext op, uint64_t coeff_idx) = 0; virtual uint64_t copy_coeffs(PolyContext op, C* coeffs, uint64_t start_idx, uint64_t end_idx) = 0; - // Methods to get views of coefficients and evaluations, including device id - virtual std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> - get_coefficients_view(PolyContext p) = 0; + // get view of coefficients + virtual std::tuple, uint64_t /*size*/> get_coefficients_view(PolyContext p) = 0; }; -} // namespace polynomials +} // namespace icicle diff --git a/icicle/include/polynomials/polynomial_context.h b/icicle/include/icicle/polynomials/polynomial_context.h similarity index 93% rename from icicle/include/polynomials/polynomial_context.h rename to icicle/include/icicle/polynomials/polynomial_context.h index 2cd95bb4f..de5959c85 100644 --- a/icicle/include/polynomials/polynomial_context.h +++ b/icicle/include/icicle/polynomials/polynomial_context.h @@ -6,9 +6,9 @@ #include // for std::max #include // for uint64_t, etc. #include -#include "utils/integrity_pointer.h" +#include "icicle/utils/integrity_pointer.h" -namespace polynomials { +namespace icicle { template class IPolynomialBackend; @@ -71,8 +71,8 @@ namespace polynomials { virtual std::pair get_coefficients() = 0; virtual std::pair get_rou_evaluations() = 0; - // Methods to get views of coefficients - virtual std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> get_coefficients_view() = 0; + // get view of coefficients + virtual std::tuple, uint64_t /*size*/> get_coefficients_view() = 0; // Method for printing the context state to an output stream. virtual void print(std::ostream& os) = 0; @@ -88,4 +88,4 @@ namespace polynomials { public: const uint64_t m_id; }; -} // namespace polynomials +} // namespace icicle diff --git a/icicle/include/polynomials/polynomials.h b/icicle/include/icicle/polynomials/polynomials.h similarity index 79% rename from icicle/include/polynomials/polynomials.h rename to icicle/include/icicle/polynomials/polynomials.h index 7916b837f..c1da5c381 100644 --- a/icicle/include/polynomials/polynomials.h +++ b/icicle/include/icicle/polynomials/polynomials.h @@ -2,16 +2,13 @@ #include #include -#include "utils/integrity_pointer.h" -#include "fields/field_config.cuh" +#include "icicle/utils/integrity_pointer.h" #include "polynomial_context.h" #include "polynomial_backend.h" #include "polynomial_abstract_factory.h" -using namespace field_config; - -namespace polynomials { +namespace icicle { /** * @brief Represents a polynomial and provides operations for polynomial arithmetic, evaluation, and manipulation. @@ -79,7 +76,7 @@ namespace polynomials { uint64_t copy_coeffs(Coeff* host_coeffs, uint64_t start_idx, uint64_t end_idx) const; // Methods for obtaining a view of the coefficients - std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> get_coefficients_view(); + std::tuple, uint64_t /*size*/> get_coefficients_view(); // Overload stream insertion operator for printing. friend std::ostream& operator<<(std::ostream& os, Polynomial& poly) @@ -88,25 +85,12 @@ namespace polynomials { return os; } - // Static method to initialize the polynomial class with a factory for context and backend creation. - static void initialize(std::shared_ptr> factory) - { - std::atexit(cleanup); - s_factory = factory; - } - - // Cleanup method for releasing factory resources. - static void cleanup() { s_factory = nullptr; } - private: // The context of the polynomial, encapsulating its state. std::shared_ptr> m_context = nullptr; // The computational backend for the polynomial operations. std::shared_ptr> m_backend = nullptr; - // Factory for constructing the context and backend instances. - static inline std::shared_ptr> s_factory = nullptr; - public: Polynomial(); ~Polynomial() = default; @@ -120,13 +104,4 @@ namespace polynomials { std::shared_ptr> get_context() { return m_context; } }; - // explicit instantiation - - // Friend operator to allow multiplication with a scalar from the left-hand side - template - Polynomial operator*(const D& scalar, const Polynomial& rhs); - - // External template instantiation to ensure the template is compiled for specific types. - extern template class Polynomial; - -} // namespace polynomials +} // namespace icicle diff --git a/icicle/include/icicle/runtime.h b/icicle/include/icicle/runtime.h new file mode 100644 index 000000000..14b5eec51 --- /dev/null +++ b/icicle/include/icicle/runtime.h @@ -0,0 +1,271 @@ + +#pragma once + +#include "icicle/device.h" +#include "icicle/device_api.h" +#include "icicle/errors.h" + +using namespace icicle; + +/** + * @brief load icicle backed to process from install dir + * + + * @param path Path of the backend library or directory where backend libraries are installed + * @return eIcicleError Status of the loaded backend + */ +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); + +/** + * @brief Attempts to load the backend from either the environment variable or the default install directory. + * + * This function first checks if the environment variable `ICICLE_BACKEND_INSTALL_DIR` is set and points to an existing + * directory. If so, it attempts to load the backend from that directory. If the environment variable is not set or the + * directory does not exist, it falls back to the default directory (`/opt/icicle/backend`). If neither option is + * successful, the function returns an error. + * + * @return eIcicleError The status of the backend loading operation, indicating success or failure. + */ +extern "C" eIcicleError icicle_load_backend_from_env_or_default(); + +/** + * @brief Set active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_set_device(const icicle::Device& device); + +/** + * @brief Get active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_get_active_device(icicle::Device& device); + +/** + * @brief Check pointer is allocated on the host memory + * + + * @return eIcicleError::SUCCESS if true, otherwise eIcicleErrors::INVALID_POINTER + */ +extern "C" eIcicleError icicle_is_host_memory(const void* ptr); + +/** + * @brief Check pointer is allocated on the active device + * + + * @return eIcicleError::SUCCESS if true, otherwise eIcicleErrors::INVALID_POINTER + */ +extern "C" eIcicleError icicle_is_active_device_memory(const void* ptr); + +/** + * @brief Get number of available devices active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_get_device_count(int& device_count /*OUT*/); + +/** + * @brief Allocates memory on the specified device. + * + * @param ptr Pointer to the allocated memory. + * @param size Size of the memory to allocate. + * @return eIcicleError Status of the memory allocation. + */ +extern "C" eIcicleError icicle_malloc(void** ptr, size_t size); + +/** + * @brief Asynchronously allocates memory on the specified device. + * + + * @param ptr Pointer to the allocated memory. + * @param size Size of the memory to allocate. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory allocation. + */ +extern "C" eIcicleError icicle_malloc_async(void** ptr, size_t size, icicleStreamHandle stream); + +/** + * @brief Frees memory on the specified device. + * + * @param ptr Pointer to the memory to free. + * @return eIcicleError Status of the memory deallocation. + */ +extern "C" eIcicleError icicle_free(void* ptr); + +/** + * @brief Asynchronously frees memory on the specified device. + * + * @param ptr Pointer to the memory to free. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory deallocation. + */ +extern "C" eIcicleError icicle_free_async(void* ptr, icicleStreamHandle stream); + +/** + * @brief Gets the total and available memory on the specified device. + * + * @param total Total memory available on the device (output parameter). + * @param free Available memory on the device (output parameter). + * @return eIcicleError Status of the memory query. + */ +extern "C" eIcicleError icicle_get_available_memory(size_t& total /*OUT*/, size_t& free /*OUT*/); + +/** + * @brief Sets memory on the active device to a given value. + * + * This function sets a block of memory on the specified device to the specified value. + * + * @param ptr Pointer to the memory. + * @param value Value to set. + * @param size Size of the memory to set. + * @return eIcicleError Status of the memory set. + */ +extern "C" eIcicleError icicle_memset(void* ptr, int value, size_t size); + +/** + * @brief Asynchronously sets memory on the specified device to a given value. + * + * This function sets a block of memory on the specified device to the specified value using the given stream for the + * operation. + * + * @param ptr Pointer to the memory. + * @param value Value to set. + * @param size Size of the memory to set. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the memory set. + */ +extern "C" eIcicleError icicle_memset_async(void* ptr, int value, size_t size, icicleStreamHandle stream); + +// Data transfer + +/** + * @brief Copies data from the host/device to host/device. Data location is inferred from ptrs. + * + * @param dst Destination pointer. + * @param src Source pointer. + * @param size Size of the data to copy. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy(void* dst, const void* src, size_t size); + +/** + * @brief Copies data from the host/device to host/device async. Data location is inferred from ptrs. + * + * @param dst Destination pointer. + * @param src Source pointer. + * @param size Size of the data to copy. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy_async(void* dst, const void* src, size_t size, icicleStreamHandle stream); + +// Note: the following APIs can be used to avoid overhead of device inference (given ptr) + +/** + * @brief Copies data from the device to the host. + * + * @param dst Destination pointer on the host. + * @param src Source pointer on the device. + * @param size Size of the data to copy. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy_to_host(void* dst, const void* src, size_t size); + +/** + * @brief Asynchronously copies data from the device to the host. + * + * @param dst Destination pointer on the host. + * @param src Source pointer on the device. + * @param size Size of the data to copy. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy_to_host_async(void* dst, const void* src, size_t size, icicleStreamHandle stream); + +/** + * @brief Copies data from the host to the device. + * + * @param dst Destination pointer on the device. + * @param src Source pointer on the host. + * @param size Size of the data to copy. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy_to_device(void* dst, const void* src, size_t size); + +/** + * @brief Asynchronously copies data from the host to the device. + * + * @param dst Destination pointer on the device. + * @param src Source pointer on the host. + * @param size Size of the data to copy. + * @param stream Stream to use for the asynchronous operation. + * @return eIcicleError Status of the data copy. + */ +extern "C" eIcicleError icicle_copy_to_device_async(void* dst, const void* src, size_t size, icicleStreamHandle stream); + +// Stream management + +/** + * @brief Creates a stream on the specified device. + * + + * @param stream Pointer to the created stream. + * @return eIcicleError Status of the stream creation. + */ +extern "C" eIcicleError icicle_create_stream(icicleStreamHandle* stream); + +/** + * @brief Destroys a stream on the specified device. + * + + * @param stream The stream to destroy. + * @return eIcicleError Status of the stream destruction. + */ +extern "C" eIcicleError icicle_destroy_stream(icicleStreamHandle stream); + +// Synchronization + +/** + * @brief Synchronizes the specified stream. + * + * @param stream The stream to synchronize + * @return eIcicleError Status of the synchronization. + */ +extern "C" eIcicleError icicle_stream_synchronize(icicleStreamHandle stream); + +/** + * @brief Synchronizes the current device. + * + * @return eIcicleError Status of the synchronization. + */ +extern "C" eIcicleError icicle_device_synchronize(); + +/** + * @brief Retrieves the properties of the specified device. + * + * @param properties Structure to be filled with device properties. + * @return eIcicleError Status of the properties query. + */ +extern "C" eIcicleError icicle_get_device_properties(DeviceProperties& properties); + +/** + * @brief Checks if the specified device is available. + * + * @param dev The device to check for availability. + * @return eIcicleError Status of the device availability check. + * - `SUCCESS` if the device is available. + * - `INVALID_DEVICE` if the device is not available. + */ +extern "C" eIcicleError icicle_is_device_available(const Device& dev); + +/** + * @brief Retrieves the registered devices in comma-separated string. + * + * @param output buffer for writing registered devices types + * @return eIcicleError Status of the properties query. + */ +extern "C" eIcicleError icicle_get_registered_devices(char* output, size_t output_size); \ No newline at end of file diff --git a/icicle/include/utils/integrity_pointer.h b/icicle/include/icicle/utils/integrity_pointer.h similarity index 100% rename from icicle/include/utils/integrity_pointer.h rename to icicle/include/icicle/utils/integrity_pointer.h diff --git a/icicle/include/icicle/utils/log.h b/icicle/include/icicle/utils/log.h new file mode 100644 index 000000000..f8e0af490 --- /dev/null +++ b/icicle/include/icicle/utils/log.h @@ -0,0 +1,68 @@ +#pragma once + +#include +#include + +#define ICICLE_LOG_VERBOSE Log(Log::Verbose) +#define ICICLE_LOG_DEBUG Log(Log::Debug) +#define ICICLE_LOG_INFO Log(Log::Info) +#define ICICLE_LOG_WARNING Log(Log::Warning) +#define ICICLE_LOG_ERROR Log(Log::Error) + +class Log +{ +public: + enum eLogLevel { Verbose, Debug, Info, Warning, Error }; + + Log(eLogLevel level) : level{level} + { + if (level >= s_min_log_level) { oss << "[" << logLevelToString(level) << "] "; } + } + + ~Log() + { + if (level >= s_min_log_level) { std::cerr << oss.str() << std::endl; } + } + + template + Log& operator<<(const T& msg) + { + if (level >= s_min_log_level) { oss << msg; } + return *this; + } + + // Static method to set the log level + static void set_min_log_level(eLogLevel level) { s_min_log_level = level; } + +private: + eLogLevel level; + std::ostringstream oss; + + // Convert log level to string + const char* logLevelToString(eLogLevel level) const + { + switch (level) { + case Verbose: + return "DEBUG"; + case Debug: + return "DEBUG"; + case Info: + return "INFO"; + case Warning: + return "WARNING"; + case Error: + return "ERROR"; + default: + return ""; + } + } + + // logging message with level>=s_min_log_level +#if defined(NDEBUG) + static inline eLogLevel s_min_log_level = eLogLevel::Info; +#else + static inline eLogLevel s_min_log_level = eLogLevel::Debug; +#endif + + // Note: for verbose, need to explicitly call `set_min_log_level(eLogLevel::Verbose)` +}; diff --git a/icicle/include/icicle/utils/modifiers.h b/icicle/include/icicle/utils/modifiers.h new file mode 100644 index 000000000..ffe86d69c --- /dev/null +++ b/icicle/include/icicle/utils/modifiers.h @@ -0,0 +1,26 @@ + +// Note: currently icicle frontend is sharing headers with cuda backend so need this hack. TODO Yuval: decouple. +#pragma once + +#ifdef __CUDACC__ +#include +#if defined(DEVMODE) || defined(DEBUG) +#define INLINE_MACRO +#define UNROLL +#else +#define INLINE_MACRO __forceinline__ +#define UNROLL #pragma unroll +#endif + +#define HOST_INLINE __host__ INLINE_MACRO +#define DEVICE_INLINE __device__ INLINE_MACRO +#define HOST_DEVICE_INLINE __host__ __device__ INLINE_MACRO +#else // not CUDA +#define INLINE_MACRO +#define UNROLL +#define HOST_INLINE +#define DEVICE_INLINE +#define HOST_DEVICE_INLINE +#define __host__ +#define __device__ +#endif diff --git a/icicle/include/icicle/utils/timer.hpp b/icicle/include/icicle/utils/timer.hpp new file mode 100644 index 000000000..25439de34 --- /dev/null +++ b/icicle/include/icicle/utils/timer.hpp @@ -0,0 +1,34 @@ +#include +#include +#include + +/** + * @class Timer + * @brief class for simple time measurements and benchmarking of other libraries. + */ +class Timer +{ +private: + std::chrono::time_point start_point; + std::string m_timer_name; + +public: + Timer(std::string timer_name) + { + start_point = std::chrono::high_resolution_clock::now(); + m_timer_name = timer_name; + } + + ~Timer() { Stop(); } + + void Stop() + { + auto end_point = std::chrono::high_resolution_clock::now(); + auto start_time = std::chrono::time_point_cast(start_point).time_since_epoch().count(); + auto end_time = std::chrono::time_point_cast(end_point).time_since_epoch().count(); + auto duration = end_time - start_time; + + double dur_s = duration * 0.001; + std::cout << "Time of " << m_timer_name << ":\t" << dur_s << "ms\n"; + } +}; \ No newline at end of file diff --git a/icicle/include/utils/utils.h b/icicle/include/icicle/utils/utils.h similarity index 53% rename from icicle/include/utils/utils.h rename to icicle/include/icicle/utils/utils.h index 566492f60..7efcbc2ac 100644 --- a/icicle/include/utils/utils.h +++ b/icicle/include/icicle/utils/utils.h @@ -4,16 +4,6 @@ #define CONCAT_DIRECT(a, b) a##_##b #define CONCAT_EXPAND(a, b) CONCAT_DIRECT(a, b) // expand a,b before concatenation - -static unsigned int next_pow_of_two(unsigned int v) { - v--; - v |= v >> 1; - v |= v >> 2; - v |= v >> 4; - v |= v >> 8; - v |= v >> 16; - v++; - return v; -} +#define UNIQUE(a) CONCAT_EXPAND(a, __LINE__) #endif // ICICLE_UTILS_H \ No newline at end of file diff --git a/icicle/include/icicle/vec_ops.h b/icicle/include/icicle/vec_ops.h new file mode 100644 index 000000000..735aaf65c --- /dev/null +++ b/icicle/include/icicle/vec_ops.h @@ -0,0 +1,287 @@ +#pragma once + +#include + +#include "errors.h" +#include "runtime.h" + +#include "icicle/fields/field.h" +#include "icicle/utils/utils.h" +#include "icicle/config_extension.h" + +namespace icicle { + + /*************************** Frontend APIs ***************************/ + /** + * @brief Configuration for vector operations. + * @note APIs with a single input, ignore input b. + */ + struct VecOpsConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_a_on_device; /**< True if `a` is on the device, false if it is not. Default value: false. */ + bool is_b_on_device; /**< True if `b` is on the device, false if it is not. Default value: false. OPTIONAL. */ + bool is_result_on_device; /**< If true, the output is preserved on the device, otherwise on the host. Default value: + false. */ + bool is_async; /**< Whether to run the vector operations asynchronously. + If set to `true`, the function will be non-blocking and synchronization + must be explicitly managed using `cudaStreamSynchronize` or `cudaDeviceSynchronize`. + If set to `false`, the function will block the current CPU thread. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extension. */ + }; + + /** + * @brief Returns the default value of VecOpsConfig. + * + * @return Default value of VecOpsConfig. + */ + static VecOpsConfig default_vec_ops_config() + { + VecOpsConfig config = { + nullptr, // stream + false, // is_a_on_device + false, // is_b_on_device + false, // is_result_on_device + false, // is_async + }; + return config; + } + + // Element-wise vector operations + + /** + * @brief Adds two vectors element-wise. + * + * @tparam T Type of the elements in the vectors. + * @param vec_a Input vector `a`. + * @param vec_b Input vector `b`. + * @param size Number of elements in the vectors. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError vector_add(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Accumulates the elements of two vectors element-wise and stores the result in the first vector. + * + * @tparam T Type of the elements in the vectors. + * @param vec_a Input/output vector `a`. The result will be written back to this vector. + * @param vec_b Input vector `b`. + * @param size Number of elements in the vectors. + * @param config Configuration for the operation. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError vector_accumulate(T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config); + + /** + * @brief Subtracts vector `b` from vector `a` element-wise. + * + * @tparam T Type of the elements in the vectors. + * @param vec_a Input vector `a`. + * @param vec_b Input vector `b`. + * @param size Number of elements in the vectors. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError vector_sub(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Multiplies two vectors element-wise. + * + * @tparam T Type of the elements in the vectors. + * @param vec_a Input vector `a`. + * @param vec_b Input vector `b`. + * @param size Number of elements in the vectors. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError vector_mul(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Divides vector `a` by vector `b` element-wise. + * + * @tparam T Type of the elements in the vectors. + * @param vec_a Input vector `a`. + * @param vec_b Input vector `b`. + * @param size Number of elements in the vectors. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError vector_div(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Converts elements to and from Montgomery form. + * + * @tparam T Type of the elements. + * @param input Input vector. + * @param size Number of elements in the input vector. + * @param is_into True to convert into Montgomery form, false to convert out of Montgomery form. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError convert_montgomery(const T* input, uint64_t size, bool is_into, const VecOpsConfig& config, T* output); + + // Scalar-Vector operations + + /** + * @brief Adds a scalar to each element of a vector. + * + * @tparam T Type of the elements in the vector and the scalar. + * @param scalar_a Input scalar. + * @param vec_b Input vector. + * @param size Number of elements in the vector. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError scalar_add_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Subtracts each element of a vector from a scalar, elementwise (res[i]=scalar-vec[i]). + * + * @tparam T Type of the elements in the vector and the scalar. + * @param scalar_a Input scalar. + * @param vec_b Input vector. + * @param size Number of elements in the vector. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError scalar_sub_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + /** + * @brief Multiplies each element of a vector by a scalar. + * + * @tparam T Type of the elements in the vector and the scalar. + * @param scalar_a Input scalar. + * @param vec_b Input vector. + * @param size Number of elements in the vector. + * @param config Configuration for the operation. + * @param output Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError scalar_mul_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); + + // Matrix operations + + /** + * @brief Transposes a matrix. + * + * @tparam T Type of the elements in the matrix. + * @param mat_in Input matrix. + * @param nof_rows Number of rows in the input matrix. + * @param nof_cols Number of columns in the input matrix. + * @param config Configuration for the operation. + * @param mat_out Output matrix to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError + matrix_transpose(const T* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, T* mat_out); + + // Miscellaneous operations + + /** + * @brief Reorders the vector elements based on bit-reverse. That is out[i]=in[bitrev[i]]. + * + * @tparam T Type of the elements in the vector. + * @param vec_in Input vector. + * @param size Number of elements in the input vector. + * @param config Configuration for the operation. + * @param vec_out Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError bit_reverse(const T* vec_in, uint64_t size, const VecOpsConfig& config, T* vec_out); + + /** + * @brief Extracts a slice from a vector. + * + * @tparam T Type of the elements in the vector. + * @param vec_in Input vector. + * @param offset Offset from which to start the slice. + * @param stride Stride between elements in the slice. + * @param size Number of elements in the slice. + * @param config Configuration for the operation. + * @param vec_out Output vector to store the result. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError + slice(const T* vec_in, uint64_t offset, uint64_t stride, uint64_t size, const VecOpsConfig& config, T* vec_out); + + /** + * @brief Finds the highest non-zero index in a vector. + * + * @tparam T Type of the elements in the vector. + * @param vec_in Input vector. + * @param size Number of elements in the input vector. + * @param config Configuration for the operation. + * @param out_idx Output index of the highest non-zero element. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError highest_non_zero_idx(const T* vec_in, uint64_t size, const VecOpsConfig& config, int64_t* out_idx); + + /** + * @brief Evaluates a polynomial at given domain points. + * + * @tparam T Type of the elements in the polynomial and domain. + * @param coeffs Pointer to the array of coefficients of the polynomial. + * @param coeffs_size Number of coefficients in the polynomial. + * @param domain Pointer to the array of points at which to evaluate the polynomial. + * @param domain_size Number of domain points. + * @param config Configuration for the operation. + * @param evals Pointer to the array where the evaluated results will be stored. This is an output parameter. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError polynomial_eval( + const T* coeffs, + uint64_t coeffs_size, + const T* domain, + uint64_t domain_size, + const VecOpsConfig& config, + T* evals /*OUT*/); + + /** + * @brief Divides two polynomials. + * + * @tparam T Type of the elements in the polynomials. + * @param numerator Pointer to the array of coefficients of the numerator polynomial. + * @param numerator_deg Degree of the numerator polynomial. + * @param denominator Pointer to the array of coefficients of the denominator polynomial. + * @param denominator_deg Degree of the denominator polynomial. + * @param config Configuration for the operation. + * @param q_out Pointer to the array where the quotient will be stored. This is an output parameter. + * @param q_size Size of the quotient array. + * @param r_out Pointer to the array where the remainder will be stored. This is an output parameter. + * @param r_size Size of the remainder array. + * @return eIcicleError Error code indicating success or failure. + */ + template + eIcicleError polynomial_division( + const T* numerator, + int64_t numerator_deg, + const T* denumerator, + int64_t denumerator_deg, + const VecOpsConfig& config, + T* q_out /*OUT*/, + uint64_t q_size, + T* r_out /*OUT*/, + uint64_t r_size); + +} // namespace icicle \ No newline at end of file diff --git a/icicle/include/matrix/matrix.cuh b/icicle/include/matrix/matrix.cuh deleted file mode 100644 index e6e58fc1f..000000000 --- a/icicle/include/matrix/matrix.cuh +++ /dev/null @@ -1,14 +0,0 @@ -#pragma once -#ifndef MATRIX_H -#define MATRIX_H - -namespace matrix { - template - struct Matrix { - T* values; - size_t width; - size_t height; - }; -} // namespace matrix - -#endif \ No newline at end of file diff --git a/icicle/include/merkle-tree/merkle.cuh b/icicle/include/merkle-tree/merkle.cuh deleted file mode 100644 index ba50ff760..000000000 --- a/icicle/include/merkle-tree/merkle.cuh +++ /dev/null @@ -1,130 +0,0 @@ -#pragma once -#ifndef MERKLE_H -#define MERKLE_H - -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "utils/utils.h" -#include "hash/hash.cuh" -#include "matrix/matrix.cuh" - -#include -#include -#include -#include - -using namespace hash; -using matrix::Matrix; - -/** - * @namespace merkle_tree - * Implementation of the [Merkle tree](https://en.wikipedia.org/wiki/Merkle_tree) builder, - * parallelized for the use on GPU - */ -namespace merkle_tree { - static constexpr size_t GIGA = 1024 * 1024 * 1024; - - /// Bytes per stream - static constexpr uint64_t STREAM_CHUNK_SIZE = GIGA; - - /// Flattens the tree digests and sum them up to get - /// the memory needed to contain all the digests - static size_t get_digests_len(uint32_t height, uint32_t arity, uint32_t digest_elements) - { - size_t digests_len = 0; - size_t row_length = digest_elements; - for (int i = 0; i <= height; i++) { - digests_len += row_length; - row_length *= arity; - } - - return digests_len; - } - - template - void swap(T** r, T** s) - { - T* t = *r; - *r = *s; - *s = t; - } - - static unsigned int get_height(uint64_t number_of_elements) - { - unsigned int height = 0; - while (number_of_elements >>= 1) - ++height; - return height; - } - - /** - * @struct TreeBuilderConfig - * Struct that encodes various Tree builder parameters. - */ - struct TreeBuilderConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream id. */ - unsigned int arity; - unsigned int - keep_rows; /**< How many rows of the Merkle tree rows should be written to output. '0' means all of them */ - unsigned int - digest_elements; /** @param digest_elements the size of output for each bottom layer hash and compression. - * Will also be equal to the size of the root of the tree. Default value 1 */ - bool are_inputs_on_device; /**< True if inputs are on device and false if they're on host. Default value: false. */ - bool - are_outputs_on_device; /**< True if outputs are on device and false if they're on host. Default value: false. */ - bool is_async; /**< Whether to run the tree builder asynchronously. If set to `true`, the build_merkle_tree - * function will be non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the - * function will block the current CPU thread. */ - }; - - static TreeBuilderConfig - default_merkle_config(const device_context::DeviceContext& ctx = device_context::get_default_device_context()) - { - TreeBuilderConfig config = { - ctx, // ctx - 2, // arity - 0, // keep_rows - 1, // digest_elements - false, // are_inputes_on_device - false, // are_outputs_on_device - false, // is_async - }; - return config; - } - - /** - * Builds the Merkle tree - * - * @param leaves a pointer to the leaves layer. May be allocated on device or on host, regulated by the config - * Expected to have arity ^ (height) * input_block_len elements - * @param digests a pointer to the digests storage. May only be allocated on the host - * Expected to have `sum(digests_len * (arity ^ (i))) for i in [0..keep_rows]` - * @param height the height of the merkle tree - * @param input_block_len the size of input vectors at the bottom layer of the tree - * # Algorithm - * The function will split large tree into many subtrees of size that will fit `STREAM_CHUNK_SIZE`. - * Each subtree is build in it's own stream (there is a maximum number of streams) - * After all subtrees are constructed - the function will combine the resulting sub-digests into the final top-tree - */ - template - cudaError_t build_merkle_tree( - const Leaf* inputs, - Digest* digests, - unsigned int height, - unsigned int input_block_len, - const Hasher& compression, - const Hasher& bottom_layer, - const TreeBuilderConfig& config); - - template - cudaError_t mmcs_commit( - const Matrix* inputs, - const unsigned int number_of_inputs, - Digest* digests, - const Hasher& hasher, - const Hasher& compression, - const TreeBuilderConfig& tree_config); -} // namespace merkle_tree - -#endif \ No newline at end of file diff --git a/icicle/include/msm/msm.cuh b/icicle/include/msm/msm.cuh deleted file mode 100644 index 1bd27a48a..000000000 --- a/icicle/include/msm/msm.cuh +++ /dev/null @@ -1,182 +0,0 @@ -#pragma once -#ifndef MSM_H -#define MSM_H - -#include - -#include "curves/affine.cuh" -#include "curves/projective.cuh" -#include "fields/field.cuh" -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" - -/** - * @namespace msm - * Multi-scalar-multiplication, or MSM, is the sum of products of the form: - * \f[ - * MSM(s_i, P_i) = \sum_{i=1}^N s_i \cdot P_i - * \f] - * where \f$ \{P_i\} \f$ are elements of a certain group, \f$ \{s_i\} \f$ are scalars and \f$ N \f$ is the number of - * terms. In cryptographic applications, prime-order subgroups of elliptic curve groups are typically used, so we refer - * to group elements \f$ \{P_i\} \f$ as "points". - * - * To solve an MSM problem, we use an algorithm called the "bucket method". For a theoretical background on this - * algorithm, see [this](https://www.youtube.com/watch?v=Bl5mQA7UL2I) great talk by Gus Gutoski. - * - * This codebase is based on and evolved from Matter Labs' - * [Zprize - * submission](https://github.com/matter-labs/z-prize-msm-gpu/blob/main/bellman-cuda-rust/bellman-cuda-sys/native/msm.cu). - */ -namespace msm { - - /** - * @struct MSMConfig - * Struct that encodes MSM parameters to be passed into the [MSM](@ref MSM) function. The intended use of this struct - * is to create it using [default_msm_config](@ref default_msm_config) function and then you'll hopefully only need to - * change a small number of default values for each of your MSMs. - */ - struct MSMConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream id. */ - int points_size; /**< Number of points in the MSM. If a batch of MSMs needs to be computed, this should be - * a number of different points. So, if each MSM re-uses the same set of points, this - * variable is set equal to the MSM size. And if every MSM uses a distinct set of - * points, it should be set to the product of MSM size and [batch_size](@ref - * batch_size). Default value: 0 (meaning it's equal to the MSM size). */ - int precompute_factor; /**< The number of extra points to pre-compute for each point. See the - * [precompute_msm_points](@ref precompute_msm_points) function, `precompute_factor` passed - * there needs to be equal to the one used here. Larger values decrease the - * number of computations to make, on-line memory footprint, but increase the static - * memory footprint. Default value: 1 (i.e. don't pre-compute). */ - int c; /**< \f$ c \f$ value, or "window bitsize" which is the main parameter of the "bucket - * method" that we use to solve the MSM problem. As a rule of thumb, larger value - * means more on-line memory footprint but also more parallelism and less computational - * complexity (up to a certain point). Currently pre-computation is independent of - * \f$ c \f$, however in the future value of \f$ c \f$ here and the one passed into the - * [precompute_msm_points](@ref precompute_msm_points) function will need to be identical. - * Default value: 0 (the optimal value of \f$ c \f$ is chosen automatically). */ - int bitsize; /**< Number of bits of the largest scalar. Typically equals the bitsize of scalar field, - * but if a different (better) upper bound is known, it should be reflected in this - * variable. Default value: 0 (set to the bitsize of scalar field). */ - int large_bucket_factor; /**< Variable that controls how sensitive the algorithm is to the buckets that occur - * very frequently. Useful for efficient treatment of non-uniform distributions of - * scalars and "top windows" with few bits. Can be set to 0 to disable separate - * treatment of large buckets altogether. Default value: 10. */ - int batch_size; /**< The number of MSMs to compute. Default value: 1. */ - bool are_scalars_on_device; /**< True if scalars are on device and false if they're on host. Default value: - * false. */ - bool are_scalars_montgomery_form; /**< True if scalars are in Montgomery form and false otherwise. Default value: - * true. */ - bool are_points_on_device; /**< True if points are on device and false if they're on host. Default value: false. */ - bool are_points_montgomery_form; /**< True if coordinates of points are in Montgomery form and false otherwise. - * Default value: true. */ - bool are_results_on_device; /**< True if the results should be on device and false if they should be on host. If set - * to false, `is_async` won't take effect because a synchronization is needed to - * transfer results to the host. Default value: false. */ - bool is_big_triangle; /**< Whether to do "bucket accumulation" serially. Decreases computational complexity - * but also greatly decreases parallelism, so only suitable for large batches of MSMs. - * Default value: false. */ - bool is_async; /**< Whether to run the MSM asynchronously. If set to true, the MSM function will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the MSM - * function will block the current CPU thread. */ - }; - - /** - * A function that returns the default value of [MSMConfig](@ref MSMConfig) for the [MSM](@ref MSM) function. - * @return Default value of [MSMConfig](@ref MSMConfig). - */ - static MSMConfig - default_msm_config(const device_context::DeviceContext& ctx = device_context::get_default_device_context()) - { - MSMConfig config = { - ctx, // ctx - 0, // points_size - 1, // precompute_factor - 0, // c - 0, // bitsize - 10, // large_bucket_factor - 1, // batch_size - false, // are_scalars_on_device - false, // are_scalars_montgomery_form - false, // are_points_on_device - false, // are_points_montgomery_form - false, // are_results_on_device - false, // is_big_triangle - false, // is_async - }; - return config; - } - - /** - * A function that computes MSM: \f$ MSM(s_i, P_i) = \sum_{i=1}^N s_i \cdot P_i \f$. - * @param scalars Scalars \f$ s_i \f$. In case of batch MSM, the scalars from all MSMs are concatenated. - * @param points Points \f$ P_i \f$. In case of batch MSM, all *unique* points are concatenated. - * So, if for example all MSMs share the same base points, they can be repeated only once. - * @param msm_size MSM size \f$ N \f$. If a batch of MSMs (which all need to have the same size) is computed, this is - * the size of 1 MSM. - * @param config [MSMConfig](@ref MSMConfig) used in this MSM. - * @param results Buffer for the result (or results in the case of batch MSM). - * @tparam S Scalar field type. - * @tparam A The type of points \f$ \{P_i\} \f$ which is typically an [affine - * Weierstrass](https://hyperelliptic.org/EFD/g1p/auto-shortw.html) point. - * @tparam P Output type, which is typically a [projective - * Weierstrass](https://hyperelliptic.org/EFD/g1p/auto-shortw-projective.html) point in our codebase. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - * - */ - template - cudaError_t msm(const S* scalars, const A* points, int msm_size, MSMConfig& config, P* results); - - /** - * A function that precomputes MSM bases by extending them with their shifted copies. - * e.g.: - * Original points: \f$ P_0, P_1, P_2, ... P_{size} \f$ - * Extended points: \f$ P_0, P_1, P_2, ... P_{size}, 2^{l}P_0, 2^{l}P_1, ..., 2^{l}P_{size}, - * 2^{2l}P_0, 2^{2l}P_1, ..., 2^{2cl}P_{size}, ... \f$ - * @param points Points \f$ P_i \f$. In case of batch MSM, all *unique* points are concatenated. - * @param msm_size MSM size \f$ N \f$. If a batch of MSMs (which all need to have the same size) is computed, this is - * the size of 1 MSM. - * @param config [MSMConfig](@ref MSMConfig) used in this MSM. - * @param output_points Device-allocated buffer of size config.points_size * precompute_factor for the extended - * points. - * @tparam A The type of points \f$ \{P_i\} \f$ which is typically an [affine - * Weierstrass](https://hyperelliptic.org/EFD/g1p/auto-shortw.html) point. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - * - */ - template - cudaError_t precompute_msm_points(A* points, int msm_size, msm::MSMConfig& config, A* output_points); - - /** - * A function that precomputes MSM bases by extending them with their shifted copies. - * e.g.: - * Original points: \f$ P_0, P_1, P_2, ... P_{size} \f$ - * Extended points: \f$ P_0, P_1, P_2, ... P_{size}, 2^{l}P_0, 2^{l}P_1, ..., 2^{l}P_{size}, - * 2^{2l}P_0, 2^{2l}P_1, ..., 2^{2cl}P_{size}, ... \f$ - * @param bases Bases \f$ P_i \f$. In case of batch MSM, all *unique* points are concatenated. - * @param bases_size Number of bases. - * @param precompute_factor The number of total precomputed points for each base (including the base itself). - * @param _c This is currently unused, but in the future precomputation will need to be aware of - * the `c` value used in MSM (see [MSMConfig](@ref MSMConfig)). So to avoid breaking your code with this - * upcoming change, make sure to use the same value of `c` in this function and in respective MSMConfig. - * @param are_bases_on_device Whether the bases are on device. - * @param ctx Device context specifying device id and stream to use. - * @param output_bases Device-allocated buffer of size bases_size * precompute_factor for the extended bases. - * @tparam A The type of points \f$ \{P_i\} \f$ which is typically an [affine - * Weierstrass](https://hyperelliptic.org/EFD/g1p/auto-shortw.html) point. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - * - */ - template - [[deprecated("Use precompute_msm_points instead.")]] cudaError_t precompute_msm_bases( - A* bases, - int bases_size, - int precompute_factor, - int _c, - bool are_bases_on_device, - device_context::DeviceContext& ctx, - A* output_bases); - -} // namespace msm - -#endif \ No newline at end of file diff --git a/icicle/include/ntt/ntt.cuh b/icicle/include/ntt/ntt.cuh deleted file mode 100644 index 1f032bb4e..000000000 --- a/icicle/include/ntt/ntt.cuh +++ /dev/null @@ -1,174 +0,0 @@ -#pragma once -#ifndef NTT_H -#define NTT_H - -#include - -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "gpu-utils/sharedmem.cuh" -#include "utils/utils_kernels.cuh" -#include "utils/utils.h" - -/** - * @namespace ntt - * Number Theoretic Transform, or NTT is a version of [fast Fourier - * transform](https://en.wikipedia.org/wiki/Fast_Fourier_transform) where instead of real or complex numbers, inputs and - * outputs belong to certain finite groups or fields. NTT computes the values of a polynomial \f$ p(x) = p_0 + p_1 \cdot - * x + \dots + p_{n-1} \cdot x^{n-1} \f$ on special subfields called "roots of unity", or "twiddle factors" (optionally - * shifted by an additional element called "coset generator"): \f[ NTT(p) = \{ p(\omega^0), p(\omega^1), \dots, - * p(\omega^{n-1}) \} \f] Inverse NTT, or iNTT solves the inverse problem of computing coefficients of \f$ p(x) \f$ - * given evaluations \f$ \{ p(\omega^0), p(\omega^1), \dots, p(\omega^{n-1}) \} \f$. If not specified otherwise, - * \f$ n \f$ is a power of 2. - */ -namespace ntt { - - /** - * Generate a domain that supports all NTTs of sizes under a certain threshold. Note that the this function might - * be expensive, so if possible it should be called before all time-critical operations. - * It's assumed that during program execution only the coset generator might change, but twiddles stay fixed, so - * they are initialized at the first call of this function and don't change afterwards. - * @param primitive_root Primitive root in field `S` of order \f$ 2^s \f$. This should be the smallest power-of-2 - * order that's large enough to support any NTT you might want to perform. - * @param ctx Details related to the device such as its id and stream id. - * @param fast_twiddles_mode A mode where more memory is allocated for twiddle factors in exchange for faster compute. - * In this mode need additional 4N memory when N is the largest NTT size to be supported (which is derived by the - * primitive_root). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t init_domain(S primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode = false); - - /** - * Releases and deallocates resources associated with the domain initialized for performing NTTs. - * This function should be called to clean up resources once they are no longer needed. - * It's important to note that after calling this function, any operation that relies on the released domain will - * fail unless init_domain is called again to reinitialize the resources. Therefore, ensure that release_domain is - * only called when the operations requiring the NTT domain are completely finished and the domain is no longer - * needed. - * Also note that it is releasing the domain associated to the specific device. - * @param ctx Details related to the device context such as its id and stream id. - * @return `cudaSuccess` if the resource release was successful, indicating that the domain and its associated - * resources have been properly deallocated. Returns an error code otherwise, indicating failure to release - * the resources. The error code can be used to diagnose the problem. - * */ - template - cudaError_t release_domain(device_context::DeviceContext& ctx); - - /* Returns the basic root of unity Wn - * @param logn log size of the required root. - * @return Wn root of unity - */ - template - S get_root_of_unity(uint64_t max_size); - - /* Returns the basic root of unity Wn corresponding to the basic root used to initialize the domain. - * This function can be called only after InitializeDomain()! - * Useful when computing NTT on cosets. In that case we must use the root W_2n that is between W_n and W_n+1. - * @param logn log size of the required root. - * @param ctx Details related to the device such as its id and stream id. - * @return Wn root of unity corresponding to logn and the basic root used for initDomain(root) - */ - template - S get_root_of_unity_from_domain(uint64_t logn, device_context::DeviceContext& ctx); - - /** - * @enum NTTDir - * Whether to perform normal forward NTT, or inverse NTT (iNTT). Mathematically, forward NTT computes polynomial - * evaluations from coefficients while inverse NTT computes coefficients from evaluations. - */ - enum class NTTDir { kForward, kInverse }; - - /** - * @enum Ordering - * How to order inputs and outputs of the NTT. If needed, use this field to specify decimation: decimation in time - * (DIT) corresponds to `Ordering::kRN` while decimation in frequency (DIF) to `Ordering::kNR`. Also, to specify - * butterfly to be used, select `Ordering::kRN` for Cooley-Tukey and `Ordering::kNR` for Gentleman-Sande. There's - * no implication that a certain decimation or butterfly will actually be used under the hood, this is just for - * compatibility with codebases that use "decimation" and "butterfly" to denote ordering of inputs and outputs. - * - * Ordering options are: - * - kNN: inputs and outputs are natural-order (example of natural ordering: \f$ \{a_0, a_1, a_2, a_3, a_4, a_5, a_6, - * a_7\} \f$). - * - kNR: inputs are natural-order and outputs are bit-reversed-order (example of bit-reversed ordering: \f$ \{a_0, - * a_4, a_2, a_6, a_1, a_5, a_3, a_7\} \f$). - * - kRN: inputs are bit-reversed-order and outputs are natural-order. - * - kRR: inputs and outputs are bit-reversed-order. - * - * Mixed-Radix NTT: digit-reversal is a generalization of bit-reversal where the latter is a special case with 1b - * digits. Mixed-radix NTTs of different sizes would generate different reordering of inputs/outputs. Having said - * that, for a given size N it is guaranteed that every two mixed-radix NTTs of size N would have the same - * digit-reversal pattern. The following orderings kNM and kMN are conceptually like kNR and kRN but for - * mixed-digit-reordering. Note that for the cases '(1) NTT, (2) elementwise ops and (3) INTT' kNM and kMN are most - * efficient. - * Note: kNR, kRN, kRR refer to the radix-2 NTT reversal pattern. Those cases are supported by mixed-radix NTT with - * reduced efficiency compared to kNM and kMN. - * - kNM: inputs are natural-order and outputs are digit-reversed-order (=mixed). - * - kMN: inputs are digit-reversed-order (=mixed) and outputs are natural-order. - */ - enum class Ordering { kNN, kNR, kRN, kRR, kNM, kMN }; - - /** - * @enum NttAlgorithm - * Which NTT algorithm to use. options are: - * - Auto: implementation selects automatically based on heuristic. This value is a good default for most cases. - * - Radix2: explicitly select radix-2 NTT algorithm - * - MixedRadix: explicitly select mixed-radix NTT algorithm - */ - enum class NttAlgorithm { Auto, Radix2, MixedRadix }; - - /** - * @struct NTTConfig - * Struct that encodes NTT parameters to be passed into the [NTT](@ref NTT) function. - */ - template - struct NTTConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream. */ - S coset_gen; /**< Coset generator. Used to perform coset (i)NTTs. Default value: `S::one()` - * (corresponding to no coset being used). */ - int batch_size; /**< The number of NTTs to compute. Default value: 1. */ - bool columns_batch; /**< True if the batches are the columns of an input matrix - (they are strided in memory with a stride of ntt size) Default value: false. */ - Ordering ordering; /**< Ordering of inputs and outputs. See [Ordering](@ref Ordering). Default value: - * `Ordering::kNN`. */ - bool are_inputs_on_device; /**< True if inputs are on device and false if they're on host. Default value: false. */ - bool are_outputs_on_device; /**< If true, output is preserved on device, otherwise on host. Default value: false. */ - bool is_async; /**< Whether to run the NTT asynchronously. If set to `true`, the NTT function will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the NTT - * function will block the current CPU thread. */ - NttAlgorithm ntt_algorithm; /**< Explicitly select the NTT algorithm. Default value: Auto (the implementation - selects radix-2 or mixed-radix algorithm based on heuristics). */ - }; - - /** - * A function that returns the default value of [NTTConfig](@ref NTTConfig) for the [NTT](@ref NTT) function. - * @return Default value of [NTTConfig](@ref NTTConfig). - */ - template - NTTConfig - default_ntt_config(const device_context::DeviceContext& ctx = device_context::get_default_device_context()); - - /** - * A function that computes NTT or iNTT in-place. It's necessary to call [init_domain](@ref init_domain) with an - * appropriate primitive root before calling this function (only one call to `init_domain` should suffice for all - * NTTs). - * @param input Input of the NTT. Length of this array needs to be \f$ size \cdot config.batch\_size \f$. Note - * that if inputs are in Montgomery form, the outputs will be as well and vice-versa: non-Montgomery inputs produce - * non-Montgomety outputs. - * @param size NTT size. If a batch of NTTs (which all need to have the same size) is computed, this is the size - * of 1 NTT, so it must equal the size of `inout` divided by `config.batch_size`. - * @param dir Whether to compute forward or inverse NTT. - * @param config [NTTConfig](@ref NTTConfig) used in this NTT. - * @param output Buffer for the output of the NTT. Should be of the same size as `input`. - * @tparam E The type of inputs and outputs (i.e. coefficients \f$ \{p_i\} \f$ and values \f$ p(x) \f$). Must be a - * group. - * @tparam S The type of "twiddle factors" \f$ \{ \omega^i \} \f$. Must be a field. Often (but not always) `S=E`. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t ntt(const E* input, int size, NTTDir dir, NTTConfig& config, E* output); - -} // namespace ntt - -#endif \ No newline at end of file diff --git a/icicle/include/ntt/ntt_impl.cuh b/icicle/include/ntt/ntt_impl.cuh deleted file mode 100644 index 5f5bf1e29..000000000 --- a/icicle/include/ntt/ntt_impl.cuh +++ /dev/null @@ -1,48 +0,0 @@ -#pragma once -#ifndef _NTT_IMPL_H -#define _NTT_IMPL_H - -#include -#include "ntt/ntt.cuh" // for enum Ordering - -namespace mxntt { - - template - cudaError_t generate_external_twiddles_generic( - const S& basic_root, - S* external_twiddles, - S*& internal_twiddles, - S*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream); - - template - cudaError_t generate_external_twiddles_fast_twiddles_mode( - const S& basic_root, - S* external_twiddles, - S*& internal_twiddles, - S*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream); - - template - cudaError_t mixed_radix_ntt( - const E* d_input, - E* d_output, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - S* linear_twiddle, // twiddles organized as [1,w,w^2,...] for coset-eval in fast-tw mode - int ntt_size, - int max_logn, - int batch_size, - bool columns_batch, - bool is_inverse, - bool fast_tw, - ntt::Ordering ordering, - S* arbitrary_coset, - int coset_gen_index, - cudaStream_t cuda_stream); - -} // namespace mxntt -#endif //_NTT_IMPL_H \ No newline at end of file diff --git a/icicle/include/polynomials/cuda_backend/polynomial_cuda_backend.cuh b/icicle/include/polynomials/cuda_backend/polynomial_cuda_backend.cuh deleted file mode 100644 index da07254a2..000000000 --- a/icicle/include/polynomials/cuda_backend/polynomial_cuda_backend.cuh +++ /dev/null @@ -1,23 +0,0 @@ -#pragma once - -#include "gpu-utils/device_context.cuh" -#include "fields/field_config.cuh" -#include "polynomials/polynomials.h" - -using device_context::DeviceContext; - -namespace polynomials { - template - class CUDAPolynomialFactory : public AbstractPolynomialFactory - { - std::vector m_device_contexts; // device-id --> device context - std::vector m_device_streams; // device-id --> device stream. Storing the streams here as workaround - // since DeviceContext has a reference to a stream. - - public: - CUDAPolynomialFactory(); - ~CUDAPolynomialFactory(); - std::shared_ptr> create_context() override; - std::shared_ptr> create_backend() override; - }; -} // namespace polynomials \ No newline at end of file diff --git a/icicle/include/poseidon/constants.cuh b/icicle/include/poseidon/constants.cuh deleted file mode 100644 index cbdc6e2ab..000000000 --- a/icicle/include/poseidon/constants.cuh +++ /dev/null @@ -1,114 +0,0 @@ -#pragma once -#ifndef POSEIDON_CONSTANTS_H -#define POSEIDON_CONSTANTS_H - -#include - -namespace poseidon { -#define FIRST_FULL_ROUNDS true -#define SECOND_FULL_ROUNDS false - - /** - * For most of the Poseidon configurations this is the case - * TODO: Add support for different full rounds numbers - */ - const int FULL_ROUNDS_DEFAULT = 4; - - /** - * @struct PoseidonConstants - * This constants are enough to define a Poseidon instantce - * @param round_constants A pointer to round constants allocated on the device - * @param mds_matrix A pointer to an mds matrix allocated on the device - * @param non_sparse_matrix A pointer to non sparse matrix allocated on the device - * @param sparse_matrices A pointer to sparse matrices allocated on the device - */ - template - struct PoseidonConstants { - unsigned int arity; - unsigned int alpha; - unsigned int partial_rounds; - unsigned int full_rounds_half; - S* round_constants = nullptr; - S* mds_matrix = nullptr; - S* non_sparse_matrix = nullptr; - S* sparse_matrices = nullptr; - S domain_tag = S::zero(); - - PoseidonConstants() = default; - PoseidonConstants(const PoseidonConstants& other) = default; - - PoseidonConstants& operator=(PoseidonConstants const& other) - { - this->arity = other.arity; - this->alpha = other.alpha; - this->partial_rounds = other.partial_rounds; - this->full_rounds_half = other.full_rounds_half; - this->round_constants = other.round_constants; - this->mds_matrix = other.mds_matrix; - this->non_sparse_matrix = other.non_sparse_matrix; - this->sparse_matrices = other.sparse_matrices; - this->domain_tag = other.domain_tag; - - return *this; - } - }; - - /** - * @class PoseidonKernelsConfiguration - * Describes the logic of deriving CUDA kernels parameters - * such as the number of threads and the number of blocks - */ - class PoseidonKernelsConfiguration - { - public: - // The logic behind this is that 1 thread only works on 1 element - // We have {width} elements in each state, and {number_of_states} states total - static int number_of_threads(unsigned int width) { return 256 / width * width; } - - // The partial rounds operates on the whole state, so we define - // the parallelism params for processing a single hash preimage per thread - static const int singlehash_block_size = 128; - - static int hashes_per_block(unsigned int width) { return number_of_threads(width) / width; } - - static int number_of_full_blocks(unsigned int width, size_t number_of_states) - { - int total_number_of_threads = number_of_states * width; - return total_number_of_threads / number_of_threads(width) + - static_cast(total_number_of_threads % number_of_threads(width)); - } - - static int number_of_singlehash_blocks(size_t number_of_states) - { - return number_of_states / singlehash_block_size + static_cast(number_of_states % singlehash_block_size); - } - }; - - using PKC = PoseidonKernelsConfiguration; - - template - cudaError_t create_optimized_poseidon_constants( - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const S* round_constants, - const S* mds_matrix, - const S* non_sparse_matrix, - const S* sparse_matrices, - const S domain_tag, - PoseidonConstants* poseidon_constants, - device_context::DeviceContext& ctx); - - /** - * Loads pre-calculated optimized constants, moves them to the device - */ - template - cudaError_t - init_optimized_poseidon_constants(int arity, device_context::DeviceContext& ctx, PoseidonConstants* constants); - - template - cudaError_t release_optimized_poseidon_constants(PoseidonConstants* constants, device_context::DeviceContext& ctx); -} // namespace poseidon - -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/bls12_377_poseidon.h b/icicle/include/poseidon/constants/bls12_377_poseidon.h deleted file mode 100644 index ea04df77e..000000000 --- a/icicle/include/poseidon/constants/bls12_377_poseidon.h +++ /dev/null @@ -1,10809 +0,0 @@ -#pragma once -#ifndef BLS12_377_POSEIDON_H -#define BLS12_377_POSEIDON_H - -namespace poseidon_constants_bls12_377 { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 56; - - int partial_rounds_4 = 56; - - int partial_rounds_8 = 57; - - int partial_rounds_11 = 57; - - unsigned char poseidon_constants_2[] = { - 0xbf, 0xad, 0x19, 0x96, 0x67, 0xf4, 0xe9, 0xd2, 0x04, 0x7d, 0x48, 0x36, - 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0x21, 0x80, 0x05, 0xa9, 0x67, 0xc6, 0x00, 0x45, 0x0b, 0x59, 0xb9, 0xd1, - 0x83, 0xcd, 0x4b, 0xf6, 0x02, 0xc5, 0x6c, 0x11, 0x01, 0x00, 0x00, 0x00, - 0x00, 0x68, 0x70, 0xf9, 0x00, 0x00, 0x00, 0xe3, 0x8e, 0xcf, 0x0f, 0x44, - 0x01, 0x35, 0x74, 0x76, 0x4c, 0x08, 0xa9, 0xfa, 0x00, 0xdb, 0x89, 0xb0, - 0x08, 0xaf, 0x80, 0x11, 0x4c, 0x4b, 0x4b, 0x4b, 0x4b, 0xcb, 0x83, 0x06, - 0x5b, 0x5a, 0x5a, 0x4a, 0x0e, 0x5c, 0x7d, 0xb2, 0xd3, 0x62, 0x7e, 0x0e, - 0x7d, 0xc8, 0x92, 0x3e, 0x92, 0x01, 0x3b, 0xdc, 0xe2, 0x8c, 0x14, 0x0c, - 0xab, 0xaa, 0xaa, 0xaa, 0xaa, 0x6a, 0xe8, 0x49, 0x00, 0x00, 0x00, 0xc8, - 0x46, 0xaf, 0x04, 0x60, 0x00, 0x78, 0x0f, 0x36, 0xfa, 0xf8, 0x6a, 0xfe, - 0x97, 0xd8, 0x28, 0x80, 0x28, 0x9c, 0x2f, 0x05, 0x7a, 0x0d, 0xe5, 0x35, - 0x94, 0x57, 0x4f, 0x2f, 0x0e, 0xe5, 0x35, 0xc4, 0xd6, 0x79, 0xc5, 0x65, - 0x0e, 0x35, 0xf0, 0x8f, 0x80, 0xbb, 0xbe, 0x19, 0x3b, 0x3b, 0xc0, 0xe2, - 0xef, 0x1c, 0xc6, 0x0c, 0x01, 0x00, 0x00, 0x00, 0x00, 0xa0, 0x90, 0xc9, - 0xcd, 0xcc, 0xcc, 0x78, 0xd8, 0xbd, 0x2e, 0x15, 0xce, 0x80, 0xce, 0x0a, - 0x10, 0x16, 0x78, 0x75, 0x78, 0x03, 0x44, 0xdf, 0x19, 0x6d, 0xbc, 0x11, - 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x66, 0xfd, 0x00, 0x00, 0x00, 0x40, - 0x17, 0xa2, 0x34, 0x49, 0x01, 0xc0, 0xa2, 0x4b, 0x35, 0x7a, 0xdc, 0x8c, - 0xe4, 0x78, 0x67, 0xb7, 0x41, 0xce, 0xc7, 0x11, 0x46, 0x17, 0x5d, 0x74, - 0xd1, 0x05, 0x8e, 0x3b, 0x5d, 0x74, 0xd1, 0xed, 0xdc, 0x9b, 0x0b, 0xd2, - 0x8b, 0xc6, 0xc1, 0xc6, 0x0f, 0x39, 0xa4, 0xde, 0xe8, 0x8a, 0xd7, 0x5a, - 0x5e, 0xbc, 0x8b, 0x02, 0x22, 0x0b, 0x59, 0xc8, 0x42, 0x96, 0x9a, 0x53, - 0xea, 0x4d, 0x6f, 0x6a, 0xb1, 0xbd, 0x81, 0xce, 0x59, 0x58, 0x14, 0xdb, - 0xa1, 0x84, 0x44, 0x15, 0x47, 0xf9, 0x02, 0x4c, 0xd1, 0x65, 0x8d, 0x0a - }; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/bls12_381_poseidon.h b/icicle/include/poseidon/constants/bls12_381_poseidon.h deleted file mode 100644 index c542bfc92..000000000 --- a/icicle/include/poseidon/constants/bls12_381_poseidon.h +++ /dev/null @@ -1,6827 +0,0 @@ -#pragma once -#ifndef BLS12_381_POSEIDON_H -#define BLS12_381_POSEIDON_H - -namespace poseidon_constants_bls12_381 { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 55; - - int partial_rounds_4 = 56; - - int partial_rounds_8 = 57; - - int partial_rounds_11 = 57; - - unsigned char poseidon_constants_2[] = { - 0xd8, 0xd3, 0x6e, 0x9d, 0x00, 0x0a, 0x32, 0xa7, 0x36, 0x8b, 0x75, 0xa2, 0x92, 0xac, 0x1e, 0x50, 0x24, 0x4a, 0xbb, - 0x1d, 0x86, 0x51, 0xbd, 0x23, 0x7a, 0xe1, 0x3a, 0xfa, 0x4b, 0x06, 0x9f, 0x66, 0x15, 0x3f, 0x9d, 0x2b, 0x84, 0xab, - 0x72, 0x6e, 0x34, 0x27, 0xac, 0x45, 0x96, 0x7c, 0xe6, 0xee, 0x6c, 0xa6, 0x4f, 0xc8, 0xf2, 0x7f, 0x53, 0xe4, 0x36, - 0xca, 0xac, 0xfb, 0xde, 0xa8, 0x61, 0x0a, 0xd5, 0x65, 0x81, 0x12, 0x71, 0x47, 0x23, 0x1e, 0x30, 0x49, 0xaa, 0x1a, - 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0xcf, 0xf3, 0xc2, 0x08, 0x0c, 0xc3, 0x9e, 0x0f, 0x12, 0x8e, 0xc3, 0x20, 0x7d, 0x25, 0x19, 0x76, 0x42, 0x1d, 0xd6, - 0x85, 0x70, 0xa2, 0x75, 0x71, 0x0a, 0x0b, 0x01, 0x00, 0x00, 0x80, 0xd0, 0x45, 0x17, 0xdd, 0x44, 0x29, 0xe7, 0x22, - 0x1a, 0x4b, 0x4c, 0x44, 0xbf, 0x7c, 0x1a, 0xac, 0x07, 0x94, 0x2b, 0x48, 0x39, 0x83, 0xd0, 0x04, 0x5b, 0xab, 0xa8, - 0x6e, 0xb3, 0x90, 0x85, 0x2c, 0x58, 0xc8, 0x42, 0x16, 0xa6, 0x87, 0xa5, 0x37, 0xe1, 0xa4, 0x8e, 0x32, 0x8a, 0x4c, - 0x12, 0x99, 0xe5, 0x14, 0x75, 0x7a, 0xcf, 0x61, 0x03, 0xc0, 0xc2, 0xa7, 0xce, 0x64}; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/bn254_poseidon.h b/icicle/include/poseidon/constants/bn254_poseidon.h deleted file mode 100644 index 50fe5de4d..000000000 --- a/icicle/include/poseidon/constants/bn254_poseidon.h +++ /dev/null @@ -1,10809 +0,0 @@ -#pragma once -#ifndef BN254_POSEIDON_H -#define BN254_POSEIDON_H - -namespace poseidon_constants_bn254 { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 56; - - int partial_rounds_4 = 56; - - int partial_rounds_8 = 57; - - int partial_rounds_11 = 57; - - unsigned char poseidon_constants_2[] = { - 0x43, 0x2b, 0xcf, 0x42, 0xd2, 0x41, 0xdc, 0xcd, 0x76, 0xa4, 0xf0, 0xb6, - 0x41, 0x8b, 0x17, 0x74, 0x05, 0x9e, 0xb4, 0x12, 0x57, 0x9f, 0xe9, 0x27, - 0x7c, 0x56, 0xb9, 0x1b, 0xfd, 0x51, 0x4c, 0x2c, 0x04, 0x41, 0x05, 0x5b, - 0x0e, 0x75, 0xa2, 0x73, 0x5b, 0x9a, 0xc9, 0x22, 0xdc, 0xe7, 0xde, 0x3f, - 0x1b, 0x6f, 0x35, 0xa5, 0xcd, 0xbf, 0xfc, 0xc6, 0x1e, 0xc6, 0xb9, 0xac, - 0x18, 0x8d, 0x5f, 0x1c, 0x90, 0xba, 0x41, 0x45, 0x1c, 0x0f, 0x38, 0x4e, - 0x7a, 0xe6, 0x82, 0x4a, 0x31, 0x20, 0x28, 0x7c, 0x8a, 0xe9, 0x74, 0xd5, - 0xb8, 0x95, 0x55, 0x04, 0x4c, 0x1e, 0x54, 0xb4, 0x88, 0x19, 0x3c, 0x2d, - 0x98, 0xcb, 0xe0, 0x86, 0xf6, 0x1d, 0x99, 0x4e, 0x10, 0xeb, 0xc7, 0xe1, - 0x31, 0x9f, 0x2c, 0x17, 0x6a, 0x83, 0x20, 0x03, 0xc5, 0x65, 0xbe, 0x74, - 0xf5, 0xbe, 0x46, 0xca, 0x78, 0xe6, 0x2a, 0x22, 0xe1, 0xff, 0xfb, 0x21, - 0xdf, 0xb8, 0x5a, 0x16, 0xe3, 0x3f, 0x2c, 0x18, 0x0f, 0x28, 0x40, 0xea, - 0x11, 0x7d, 0x89, 0x27, 0xc1, 0x4d, 0x1f, 0x9d, 0x05, 0x27, 0xab, 0xfe, - 0xd3, 0xfd, 0x3e, 0x08, 0x4a, 0x55, 0x87, 0xe4, 0xec, 0xad, 0x6e, 0x10, - 0xbf, 0x84, 0x15, 0xb4, 0xa1, 0x35, 0xfa, 0x65, 0x5e, 0x1c, 0x34, 0x17, - 0xfb, 0x79, 0x30, 0x4b, 0x01, 0x45, 0x91, 0x02, 0xc8, 0x50, 0x18, 0x23, - 0x45, 0x1b, 0xf9, 0x28, 0xeb, 0x8f, 0x75, 0x6d, 0x1a, 0x26, 0x84, 0x3f, - 0xd6, 0x66, 0xb5, 0x3e, 0x97, 0xdb, 0x12, 0xca, 0x54, 0x0c, 0xbe, 0xf9, - 0x9b, 0xcc, 0x5c, 0x46, 0x0c, 0x5b, 0x86, 0x20, 0x43, 0xcf, 0x4a, 0x05, - 0x78, 0x6f, 0x53, 0x83, 0x33, 0x60, 0x35, 0x70, 0xd8, 0x32, 0x73, 0x1c, - 0x6a, 0x83, 0x12, 0x5c, 0x81, 0xc6, 0xe5, 0x4b, 0x9d, 0xb8, 0x84, 0x84, - 0xc6, 0x46, 0x7e, 0x13, 0xda, 0x79, 0xf0, 0x99, 0x0c, 0x61, 0xec, 0xa4, - 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0x15, 0xc4, 0xa7, 0x7b, 0x1e, 0x8d, 0xfc, 0x91, 0x7b, 0x29, 0x32, 0xf7, - 0x56, 0x6b, 0xc1, 0xf5, 0x57, 0xd1, 0x84, 0x74, 0x14, 0x85, 0x80, 0x21, - 0xb9, 0xda, 0xb6, 0x1d, 0x9a, 0x64, 0xb8, 0x3c, 0x60, 0xb7, 0xa2, 0x57, - 0xc9, 0xe0, 0x35, 0xb0, 0x5f, 0xae, 0x64, 0x3d, 0x60, 0x08, 0x74, 0x50, - 0xae, 0x71, 0x53, 0x0c, 0x1e, 0x70, 0x3f, 0x1d, 0x42, 0x28, 0x49, 0xf2, - 0xc7, 0x64, 0x2f, 0x3a, 0xea, 0x3b, 0x31, 0x1f, 0x3e, 0x10, 0x51, 0xd9, - 0x4f, 0x70, 0x93, 0x93, 0xe5, 0x84, 0xb2, 0x0b, 0xb6, 0x64, 0xe1, 0x77, - 0x62, 0x8c, 0x7a, 0x29, 0x25, 0x18, 0x49, 0xf2, 0xc7, 0x64, 0x2f, 0x3a, - 0xea, 0x3b, 0x31, 0x1f, 0x3e, 0x10, 0x51, 0xd9, 0x4f, 0x70, 0x93, 0x93, - 0xe5, 0x84, 0xb2, 0x0b, 0xb6, 0x64, 0xe1, 0x77, 0x62, 0x8c, 0x7a, 0x29, - 0x23, 0x95, 0x78, 0x88, 0x82, 0xab, 0x12, 0x69, 0xbc, 0xcc, 0xd0, 0xd4, - 0x21, 0xfa, 0x5a, 0x05, 0x25, 0x7b, 0xcc, 0xf9, 0x91, 0x4c, 0xbf, 0xc0, - 0x15, 0x51, 0xb3, 0xc6, 0xce, 0xdb, 0xb2, 0x2a, 0x2d, 0x2c, 0x5a, 0xea, - 0x3c, 0x83, 0xd5, 0xfd, 0x68, 0x3d, 0x2e, 0x41, 0x87, 0x41, 0xf7, 0x79, - 0x44, 0x06, 0xf9, 0xda, 0xff, 0x67, 0x7b, 0xe2, 0xc5, 0x14, 0xba, 0x5c, - 0x3e, 0x11, 0xed, 0x13, 0xf1, 0x6d, 0x7d, 0xa6, 0xaf, 0xa7, 0x76, 0x9a, - 0xd1, 0xbf, 0x00, 0x24, 0x8f, 0x8a, 0xee, 0x87, 0x38, 0x64, 0x08, 0x4f, - 0xa5, 0x5a, 0x52, 0x4b, 0xdf, 0x78, 0xd8, 0x82, 0x55, 0x41, 0x03, 0x1f, - 0x33, 0x31, 0xc5, 0xb5, 0xe1, 0xf3, 0x79, 0xca, 0xe4, 0x05, 0x61, 0x54, - 0xb1, 0xf0, 0x0c, 0xd1, 0x7b, 0x68, 0x04, 0xa1, 0x1e, 0xa4, 0x21, 0x06, - 0xe2, 0x04, 0x8f, 0xbc, 0x0d, 0xad, 0x25, 0x17, 0x24, 0xef, 0x21, 0xc1, - 0x3a, 0xfc, 0x47, 0x78, 0x6b, 0xb5, 0x97, 0xaa, 0x58, 0x7b, 0x12, 0x3a, - 0x73, 0xf6, 0xb9, 0xf4, 0xca, 0x1f, 0x6c, 0xac, 0xa4, 0xfb, 0xbd, 0x39, - 0x19, 0x85, 0x54, 0x22, 0x10, 0x05, 0xa9, 0x7f, 0x39, 0x8e, 0xf9, 0x46, - 0x41, 0x6a, 0x17, 0x92, 0xef, 0x24, 0x9f, 0x6b, 0x80, 0x06, 0xfd, 0x51, - 0x51, 0x7d, 0xd9, 0x0f, 0xe7, 0x27, 0x1c, 0xb0, 0x68, 0x69, 0xc5, 0x2f, - 0x31, 0xf4, 0xcc, 0x85, 0xe4, 0x81, 0xbb, 0x41, 0x4e, 0xce, 0x97, 0xa6, - 0xd9, 0x45, 0x9b, 0x40, 0x55, 0x82, 0x4a, 0xf3, 0x68, 0x8d, 0x68, 0xaa, - 0xca, 0x4a, 0xe6, 0x15, 0x3d, 0xad, 0x8c, 0x14, 0x01, 0x00, 0x00, 0x40, - 0x9c, 0xa7, 0x0b, 0x66, 0x72, 0x05, 0x35, 0xfa, 0x7d, 0x11, 0xa6, 0xc2, - 0xa4, 0x8c, 0xed, 0x9e, 0x1e, 0x8f, 0xac, 0x47, 0x75, 0x6c, 0x06, 0x46, - 0x1b, 0x5c, 0xab, 0x2c, 0xb7, 0x6d, 0xdb, 0xfe, 0x65, 0x48, 0xd9, 0x84, - 0x53, 0x1a, 0x44, 0x2d, 0x05, 0x6c, 0x71, 0xa7, 0x06, 0xf4, 0xf6, 0x76, - 0x68, 0xe0, 0x4e, 0xd6, 0xb9, 0x1d, 0xa8, 0x34, 0x08, 0xe1, 0x74, 0x03, - 0xcd, 0xcc, 0xcc, 0xec, 0x46, 0xa9, 0xa6, 0xa2, 0x57, 0x64, 0xc3, 0x98, - 0xf8, 0x1e, 0x3a, 0xe3, 0x83, 0xc7, 0x99, 0x66, 0x29, 0xe7, 0x3d, 0xc3, - 0x38, 0xf3, 0x5b, 0x73, 0x31, 0xc6, 0x73, 0x06, 0x01, 0x00, 0x00, 0xb1, - 0x3a, 0xd6, 0xa3, 0x2f, 0x88, 0xd9, 0x1d, 0xf2, 0xc3, 0xa9, 0xb0, 0x55, - 0xd7, 0x42, 0x69, 0x19, 0x5b, 0x41, 0xcb, 0x1c, 0x27, 0x86, 0x1e, 0xb3, - 0x8b, 0x09, 0x5e, 0x2d, 0x97, 0x96, 0x96, 0x36, 0x34, 0xde, 0x6d, 0x90, - 0x7e, 0xeb, 0xb9, 0xee, 0x73, 0xca, 0x8a, 0x68, 0xa8, 0x97, 0x1e, 0x97, - 0x6d, 0xeb, 0xb2, 0xe6, 0x3b, 0xed, 0x7a, 0x4f, 0xec, 0x57, 0x14, 0x11, - 0x01, 0x00, 0x00, 0xb8, 0x0b, 0x2f, 0xc7, 0x6a, 0x17, 0x5c, 0xbd, 0xe4, - 0x60, 0x5b, 0x86, 0x89, 0x3b, 0x45, 0xc1, 0x96, 0xba, 0x7a, 0xf6, 0x66, - 0x27, 0x50, 0xcb, 0x9b, 0x33, 0x11, 0xb4, 0x2d, 0x36, 0x94, 0xd7, 0xa0, - 0x58, 0x35, 0x6a, 0x54, 0xa9, 0x8c, 0x75, 0xeb, 0xcd, 0xe3, 0xb1, 0xd9, - 0x1f, 0x12, 0x80, 0xa8, 0xee, 0x7c, 0x7d, 0x75, 0x7b, 0xed, 0x60, 0xfe, - 0x20, 0x04, 0x8c, 0x02, 0x9a, 0x99, 0x99, 0x2d, 0x5a, 0x7c, 0xf5, 0x0a, - 0x66, 0xe7, 0x00, 0x91, 0x4c, 0x91, 0x78, 0x34, 0xba, 0xab, 0x53, 0xad, - 0xcc, 0x7e, 0x82, 0x00, 0x75, 0x5e, 0xd1, 0xce, 0x41, 0xe8, 0xef, 0x10, - 0x25, 0x49, 0x92, 0xa4, 0xca, 0x2c, 0xdc, 0x19, 0x68, 0x8c, 0xc0, 0x46, - 0xc6, 0xea, 0x5c, 0xd2, 0x78, 0x15, 0x25, 0x25, 0x2d, 0x02, 0xfa, 0x76, - 0x34, 0x49, 0x2b, 0x6e, 0xd6, 0x5a, 0x6f, 0x12, 0x18, 0x5d, 0x74, 0x69, - 0x13, 0xb3, 0xa5, 0x22, 0xc0, 0xcc, 0x72, 0x24, 0x1a, 0x07, 0xaf, 0xb2, - 0xf9, 0xa4, 0xa9, 0xcc, 0x70, 0xd2, 0x93, 0xa0, 0xd6, 0x78, 0x2d, 0x25, - 0x94, 0x92, 0xfe, 0x20, 0x86, 0x2c, 0x64, 0x31, 0xa9, 0xe5, 0x2b, 0xf9, - 0x43, 0xe9, 0x10, 0xaa, 0x8b, 0x92, 0x2a, 0x8a, 0xd6, 0x38, 0x11, 0x54, - 0x95, 0x8a, 0x2a, 0xa1, 0x7f, 0xda, 0x8d, 0xd2, 0x21, 0x99, 0xef, 0x12 - }; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/bw6_761_poseidon.h b/icicle/include/poseidon/constants/bw6_761_poseidon.h deleted file mode 100644 index 0380c077e..000000000 --- a/icicle/include/poseidon/constants/bw6_761_poseidon.h +++ /dev/null @@ -1,16195 +0,0 @@ -#pragma once -#ifndef BW6_761_POSEIDON_H -#define BW6_761_POSEIDON_H - -namespace poseidon_constants_bw6_761 { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 56; - - int partial_rounds_4 = 56; - - int partial_rounds_8 = 57; - - int partial_rounds_11 = 57; - -unsigned char poseidon_constants_2[] = { - 0x1c, 0xb0, 0xec, 0x1e, 0x25, 0xea, 0x89, 0x62, 0x80, 0xf8, 0x4d, 0x6e, - 0xe7, 0x6f, 0x21, 0x9c, 0x4b, 0xeb, 0xaa, 0x6b, 0x6a, 0x2a, 0x99, 0xd2, - 0x3f, 0x21, 0x16, 0xd9, 0x21, 0xcd, 0x32, 0x4b, 0xd1, 0x0b, 0xff, 0xee, - 0x92, 0xa8, 0x9b, 0x91, 0x7b, 0xdd, 0x20, 0xec, 0x90, 0xcc, 0x97, 0x01, - 0x2f, 0x9e, 0xf6, 0x33, 0x29, 0xef, 0x19, 0xfd, 0x8d, 0x84, 0xd4, 0xbe, - 0x7b, 0x69, 0x98, 0x23, 0x90, 0xf2, 0xf2, 0x55, 0x03, 0x65, 0x5d, 0x6b, - 0x07, 0xc9, 0xcb, 0x18, 0xb1, 0x4a, 0x9f, 0x90, 0x62, 0x68, 0xb2, 0xcf, - 0xd2, 0x8e, 0xee, 0x64, 0xb1, 0x02, 0xd9, 0x2f, 0x62, 0x6e, 0x4f, 0x01, - 0xb7, 0x1f, 0x5c, 0xf9, 0x66, 0xf8, 0xa9, 0xc4, 0x70, 0x30, 0x7b, 0xa4, - 0xae, 0xaf, 0x8d, 0x2d, 0x8c, 0x94, 0xda, 0x36, 0xf2, 0x00, 0x04, 0x62, - 0x1c, 0x03, 0x3a, 0x3a, 0xc3, 0xf6, 0x37, 0xdc, 0xc1, 0x44, 0xb1, 0x2a, - 0x32, 0xac, 0x18, 0x73, 0x6c, 0xec, 0xd4, 0xf7, 0xd1, 0xc3, 0x6b, 0x00, - 0x79, 0xe8, 0x5c, 0xca, 0xbc, 0x89, 0xfd, 0x23, 0x50, 0x2f, 0x6b, 0x2c, - 0x53, 0xbc, 0xf4, 0x15, 0x60, 0xf5, 0x97, 0x6c, 0xee, 0xd5, 0x11, 0x65, - 0xe0, 0x2e, 0x5c, 0x2e, 0xe7, 0x2f, 0x58, 0x25, 0x72, 0x1f, 0x53, 0x46, - 0x3b, 0x76, 0xdf, 0x2c, 0x3a, 0x13, 0x63, 0x59, 0xa7, 0xc7, 0x2e, 0x01, - 0x90, 0x69, 0xa2, 0xa1, 0x63, 0xd4, 0x4c, 0xe3, 0x13, 0x9c, 0x9d, 0xf7, - 0xfd, 0x82, 0x8d, 0x3a, 0x36, 0x9b, 0xd6, 0x93, 0xaf, 0x89, 0x19, 0x68, - 0xff, 0x99, 0x9a, 0x58, 0x24, 0xeb, 0x28, 0x64, 0x0c, 0x0c, 0x1a, 0xa2, - 0x37, 0x12, 0xea, 0x53, 0x13, 0x3a, 0x4e, 0x73, 0xd7, 0xee, 0xc1, 0x00, - 0x67, 0x19, 0xac, 0xeb, 0x99, 0xcf, 0xf9, 0xa4, 0xd8, 0xca, 0xe6, 0x81, - 0x4a, 0x3a, 0x34, 0xbb, 0x52, 0x86, 0x89, 0xe8, 0x64, 0x58, 0xed, 0x84, - 0x27, 0x1c, 0xa1, 0x42, 0x2e, 0x88, 0xd4, 0x9c, 0x4a, 0x5b, 0x92, 0xb5, - 0x11, 0xd0, 0x7c, 0xff, 0xcf, 0x10, 0xa5, 0x80, 0x22, 0x61, 0xab, 0x00, - 0x27, 0xa3, 0xe8, 0x07, 0x5f, 0x4c, 0xce, 0x96, 0xf3, 0x9a, 0x5a, 0x0d, - 0x23, 0x29, 0x84, 0x0b, 0xe6, 0x6e, 0x26, 0xeb, 0xca, 0xa5, 0xf5, 0xf5, - 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-unsigned char poseidon_constants_11[] = { - 0xf1, 0x45, 0x4f, 0x4d, 0xae, 0x2e, 0x72, 0x8d, 0x98, 0x31, 0xf4, 0xeb, - 0xe7, 0xdd, 0x61, 0x79, 0x1a, 0xbe, 0x4f, 0x78, 0xcd, 0xc2, 0x0f, 0x45, - 0x66, 0xc3, 0xd3, 0xb2, 0xe7, 0xb5, 0xb3, 0xaa, 0x46, 0x3a, 0x21, 0xba, - 0xbe, 0x22, 0x15, 0xaa, 0x85, 0x8d, 0x30, 0x20, 0xe5, 0xac, 0x25, 0x00, - 0xc9, 0x67, 0xb5, 0x13, 0x56, 0x00, 0x9b, 0x27, 0x25, 0x49, 0x65, 0x05, - 0x99, 0x26, 0x94, 0x9b, 0x54, 0x37, 0xe4, 0xe6, 0x0e, 0x58, 0x75, 0xb7, - 0x77, 0xa9, 0x73, 0x52, 0xb9, 0x46, 0x28, 0x6e, 0x23, 0xef, 0x20, 0xbb, - 0x28, 0x4a, 0x1f, 0x9d, 0x98, 0x36, 0x93, 0x57, 0x19, 0x69, 0x04, 0x00, - 0x6b, 0xaa, 0x22, 0x63, 0x1e, 0x98, 0x87, 0x52, 0x23, 0xf6, 0x15, 0xb9, - 0x7e, 0x11, 0x59, 0xc8, 0xa0, 0x5c, 0xa5, 0x7f, 0xd7, 0xa9, 0x72, 0x88, - 0x33, 0xf8, 0x74, 0xc9, 0x46, 0x8b, 0xfe, 0xf2, 0x5a, 0x31, 0x76, 0xdc, - 0x9f, 0x9c, 0x39, 0x40, 0x18, 0x59, 0x5c, 0x0a, 0x9b, 0x81, 0x99, 0x01, - 0x8f, 0x59, 0x44, 0x5a, 0x2b, 0x3e, 0x58, 0xe8, 0x43, 0xdd, 0xed, 0xfb, - 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0x00, 0x67, 0x44, 0x5b, 0x10, 0x4d, 0xa3, 0x16, 0x98, 0xbd, 0x99, 0x01, - 0xe9, 0xa2, 0x8b, 0x2e, 0xba, 0x88, 0xb0, 0xef, 0x45, 0x17, 0x5d, 0xdc, - 0x03, 0xdb, 0x99, 0x79, 0xd1, 0x61, 0x19, 0xf0, 0x67, 0xf3, 0xc2, 0x6f, - 0x43, 0xca, 0x94, 0xf4, 0x9f, 0x8e, 0x93, 0x11, 0x19, 0x62, 0x0d, 0x8d, - 0xf1, 0x0c, 0x99, 0x5b, 0x35, 0xf8, 0x66, 0x05, 0x6d, 0xc7, 0x61, 0x00, - 0x5a, 0xc8, 0x42, 0x16, 0xb2, 0xd0, 0xa3, 0xdd, 0x21, 0x0b, 0x59, 0x58, - 0xf0, 0x97, 0xc7, 0xfe, 0x4d, 0xc7, 0x77, 0x88, 0xd2, 0xbd, 0x0a, 0x5f, - 0x13, 0xcf, 0x38, 0xc9, 0x2b, 0x2b, 0xe8, 0x70, 0x09, 0xa7, 0xb4, 0x8f, - 0xa4, 0x26, 0x4c, 0x24, 0x19, 0x47, 0x7c, 0x58, 0x08, 0xd1, 0x88, 0x01 -}; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/constants_template.h b/icicle/include/poseidon/constants/constants_template.h deleted file mode 100644 index bbd612fc2..000000000 --- a/icicle/include/poseidon/constants/constants_template.h +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once -#ifndef CURVE_POSEIDON_H -#define CURVE_POSEIDON_H - -namespace poseidon_constants_curve { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 0; - - int partial_rounds_4 = 0; - - int partial_rounds_8 = 0; - - int partial_rounds_11 = 0; - - unsigned char poseidon_constants_2[] = { - 0x00 - }; - - unsigned char poseidon_constants_4[] = { - 0x00 - }; - - unsigned char poseidon_constants_8[] = { - 0x00 - }; - - unsigned char poseidon_constants_11[] = { - 0x00 - }; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/constants/generate_parameters.py b/icicle/include/poseidon/constants/generate_parameters.py deleted file mode 100755 index 1e9e75bd4..000000000 --- a/icicle/include/poseidon/constants/generate_parameters.py +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python3 - -# 0.3.7 -import galois -import numpy as np - -# pip install poseidon-hash -from poseidon import round_constants as rc, round_numbers as rn - -# Modify these -arity = 2 -p = 2 ** 31 - 1 # grumpkin -# p = 0x30644e72e131a029b85045b68181585d97816a916871ca8d3c208c16d87cfd47 # grumpkin -# p = 0x73EDA753299D7D483339D80809A1D80553BDA402FFFE5BFEFFFFFFFF00000001 # bls12-381 -# p = 0x12ab655e9a2ca55660b44d1e5c37b00159aa76fed00000010a11800000000001 # bls12-377 -# p = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001 # bn254 -# p = 0x1ae3a4617c510eac63b05c06ca1493b1a22d9f300f5138f1ef3622fba094800170b5d44300000008508c00000000001 # bw6-761 -prime_bit_len = 31 -field_bytes = 4 - -# leave set to -1 if not sure -full_round = 8 -half_full_round = full_round // 2 -# leave set to -1 if not sure -partial_round = -1 - -security_level = 128 - -# May speed up Galois significantly. If not sure - set it to None -# You can get primitive element fast by using sage -# p = ... -# F = GF(p) -# F.primitive_element() -# -primitive_element = None -# primitive_element = 7 # bls12-381 -# primitive_element = 22 # bls12-377 -# primitive_element = 5 # bn254 -# primitive_element = 15 # bw6-761 -# primitive_element = 3 # grumpkin - -# currently we only support alpha 5, if you need alpha other than 5 - feal free to reach out -alpha = 5 -t = arity + 1 - -def flatten(xss): - return [x for xs in xss for x in xs] - -if __name__ == "__main__": - if full_round == -1 or partial_round == -1: - full_round, partial_round, half_full_round = rn.calc_round_numbers(prime_bit_len, security_level, t, alpha, True) - print("Half full rounds:", half_full_round) - print("Partial rounds:", partial_round) - - print("Loading galois... This might take from several minutes to an hour") - field_p = galois.GF(p, 1, verify=False, primitive_element=primitive_element) - print("Galois loaded") - mds_matrix = rc.mds_matrix_generator(field_p, t) - non_opt_rc = rc.calc_round_constants(t, full_round, partial_round, p, field_p, alpha, prime_bit_len) - split_rc = [field_p(x.tolist()) for x in np.array_split(non_opt_rc, len(non_opt_rc) / t)] - opt_rc = rc.optimized_rc(split_rc, half_full_round, partial_round, mds_matrix) - pre_matrix, sparse_matrices = rc.optimized_matrix(mds_matrix, partial_round, field_p) - - sparse_aligned = [] - for m in sparse_matrices: - m = flatten(m.tolist()) - for j in range(0, t * t, t): - sparse_aligned.append(m[j]) - for j in range(1, t): - sparse_aligned.append(m[j]) - - with open("constants.bin", "wb") as constants_file: - for l in [opt_rc, flatten(mds_matrix), flatten(pre_matrix), sparse_aligned]: - for c in l: - constants_file.write(int(c).to_bytes(field_bytes, byteorder='little')) \ No newline at end of file diff --git a/icicle/include/poseidon/constants/grumpkin_poseidon.h b/icicle/include/poseidon/constants/grumpkin_poseidon.h deleted file mode 100644 index 32c55ccb8..000000000 --- a/icicle/include/poseidon/constants/grumpkin_poseidon.h +++ /dev/null @@ -1,10806 +0,0 @@ -#pragma once -#ifndef GRUMPKIN_POSEIDON_H -#define GRUMPKIN_POSEIDON_H - -namespace poseidon_constants_grumpkin { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 56; - - int partial_rounds_4 = 56; - - int partial_rounds_8 = 57; - - int partial_rounds_11 = 57; - - unsigned char poseidon_constants_2[] = { - 0x31, 0x2a, 0xfd, 0xbb, 0x33, 0xfa, 0x28, 0xd8, 0xb9, 0x3b, 0x37, 0x56, - 0xca, 0x07, 0x07, 0x22, 0xb4, 0xc2, 0x89, 0x50, 0x50, 0xc1, 0x51, 0x59, - 0x0d, 0xe6, 0xec, 0xb8, 0x46, 0x23, 0x51, 0x21, 0xc8, 0x88, 0xa5, 0x3d, - 0x75, 0x06, 0x36, 0x19, 0x10, 0x48, 0x3b, 0xfc, 0xd7, 0xe0, 0x91, 0x42, - 0xba, 0xbe, 0xbb, 0x99, 0xe1, 0x48, 0xc5, 0x92, 0x3e, 0xe8, 0x69, 0x2c, - 0x25, 0x40, 0x3a, 0x2b, 0x64, 0xd4, 0xc0, 0xc1, 0xe8, 0x80, 0xbe, 0x12, - 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0x7c, 0x8f, 0xa0, 0x60, 0x61, 0x72, 0x21, 0x62, 0x11, 0x58, 0x7f, 0x88, - 0x5a, 0xcb, 0x29, 0x14, 0xe3, 0x11, 0xf4, 0xd0, 0x0f, 0x9c, 0x51, 0x3d, - 0xc4, 0x29, 0x76, 0xbe, 0x9f, 0xd3, 0x0a, 0xdf, 0x7b, 0x29, 0x32, 0xf7, - 0x56, 0x6b, 0xc1, 0xf5, 0x57, 0xd1, 0x84, 0x74, 0x14, 0x85, 0x80, 0x21, - 0xf3, 0xf3, 0x60, 0xe5, 0x49, 0xbe, 0xba, 0xf6, 0xf0, 0x3e, 0xed, 0xbf, - 0x50, 0xd9, 0x55, 0x78, 0x2a, 0x0e, 0xad, 0x85, 0x1c, 0x05, 0xfd, 0x9d, - 0xdf, 0x83, 0x12, 0x67, 0xdc, 0x67, 0x98, 0x01, 0x3b, 0x70, 0xec, 0xa5, - 0xc0, 0xa9, 0x7b, 0x87, 0x3c, 0x32, 0x0c, 0xbf, 0x19, 0x09, 0x5c, 0x1c, - 0x28, 0xb8, 0xc9, 0xc9, 0x72, 0x42, 0xd9, 0x05, 0x5b, 0xb2, 0xf0, 0x3b, - 0x31, 0x46, 0xbd, 0x14, 0x1e, 0x60, 0xec, 0xa5, 0xc0, 0xa9, 0x7b, 0x87, - 0x3c, 0x32, 0x0c, 0xbf, 0x19, 0x09, 0x5c, 0x1c, 0x28, 0xb8, 0xc9, 0xc9, - 0x72, 0x42, 0xd9, 0x05, 0x5b, 0xb2, 0xf0, 0x3b, 0x31, 0x46, 0xbd, 0x14, - 0x6a, 0x75, 0x1c, 0x43, 0xe0, 0xca, 0xb8, 0x7d, 0xbc, 0x0d, 0x61, 0x2e, - 0xfb, 0x89, 0xb5, 0xef, 0x18, 0x52, 0x88, 0xa6, 0xb6, 0xdd, 0xd4, 0xd5, - 0x63, 0x8b, 0x77, 0x84, 0x34, 0x3d, 0x77, 0x1c, 0x06, 0x93, 0x93, 0x9e, - 0x33, 0xde, 0x5a, 0xfc, 0x93, 0xaf, 0x01, 0x17, 0xc5, 0xdc, 0x88, 0x7f, - 0xc1, 0xe9, 0xa6, 0x3d, 0x24, 0xc9, 0x87, 0xbc, 0x9f, 0x78, 0xf2, 0xd3, - 0x20, 0x95, 0x8b, 0x2d, 0xb4, 0xe8, 0xe1, 0x72, 0x7d, 0x59, 0x98, 0x4f, - 0x54, 0x35, 0x3d, 0xcb, 0x86, 0x4b, 0xbc, 0x75, 0x3b, 0xc9, 0x5a, 0x8e, - 0x3b, 0xed, 0x3b, 0x47, 0xf3, 0xca, 0x23, 0x05, 0xf7, 0xcd, 0x94, 0x0f, - 0xf3, 0x9c, 0x1b, 0xf8, 0x81, 0xe9, 0x33, 0xb9, 0x74, 0x04, 0xcd, 0x1e, - 0xaa, 0xa9, 0x68, 0x08, 0x4d, 0xc8, 0xed, 0x65, 0xbe, 0x25, 0xd0, 0xa9, - 0xa0, 0xff, 0xa6, 0xf9, 0xb0, 0x7d, 0xcc, 0x15, 0xfb, 0x27, 0x57, 0xf8, - 0xaf, 0x33, 0x8e, 0x0f, 0x78, 0xd2, 0x69, 0x65, 0xa5, 0xe3, 0xc5, 0xcf, - 0xef, 0x2a, 0xbf, 0x20, 0xb9, 0x82, 0xe7, 0x7b, 0xe4, 0x44, 0x8e, 0x3b, - 0x0b, 0x18, 0x1a, 0x1c, 0x5b, 0xbe, 0xbc, 0x5e, 0xf9, 0xd4, 0xa5, 0xe4, - 0x27, 0x77, 0x93, 0x3f, 0xd1, 0x4e, 0x6c, 0xb0, 0x72, 0x0f, 0xbb, 0xea, - 0x1f, 0x31, 0xe9, 0x33, 0x14, 0x95, 0x9b, 0x05, 0x64, 0x3f, 0x1f, 0x23, - 0xb0, 0xb4, 0x0c, 0x45, 0x74, 0x66, 0x3f, 0xb9, 0x86, 0xd7, 0x8b, 0x86, - 0x31, 0x86, 0x43, 0xb7, 0x5d, 0x90, 0x1d, 0x5e, 0x88, 0x59, 0xfe, 0x7b, - 0x6a, 0x47, 0x98, 0x8c, 0xba, 0xbf, 0xea, 0x21, 0x69, 0xc2, 0x9a, 0x16, - 0x15, 0xd0, 0x6c, 0xfc, 0x5a, 0xe2, 0xf2, 0xfd, 0xd4, 0xc4, 0x63, 0x29, - 0xa5, 0x8c, 0xed, 0x9e, 0x1e, 0x8f, 0xac, 0x47, 0x75, 0x6c, 0x06, 0x46, - 0x1b, 0x5c, 0xab, 0x2c, 0x01, 0xc7, 0x86, 0x85, 0x36, 0xb7, 0x62, 0xf8, - 0x6e, 0xb1, 0xeb, 0x88, 0x04, 0x66, 0x53, 0x89, 0x49, 0x7c, 0x9c, 0x1c, - 0x7d, 0xa4, 0x63, 0x35, 0xfc, 0x46, 0x39, 0x43, 0x5a, 0xab, 0x05, 0x26, - 0x37, 0xa9, 0x42, 0x40, 0x1d, 0xb1, 0x66, 0x75, 0x6d, 0xd2, 0x5e, 0xe2, - 0xa2, 0x05, 0x45, 0xc8, 0x0f, 0x1e, 0x67, 0x9a, 0xa5, 0x9c, 0xf7, 0x0c, - 0xe3, 0xcc, 0x6f, 0xcd, 0xc5, 0x18, 0xcf, 0x19, 0x78, 0x4e, 0xc6, 0xd9, - 0xcc, 0x4e, 0xd2, 0x71, 0xef, 0x01, 0xc0, 0xca, 0xf1, 0xcb, 0x38, 0xa5, - 0xb4, 0xc1, 0xd8, 0xa8, 0x36, 0x27, 0xad, 0x77, 0x17, 0xea, 0xab, 0x9e, - 0x20, 0x6c, 0x38, 0x1b, 0x4d, 0xf0, 0xa4, 0xe7, 0x8c, 0xb7, 0x16, 0xff, - 0xe4, 0x6b, 0xc0, 0x45, 0x31, 0x37, 0xe2, 0x5f, 0x70, 0xba, 0x69, 0x0f, - 0x49, 0xf2, 0x21, 0xef, 0x27, 0x9e, 0xfc, 0x34, 0x48, 0xe5, 0x62, 0x0b, - 0xfc, 0x60, 0xcb, 0x21, 0xf9, 0x2e, 0x3b, 0xd5, 0xa1, 0x94, 0x32, 0xc6, - 0xa5, 0xe4, 0xa4, 0xf2, 0x3b, 0x45, 0xc1, 0x96, 0xba, 0x7a, 0xf6, 0x66, - 0x27, 0x50, 0xcb, 0x9b, 0x33, 0x11, 0xb4, 0x2d, 0x8c, 0x78, 0xf8, 0x38, - 0x9a, 0xab, 0xd2, 0x0f, 0x5b, 0x1a, 0xcd, 0x79, 0xba, 0xbd, 0xc3, 0x5d, - 0x9f, 0x5a, 0x80, 0x4a, 0xa9, 0x70, 0x73, 0x4b, 0x69, 0xa3, 0xe4, 0xf7, - 0xa4, 0x14, 0xbc, 0x0c, 0x3b, 0x3e, 0x51, 0xa6, 0xdb, 0x27, 0x15, 0x67, - 0x75, 0x90, 0xe3, 0x83, 0xde, 0x1e, 0x14, 0x3c, 0xa3, 0xac, 0x2d, 0xd4, - 0xe9, 0xc6, 0xcd, 0xb7, 0xb4, 0x41, 0x60, 0x12, 0x31, 0x66, 0x74, 0x1f, - 0x56, 0x2f, 0xaf, 0x03, 0xcf, 0x24, 0x97, 0xa5, 0xf4, 0x20, 0x9d, 0xb0, - 0xad, 0xee, 0x8c, 0x5b, 0x86, 0xfd, 0x12, 0x13, 0xfe, 0xc2, 0x97, 0x23, - 0xa8, 0x84, 0x7b, 0xd7, 0xe6, 0x1c, 0x59, 0x19, 0x6c, 0xae, 0x05, 0xc4, - 0xe9, 0x34, 0xea, 0x3c, 0x4c, 0xe6, 0xed, 0x3e, 0x92, 0x6d, 0xb4, 0xcc, - 0x32, 0x3e, 0xfa, 0x62, 0xe5, 0xeb, 0x1a, 0x71, 0x30, 0x2a, 0x25, 0xad, - 0xd6, 0x1a, 0x33, 0x02, 0x49, 0x2c, 0x7a, 0x4a, 0x71, 0x22, 0x72, 0xc2, - 0xdf, 0x27, 0x20, 0x62, 0xf6, 0x99, 0xd3, 0x44, 0x13, 0x29, 0x59, 0xbd, - 0x04, 0xad, 0x97, 0x95, 0xaa, 0xf7, 0x3b, 0x4b, 0x79, 0x3e, 0x35, 0x04 - }; -} // namespace poseidon_constants -#endif diff --git a/icicle/include/poseidon/constants/m31_poseidon.h b/icicle/include/poseidon/constants/m31_poseidon.h deleted file mode 100644 index 07bedfa54..000000000 --- a/icicle/include/poseidon/constants/m31_poseidon.h +++ /dev/null @@ -1,508 +0,0 @@ -#pragma once -#ifndef M31_POSEIDON_H -#define M31_POSEIDON_H - -namespace poseidon_constants_m31 { - /** - * This inner namespace contains optimized constants for running Poseidon. - * These constants were generated using an algorithm defined at - * https://spec.filecoin.io/algorithms/crypto/poseidon/ - * The number in the name corresponds to the arity of hash function - * Each array contains: - * RoundConstants | MDSMatrix | Non-sparse matrix | Sparse matrices - */ - - int partial_rounds_2 = 7; - - int partial_rounds_4 = 11; - - int partial_rounds_8 = 12; - - int partial_rounds_11 = 12; - - unsigned char poseidon_constants_2[] = { - 0x33, 0x8b, 0x6d, 0x47, 0xbb, 0x97, 0x11, 0x67, 0x92, 0x9d, 0x55, 0x2d, - 0xee, 0x1e, 0x2e, 0x45, 0xfe, 0x35, 0x0e, 0x25, 0x7e, 0xc3, 0x4f, 0x70, - 0x4d, 0x0a, 0x8c, 0x18, 0xd9, 0x43, 0xa4, 0x61, 0xfb, 0x14, 0xd9, 0x14, - 0x99, 0x13, 0xb9, 0x30, 0xec, 0x3b, 0x8c, 0x16, 0xcc, 0xb2, 0x0b, 0x2e, - 0x9e, 0x18, 0xbf, 0x26, 0xb6, 0xb7, 0x2a, 0x44, 0x61, 0x29, 0xdb, 0x21, - 0x18, 0x84, 0x03, 0x4e, 0xef, 0x95, 0xf9, 0x45, 0xe3, 0xd8, 0xf2, 0x46, - 0x82, 0xb4, 0xc9, 0x5e, 0x5f, 0xf3, 0xb2, 0x4f, 0x61, 0x80, 0x50, 0x0f, - 0x0d, 0x7f, 0xe3, 0x1b, 0x23, 0xbd, 0x05, 0x2f, 0x0f, 0xb1, 0x60, 0x67, - 0xd8, 0x85, 0xdf, 0x57, 0x0c, 0x8c, 0xdf, 0x50, 0x9e, 0x65, 0x3c, 0x58, - 0x07, 0xbd, 0x29, 0x7e, 0xc5, 0xe5, 0xa7, 0x5a, 0x5a, 0x4b, 0x0c, 0x29, - 0x89, 0x9d, 0x14, 0x11, 0x8c, 0x20, 0xcb, 0x76, 0x4d, 0x56, 0x2d, 0x4a, - 0x10, 0xda, 0xaf, 0x0a, 0x65, 0x9d, 0x98, 0x3e, 0xa1, 0xac, 0x57, 0x46, - 0xcb, 0xe8, 0xfc, 0x5b, 0xd4, 0x43, 0x4b, 0x63, 0x1b, 0x13, 0x4b, 0x1f, - 0xed, 0xac, 0xbf, 0x30, 0x27, 0x15, 0xac, 0x53, 0x4b, 0x27, 0x61, 0x3e, - 0x37, 0xc3, 0x65, 0x74, 0x55, 0x55, 0x55, 0x55, 0x00, 0x00, 0x00, 0x20, - 0x33, 0x33, 0x33, 0x33, 0x00, 0x00, 0x00, 0x20, 0x33, 0x33, 0x33, 0x33, - 0xaa, 0xaa, 0xaa, 0x6a, 0x33, 0x33, 0x33, 0x33, 0xaa, 0xaa, 0xaa, 0x6a, - 0x6d, 0xdb, 0xb6, 0x6d, 0x55, 0x55, 0x55, 0x55, 0xc0, 0x72, 0x8d, 0x36, - 0x2c, 0xe5, 0xc0, 0x51, 0x00, 0x00, 0x00, 0x20, 0x0b, 0xd5, 0x67, 0x6c, - 0x6c, 0x67, 0x2c, 0x13, 0x33, 0x33, 0x33, 0x33, 0x6c, 0x67, 0x2c, 0x13, - 0xe6, 0xb8, 0x2c, 0x62, 0x55, 0x55, 0x55, 0x55, 0x15, 0x1f, 0xaf, 0x6a, - 0xd9, 0xa8, 0x14, 0x44, 0xae, 0xb0, 0x38, 0x4b, 0x17, 0x76, 0xd9, 0x39, - 0x55, 0x55, 0x55, 0x55, 0x28, 0xef, 0x9d, 0x4f, 0xc7, 0x3b, 0xa6, 0x24, - 0x84, 0x5b, 0x79, 0x6f, 0xde, 0x4f, 0x8f, 0x3d, 0x55, 0x55, 0x55, 0x55, - 0x54, 0xc2, 0xb2, 0x00, 0x5a, 0xed, 0x68, 0x0c, 0xeb, 0xd4, 0xc4, 0x61, - 0x02, 0x8c, 0x85, 0x27, 0x55, 0x55, 0x55, 0x55, 0xe4, 0xc5, 0xbd, 0x0a, - 0xf6, 0xec, 0x75, 0x26, 0xe0, 0xdb, 0xd8, 0x52, 0xdf, 0x28, 0xff, 0x33, - 0x55, 0x55, 0x55, 0x55, 0xac, 0x68, 0x06, 0x00, 0xc9, 0xff, 0x91, 0x19, - 0xb1, 0x12, 0x2b, 0x19, 0xa2, 0xdd, 0x47, 0x39, 0x55, 0x55, 0x55, 0x55, - 0xd5, 0x03, 0x00, 0x00, 0x45, 0xc8, 0xcc, 0x4c, 0x55, 0x55, 0x55, 0x35, - 0x8d, 0xd6, 0x68, 0x3d, 0x55, 0x55, 0x55, 0x55, 0x03, 0x00, 0x00, 0x00, - 0x64, 0x66, 0x66, 0x26, 0x00, 0x00, 0x00, 0x20, 0x33, 0x33, 0x33, 0x33 -}; - - unsigned char poseidon_constants_4[] = { - 0xdb, 0x64, 0xa5, 0x32, 0xd6, 0x3d, 0x12, 0x6e, 0x65, 0x66, 0x46, 0x59, - 0x2a, 0x64, 0x51, 0x3b, 0xaf, 0xbe, 0x72, 0x0b, 0x66, 0x5f, 0x5c, 0x6c, - 0x66, 0x11, 0x8c, 0x61, 0x99, 0x24, 0x99, 0x14, 0x1d, 0x5f, 0x67, 0x0a, - 0x4d, 0xab, 0xc4, 0x1e, 0x43, 0xb2, 0x09, 0x58, 0xc0, 0x27, 0x4c, 0x5b, - 0xf0, 0x0c, 0xf5, 0x12, 0xc9, 0x2f, 0x88, 0x4f, 0x59, 0x52, 0x5b, 0x6a, - 0x73, 0x90, 0x55, 0x5b, 0xaf, 0x47, 0x55, 0x0d, 0xa7, 0xc2, 0x0c, 0x6e, - 0xe6, 0xd6, 0x4e, 0x30, 0x9e, 0x75, 0x47, 0x12, 0xca, 0x93, 0xd1, 0x5b, - 0x64, 0x27, 0xfc, 0x60, 0x6c, 0x16, 0x52, 0x20, 0xf5, 0xe0, 0x01, 0x15, - 0x27, 0xf9, 0x96, 0x7f, 0xa0, 0x38, 0xad, 0x3c, 0x95, 0xd3, 0xe4, 0x32, - 0x57, 0x95, 0x5a, 0x6b, 0x12, 0xcc, 0xdc, 0x18, 0x2b, 0xdd, 0xa4, 0x66, - 0xbf, 0xe7, 0x96, 0x15, 0x85, 0x87, 0x6a, 0x1f, 0x15, 0x19, 0x9c, 0x65, - 0xef, 0x24, 0xaa, 0x2c, 0x3f, 0x6b, 0xbc, 0x6b, 0x54, 0x24, 0x2c, 0x17, - 0xf1, 0x7a, 0x8d, 0x57, 0x90, 0xa4, 0xd4, 0x4a, 0x12, 0x06, 0x77, 0x6a, - 0xe8, 0x6b, 0xd9, 0x51, 0x80, 0x72, 0xa1, 0x31, 0xce, 0xa8, 0x59, 0x10, - 0x0c, 0x90, 0xd4, 0x10, 0x8e, 0x60, 0x54, 0x1c, 0xe7, 0xfd, 0x42, 0x3a, - 0x73, 0xc1, 0xcc, 0x4f, 0x58, 0xbb, 0x99, 0x7c, 0xd2, 0x51, 0xda, 0x43, - 0xea, 0x6e, 0xe8, 0x16, 0xb2, 0x51, 0x53, 0x61, 0x7e, 0x68, 0x44, 0x3c, - 0x33, 0x33, 0x33, 0x33, 0xaa, 0xaa, 0xaa, 0x6a, 0x6d, 0xdb, 0xb6, 0x6d, - 0x00, 0x00, 0x00, 0x10, 0x71, 0x1c, 0xc7, 0x71, 0xaa, 0xaa, 0xaa, 0x6a, - 0x6d, 0xdb, 0xb6, 0x6d, 0x00, 0x00, 0x00, 0x10, 0x71, 0x1c, 0xc7, 0x71, - 0x99, 0x99, 0x99, 0x59, 0x6d, 0xdb, 0xb6, 0x6d, 0x00, 0x00, 0x00, 0x10, - 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0x4a, 0x15, 0x5b, 0x5d, 0xe1, 0xaa, 0x15, 0x1b, 0x62, 0xf5, 0xe8, 0x76, - 0x03, 0xc1, 0xaa, 0x06, 0x13, 0x59, 0xc8, 0x40, 0x84, 0x49, 0xc8, 0x1f, - 0x85, 0x98, 0x55, 0x6b, 0xed, 0x38, 0x45, 0x17, 0xb8, 0xc7, 0xf7, 0x69, - 0xc3, 0x87, 0xd0, 0x17, 0x0a, 0x93, 0xb7, 0x35, 0xc2, 0x45, 0x75, 0x34, - 0x7a, 0x78, 0xff, 0x51, 0x26, 0xd2, 0x59, 0x13, 0x55, 0x55, 0x55, 0x35, - 0x48, 0x38, 0xf7, 0x6e, 0x4f, 0x7d, 0xc7, 0x70, 0x32, 0x5d, 0x5b, 0x7a, - 0x85, 0x35, 0x9c, 0x07, 0x40, 0x08, 0x30, 0x5c, 0x64, 0x69, 0x27, 0x7a, - 0x07, 0x34, 0x90, 0x6c, 0x6e, 0xa6, 0x8e, 0x70, 0xd4, 0xf2, 0xf7, 0x59, - 0x0f, 0x13, 0x17, 0x5d, 0xa8, 0xa9, 0x01, 0x29, 0xad, 0xfd, 0x9a, 0x77, - 0x3c, 0x77, 0xc7, 0x67, 0xd0, 0x43, 0xb1, 0x3f, 0x97, 0x76, 0xe4, 0x72, - 0xd4, 0x82, 0x9a, 0x25, 0xec, 0xef, 0xc3, 0x03, 0xdc, 0xf9, 0x94, 0x3f, - 0xa4, 0x76, 0x88, 0x5a, 0xb8, 0x0f, 0x03, 0x76, 0x58, 0x87, 0x42, 0x11, - 0x28, 0xb7, 0xb0, 0x1d, 0x55, 0x55, 0x55, 0x35, 0x2f, 0xe6, 0x44, 0x75, - 0xf3, 0x0b, 0xe8, 0x68, 0x59, 0x72, 0x1f, 0x16, 0x8c, 0xd0, 0xe3, 0x3c, - 0xcc, 0xfc, 0x77, 0x05, 0xd6, 0x4b, 0x48, 0x78, 0x51, 0x88, 0x4c, 0x5f, - 0x30, 0x43, 0x9c, 0x2f, 0x49, 0x72, 0xba, 0x01, 0xba, 0xae, 0xfe, 0x0b, - 0x94, 0x3f, 0xe7, 0x71, 0x9d, 0xfa, 0x37, 0x06, 0xfc, 0xa2, 0x99, 0x6f, - 0xe2, 0x0d, 0xcf, 0x4b, 0x63, 0x76, 0xec, 0x49, 0xa8, 0xb5, 0x84, 0x0b, - 0x84, 0xa3, 0x75, 0x4f, 0x5e, 0x56, 0xdd, 0x37, 0x1a, 0x7d, 0x6e, 0x34, - 0x95, 0x39, 0x80, 0x1e, 0x58, 0x2e, 0x22, 0x50, 0xd3, 0x46, 0x93, 0x1e, - 0x55, 0x55, 0x55, 0x35, 0xf5, 0x96, 0x5a, 0x5f, 0x9b, 0xc8, 0x58, 0x50, - 0x3e, 0x03, 0xab, 0x16, 0xd5, 0xc6, 0x4c, 0x7f, 0x3f, 0x82, 0xf6, 0x34, - 0x1c, 0x29, 0x22, 0x16, 0x40, 0xdb, 0xe7, 0x71, 0x8b, 0x8a, 0x4b, 0x55, - 0x45, 0xbf, 0xd1, 0x68, 0x4c, 0xbb, 0xe3, 0x43, 0x1b, 0x96, 0x28, 0x3d, - 0x36, 0x4f, 0xdb, 0x58, 0xa8, 0x39, 0xac, 0x38, 0xd3, 0xeb, 0x90, 0x18, - 0x2f, 0xb7, 0x06, 0x1a, 0x5a, 0x82, 0x53, 0x13, 0x77, 0xaf, 0xe0, 0x4d, - 0x9e, 0xe9, 0x39, 0x79, 0xb7, 0xf6, 0xa2, 0x3c, 0x41, 0x9d, 0x14, 0x59, - 0x01, 0x33, 0x36, 0x20, 0x15, 0xe0, 0xe4, 0x15, 0x55, 0x55, 0x55, 0x35, - 0x58, 0x48, 0x07, 0x36, 0x3f, 0x43, 0x1e, 0x05, 0x33, 0x9e, 0x14, 0x45, - 0x69, 0xc8, 0x16, 0x63, 0x5f, 0xab, 0x77, 0x26, 0xf4, 0x08, 0xb0, 0x2e, - 0xf8, 0x31, 0x79, 0x29, 0x37, 0xc9, 0x37, 0x28, 0x55, 0x62, 0xcc, 0x43, - 0xeb, 0x6b, 0xe4, 0x03, 0xfe, 0x82, 0x50, 0x20, 0x2d, 0xdf, 0xf2, 0x7d, - 0xba, 0x07, 0xe2, 0x0e, 0x88, 0x1e, 0x82, 0x2b, 0x87, 0x54, 0x26, 0x39, - 0xdd, 0xee, 0x3e, 0x0b, 0xdc, 0xbf, 0x93, 0x1a, 0x8a, 0xce, 0xa6, 0x39, - 0x5b, 0xaf, 0x8f, 0x00, 0x7a, 0xad, 0x27, 0x71, 0x1e, 0x76, 0xd8, 0x58, - 0x96, 0x36, 0xa3, 0x14, 0x55, 0x55, 0x55, 0x35, 0x76, 0x27, 0x76, 0x62, - 0xa4, 0x9f, 0x05, 0x5a, 0x41, 0x28, 0x49, 0x12, 0x24, 0x18, 0x49, 0x12, - 0x4f, 0xc2, 0xa5, 0x25, 0x0e, 0x0e, 0x3c, 0x3c, 0x01, 0xa7, 0x65, 0x00, - 0x92, 0x9e, 0x17, 0x36, 0xa1, 0x7a, 0x92, 0x27, 0xcf, 0x74, 0xba, 0x4d, - 0xcb, 0x6f, 0x66, 0x68, 0xd8, 0x89, 0x9d, 0x58, 0xb6, 0x6d, 0xdb, 0x76, - 0x11, 0x11, 0x11, 0x11, 0x00, 0x00, 0x00, 0x08, 0x0f, 0x0f, 0x0f, 0x0f, - 0x38, 0x8e, 0xe3, 0x78, 0x28, 0xaf, 0xa1, 0x3c, 0xcc, 0xcc, 0xcc, 0x6c, - 0x79, 0x9e, 0xe7, 0x79, 0xa2, 0x8b, 0x2e, 0x7a, 0xc8, 0x42, 0x16, 0x32 - }; -} // namespace poseidon_constants -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/kernels.cuh b/icicle/include/poseidon/kernels.cuh deleted file mode 100644 index 4afa9878b..000000000 --- a/icicle/include/poseidon/kernels.cuh +++ /dev/null @@ -1,216 +0,0 @@ -#pragma once -#ifndef POSEIDON_KERNELS_H -#define POSEIDON_KERNELS_H - -#include "gpu-utils/modifiers.cuh" -#include "poseidon/constants.cuh" - -namespace poseidon { - template - __global__ void prepare_poseidon_states(const S* input, S* states, unsigned int number_of_states, const S domain_tag) - { - int idx = blockIdx.x * blockDim.x + threadIdx.x; - int state_number = idx / T; - if (state_number >= number_of_states) { return; } - int element_number = idx % T; - - S prepared_element; - - // Domain separation - if (element_number == 0) { - prepared_element = domain_tag; - } else { - prepared_element = input[idx - state_number - 1]; - } - - // Store element in state - states[idx] = prepared_element; - } - - template - DEVICE_INLINE S sbox_el(S element, const int alpha) - { - S result2 = S::sqr(element); - switch (alpha) { - case 3: - return result2 * element; - case 5: - return S::sqr(result2) * element; - case 7: - return S::sqr(result2) * result2 * element; - case 11: - return S::sqr(S::sqr(result2)) * result2 * element; - } - } - - template - __device__ S vecs_mul_matrix(S element, S* matrix, int element_number, int vec_number, S* shared_states) - { - __syncthreads(); - shared_states[threadIdx.x] = element; - __syncthreads(); - - typename S::Wide element_wide = S::mul_wide(shared_states[vec_number * T], matrix[element_number]); - UNROLL - for (int i = 1; i < T; i++) { - element_wide = element_wide + S::mul_wide(shared_states[vec_number * T + i], matrix[i * T + element_number]); - } - - return S::reduce(element_wide); - } - - template - __device__ S full_round( - S element, - size_t rc_offset, - int local_state_number, - int element_number, - bool multiply_by_mds, - bool add_pre_round_constants, - bool skip_rc, - S* shared_states, - const PoseidonConstants& constants) - { - if (add_pre_round_constants) { - element = element + constants.round_constants[rc_offset + element_number]; - rc_offset += T; - } - element = sbox_el(element, constants.alpha); - if (!skip_rc) { element = element + constants.round_constants[rc_offset + element_number]; } - - // Multiply all the states by mds matrix - S* matrix = multiply_by_mds ? constants.mds_matrix : constants.non_sparse_matrix; - return vecs_mul_matrix(element, matrix, element_number, local_state_number, shared_states); - } - - template - __global__ void full_rounds( - S* states, size_t number_of_states, size_t rc_offset, bool first_half, const PoseidonConstants constants) - { - extern __shared__ S shared_states[]; - - int idx = (blockIdx.x * blockDim.x) + threadIdx.x; - int state_number = idx / T; - if (state_number >= number_of_states) { return; } - int local_state_number = threadIdx.x / T; - int element_number = idx % T; - - S new_el = states[idx]; - bool add_pre_round_constants = first_half; - for (int i = 0; i < constants.full_rounds_half; i++) { - new_el = full_round( - new_el, rc_offset, local_state_number, element_number, !first_half || (i < (constants.full_rounds_half - 1)), - add_pre_round_constants, !first_half && (i == constants.full_rounds_half - 1), shared_states, constants); - rc_offset += T; - - if (add_pre_round_constants) { - rc_offset += T; - add_pre_round_constants = false; - } - } - states[idx] = new_el; - } - - template - __device__ S partial_round(S state[T], size_t rc_offset, int round_number, const PoseidonConstants& constants) - { - S element = state[0]; - element = sbox_el(element, constants.alpha); - element = element + constants.round_constants[rc_offset]; - - S* sparse_matrix = &constants.sparse_matrices[(T * 2 - 1) * round_number]; - - typename S::Wide state_0_wide = S::mul_wide(element, sparse_matrix[0]); - - UNROLL - for (int i = 1; i < T; i++) { - state_0_wide = state_0_wide + S::mul_wide(state[i], sparse_matrix[i]); - } - - state[0] = S::reduce(state_0_wide); - - UNROLL - for (int i = 1; i < T; i++) { - state[i] = state[i] + (element * sparse_matrix[T + i - 1]); - } - } - - template - __global__ void - partial_rounds(S* states, size_t number_of_states, size_t rc_offset, const PoseidonConstants constants) - { - int idx = (blockIdx.x * blockDim.x) + threadIdx.x; - if (idx >= number_of_states) { return; } - - S state[T]; - UNROLL - for (int i = 0; i < T; i++) { - state[i] = states[idx * T + i]; - } - - for (int i = 0; i < constants.partial_rounds; i++) { - partial_round(state, rc_offset, i, constants); - rc_offset++; - } - - UNROLL - for (int i = 0; i < T; i++) { - states[idx * T + i] = state[i]; - } - } - - template - __global__ void - squeeze_states_kernel(const S* states, unsigned int number_of_states, unsigned int rate, unsigned int offset, S* out) - { - int idx = (blockIdx.x * blockDim.x) + threadIdx.x; - if (idx >= number_of_states) { return; } - - for (int i = 0; i < rate; i++) { - out[idx * rate + i] = states[idx * T + offset + i]; - } - } - - template - cudaError_t poseidon_permutation_kernel( - const S* input, - S* out, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const PoseidonConstants& constants, - cudaStream_t& stream) - { - S* states; - CHK_IF_RETURN(cudaMallocAsync(&states, number_of_states * T * sizeof(S), stream)); - - prepare_poseidon_states - <<>>( - input, states, number_of_states, constants.domain_tag); - - size_t rc_offset = 0; - full_rounds<<< - PKC::number_of_full_blocks(T, number_of_states), PKC::number_of_threads(T), - sizeof(S) * PKC::hashes_per_block(T) * T, stream>>>( - states, number_of_states, rc_offset, FIRST_FULL_ROUNDS, constants); - rc_offset += T * (constants.full_rounds_half + 1); - - partial_rounds<<>>( - states, number_of_states, rc_offset, constants); - rc_offset += constants.partial_rounds; - - full_rounds<<< - PKC::number_of_full_blocks(T, number_of_states), PKC::number_of_threads(T), - sizeof(S) * PKC::hashes_per_block(T) * T, stream>>>( - states, number_of_states, rc_offset, SECOND_FULL_ROUNDS, constants); - - squeeze_states_kernel - <<>>( - states, number_of_states, output_len, 1, out); - - CHK_IF_RETURN(cudaFreeAsync(states, stream)); - return CHK_LAST(); - } -} // namespace poseidon - -#endif \ No newline at end of file diff --git a/icicle/include/poseidon/poseidon.cuh b/icicle/include/poseidon/poseidon.cuh deleted file mode 100644 index 24727c21e..000000000 --- a/icicle/include/poseidon/poseidon.cuh +++ /dev/null @@ -1,94 +0,0 @@ -#pragma once -#ifndef POSEIDON_H -#define POSEIDON_H - -#include -#include -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "utils/utils.h" - -#include "poseidon/kernels.cuh" -#include "poseidon/constants.cuh" -#include "hash/hash.cuh" -using namespace hash; - -/** - * @namespace poseidon - * Implementation of the [Poseidon hash function](https://eprint.iacr.org/2019/458.pdf) - * Specifically, the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/) - */ -namespace poseidon { - template - class Poseidon : public Hasher - { - public: - const std::size_t device_id; - PoseidonConstants constants; - - cudaError_t run_hash_many_kernel( - const S* input, - S* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const device_context::DeviceContext& ctx) const override - { - cudaError_t permutation_error; -#define P_PERM_T(width) \ - case width: \ - permutation_error = poseidon_permutation_kernel( \ - input, output, number_of_states, input_len, output_len, this->constants, ctx.stream); \ - break; - - switch (this->width) { - P_PERM_T(3) - P_PERM_T(5) - P_PERM_T(9) - P_PERM_T(12) - default: - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "PoseidonPermutation: #width must be one of [3, 5, 9, 12]"); - } - - CHK_IF_RETURN(permutation_error); - return CHK_LAST(); - } - - Poseidon( - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const S* round_constants, - const S* mds_matrix, - const S* non_sparse_matrix, - const S* sparse_matrices, - const S domain_tag, - device_context::DeviceContext& ctx) - : Hasher(arity + 1, arity, arity, 1), device_id(ctx.device_id) - { - PoseidonConstants constants; - CHK_STICKY(create_optimized_poseidon_constants( - arity, alpha, partial_rounds, full_rounds_half, round_constants, mds_matrix, non_sparse_matrix, sparse_matrices, - domain_tag, &constants, ctx)); - this->constants = constants; - } - - Poseidon(int arity, device_context::DeviceContext& ctx) - : Hasher(arity + 1, arity, arity, 1), device_id(ctx.device_id) - { - PoseidonConstants constants{}; - CHK_STICKY(init_optimized_poseidon_constants(arity, ctx, &constants)); - this->constants = constants; - } - - ~Poseidon() - { - auto ctx = device_context::get_default_device_context(); - ctx.device_id = this->device_id; - CHK_STICKY(release_optimized_poseidon_constants(&this->constants, ctx)); - } - }; -} // namespace poseidon - -#endif \ No newline at end of file diff --git a/icicle/include/poseidon2/constants.cuh b/icicle/include/poseidon2/constants.cuh deleted file mode 100644 index 35bc2ebf3..000000000 --- a/icicle/include/poseidon2/constants.cuh +++ /dev/null @@ -1,65 +0,0 @@ -#pragma once -#ifndef POSEIDON2_CONSTANTS_H -#define POSEIDON2_CONSTANTS_H - -#include "gpu-utils/device_context.cuh" - -namespace poseidon2 { - /** - * For most of the Poseidon2 configurations this is the case - */ - const int EXTERNAL_ROUNDS_DEFAULT = 8; - - enum DiffusionStrategy { - DEFAULT_DIFFUSION, - MONTGOMERY, - }; - - enum MdsType { DEFAULT_MDS, PLONKY }; - - /** - * @struct Poseidon2Constants - * This constants are enough to define a Poseidon2 instantce - * @param round_constants A pointer to round constants allocated on the device - * @param mds_matrix A pointer to an mds matrix allocated on the device - * @param non_sparse_matrix A pointer to non sparse matrix allocated on the device - * @param sparse_matrices A pointer to sparse matrices allocated on the device - */ - template - struct Poseidon2Constants { - int width; - int alpha; - int internal_rounds; - int external_rounds; - S* round_constants = nullptr; - S* internal_matrix_diag = nullptr; - MdsType mds_type; - DiffusionStrategy diffusion; - }; - - template - cudaError_t create_poseidon2_constants( - int width, - int alpha, - int internal_rounds, - int external_rounds, - const S* round_constants, - const S* internal_matrix_diag, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx, - Poseidon2Constants* poseidon_constants); - - template - cudaError_t init_poseidon2_constants( - int width, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx, - Poseidon2Constants* poseidon2_constants); - - template - cudaError_t release_poseidon2_constants(Poseidon2Constants* constants, device_context::DeviceContext& ctx); -} // namespace poseidon2 - -#endif \ No newline at end of file diff --git a/icicle/include/poseidon2/constants/babybear_poseidon2.h b/icicle/include/poseidon2/constants/babybear_poseidon2.h deleted file mode 100644 index b537ff1d5..000000000 --- a/icicle/include/poseidon2/constants/babybear_poseidon2.h +++ /dev/null @@ -1,1068 +0,0 @@ -#pragma once -#ifndef BABYBEAR_POSEIDON2_H -#define BABYBEAR_POSEIDON2_H - -namespace poseidon2_constants_babybear { - - namespace t2 { - int internal_rounds = 24; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x01, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, - }; - - unsigned char round_constants[] = { - 0x2a, 0x93, 0xa8, 0x76, - 0x43, 0x25, 0xb3, 0x0b, - 0x80, 0x1a, 0xa1, 0x54, - 0x89, 0x93, 0xf0, 0x29, - 0xc1, 0x8e, 0x8a, 0x4b, - 0xa2, 0xf7, 0xa2, 0x3f, - 0x0c, 0xfd, 0xe3, 0x2b, - 0x3d, 0x0c, 0xb7, 0x6a, - 0xf1, 0xe8, 0xd8, 0x0c, - 0xf0, 0xc8, 0x34, 0x3a, - 0x77, 0xbf, 0x11, 0x3c, - 0xf3, 0x37, 0xeb, 0x27, - 0xcd, 0xaf, 0xdc, 0x06, - 0x2a, 0x4b, 0xfd, 0x19, - 0xc3, 0x65, 0xcd, 0x1c, - 0xc2, 0xa0, 0x33, 0x37, - 0x54, 0x37, 0xb4, 0x18, - 0x76, 0x40, 0xd7, 0x2e, - 0xa6, 0x93, 0x8f, 0x36, - 0xcd, 0x24, 0x80, 0x0e, - 0x9a, 0x28, 0x8f, 0x3b, - 0x7b, 0xfa, 0x5a, 0x4f, - 0x68, 0x2b, 0xc8, 0x64, - 0x80, 0x56, 0x24, 0x76, - 0x2f, 0xac, 0xb4, 0x1a, - 0x8e, 0xf6, 0xa5, 0x3c, - 0xb1, 0x49, 0xbd, 0x14, - 0x15, 0x3b, 0xd0, 0x52, - 0xb3, 0x74, 0xcd, 0x08, - 0x2a, 0x15, 0xdb, 0x1e, - 0x7a, 0x49, 0x37, 0x30, - 0xec, 0x68, 0x8a, 0x49, - 0x28, 0xf2, 0x11, 0x6b, - 0x94, 0x41, 0x21, 0x21, - 0xba, 0xe0, 0x0b, 0x64, - 0x41, 0x82, 0xd1, 0x4b, - 0x34, 0x46, 0x09, 0x20, - 0xd8, 0x22, 0x6e, 0x12, - 0xd1, 0x13, 0xd2, 0x42, - 0x27, 0xb6, 0x7d, 0x66, - 0xa8, 0x5e, 0x34, 0x29, - 0xfa, 0xed, 0x79, 0x4e, - 0x20, 0xbc, 0x2a, 0x02, - 0xca, 0xe6, 0xa8, 0x36, - 0x45, 0x1d, 0xf1, 0x05, - 0x22, 0x46, 0x1d, 0x31, - 0x98, 0x75, 0x6d, 0x34, - 0xf3, 0x8e, 0xb1, 0x61, - }; - } - - - namespace t3 { - int internal_rounds = 17; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x01, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, - }; - - unsigned char round_constants[] = { - 0x97, 0x6c, 0x0c, 0x46, - 0x45, 0x13, 0x3f, 0x76, - 0x69, 0xb9, 0x4f, 0x35, - 0x4d, 0x24, 0x50, 0x06, - 0xad, 0x7b, 0x2c, 0x74, - 0xe6, 0xe4, 0x0a, 0x42, - 0x2f, 0x92, 0xb1, 0x24, - 0x87, 0x84, 0xb3, 0x54, - 0x25, 0xa5, 0xd8, 0x67, - 0xd8, 0x21, 0x37, 0x0e, - 0x61, 0xf2, 0xb8, 0x4b, - 0x12, 0x0d, 0x83, 0x67, - 0x6c, 0x9a, 0x12, 0x70, - 0xe0, 0x69, 0xba, 0x11, - 0x41, 0xf8, 0x3b, 0x46, - 0x2f, 0xa6, 0xd4, 0x32, - 0x81, 0x89, 0x27, 0x67, - 0x1f, 0x5c, 0x95, 0x13, - 0x20, 0x90, 0x60, 0x26, - 0x76, 0x77, 0xf5, 0x38, - 0x0d, 0x14, 0x84, 0x46, - 0x72, 0xfa, 0x37, 0x09, - 0xd0, 0x9e, 0x9c, 0x03, - 0x4e, 0x4f, 0x8c, 0x0a, - 0x4d, 0x32, 0x53, 0x43, - 0x28, 0xac, 0x70, 0x00, - 0x9b, 0xe9, 0xb3, 0x1b, - 0xd0, 0x72, 0xbe, 0x4e, - 0xd3, 0xc2, 0x5d, 0x26, - 0xf7, 0x49, 0x77, 0x01, - 0x86, 0x5d, 0x26, 0x69, - 0x42, 0x98, 0x8c, 0x0e, - 0x82, 0xd7, 0x0c, 0x4c, - 0x61, 0x43, 0x37, 0x64, - 0x74, 0x51, 0x8e, 0x15, - 0x75, 0x65, 0x5c, 0x0b, - 0xc2, 0x66, 0x7a, 0x08, - 0x98, 0xa4, 0xaf, 0x67, - 0x22, 0x09, 0x7f, 0x1b, - 0xdd, 0x8c, 0xb4, 0x25, - 0xfc, 0x9a, 0x91, 0x01, - 0x57, 0xff, 0x3a, 0x52, - 0xf8, 0x0c, 0x00, 0x3c, - 0x9a, 0x7c, 0x00, 0x21, - 0xa1, 0x14, 0xf6, 0x3d, - 0x29, 0xa3, 0x9c, 0x6f, - 0xcf, 0x10, 0xfe, 0x17, - 0xa1, 0xc1, 0x58, 0x18, - 0xfe, 0xc6, 0x79, 0x67, - 0x2c, 0xa1, 0x4c, 0x0a, - 0x4d, 0x0c, 0x18, 0x36, - 0x59, 0x7e, 0xd1, 0x54, - 0xd8, 0xab, 0xbc, 0x23, - }; - } - - - namespace t4 { - int internal_rounds = 21; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x47, 0x6f, 0xbf, 0x35, - 0x9f, 0x66, 0x82, 0x69, - 0xb2, 0x52, 0xf8, 0x41, - 0xb8, 0xda, 0x5c, 0x37, - }; - - unsigned char round_constants[] = { - 0x6a, 0x9b, 0x14, 0x1b, - 0x3b, 0xda, 0x0a, 0x55, - 0x72, 0x0f, 0x88, 0x22, - 0xde, 0x76, 0x29, 0x16, - 0x5b, 0x03, 0x59, 0x14, - 0x51, 0xb8, 0x23, 0x5b, - 0x50, 0x6c, 0xcb, 0x0e, - 0x6e, 0x90, 0x25, 0x5e, - 0x64, 0x79, 0x62, 0x2e, - 0x43, 0xaf, 0xed, 0x13, - 0x1a, 0xa9, 0x39, 0x0f, - 0xb2, 0x36, 0x10, 0x4d, - 0x71, 0x1a, 0x15, 0x07, - 0x2c, 0xd3, 0x88, 0x06, - 0x08, 0x23, 0x0c, 0x64, - 0xc0, 0x3a, 0x82, 0x4a, - 0x1a, 0x06, 0x4a, 0x66, - 0x76, 0x92, 0x5f, 0x73, - 0x59, 0xaa, 0x33, 0x1d, - 0xad, 0x66, 0x42, 0x44, - 0xc1, 0x8d, 0xa9, 0x65, - 0x1c, 0x7e, 0x0f, 0x21, - 0xeb, 0x29, 0xc4, 0x05, - 0xd5, 0xf1, 0xc9, 0x12, - 0x08, 0x69, 0xe1, 0x56, - 0x0f, 0x00, 0xc4, 0x2c, - 0x5d, 0x17, 0xc1, 0x4c, - 0x0c, 0x83, 0x96, 0x46, - 0x5e, 0xab, 0x76, 0x72, - 0x80, 0xe5, 0x29, 0x04, - 0x35, 0xdf, 0xad, 0x1e, - 0x4b, 0x81, 0x04, 0x3d, - 0x0c, 0xd1, 0xe9, 0x0c, - 0xb2, 0xd2, 0x1b, 0x0c, - 0x62, 0xfe, 0x0a, 0x5c, - 0x5c, 0xd0, 0xd3, 0x41, - 0xa8, 0xfb, 0xa8, 0x4e, - 0x73, 0x59, 0x41, 0x36, - 0x2b, 0x2a, 0x6c, 0x1f, - 0xc9, 0x47, 0xc4, 0x0f, - 0x31, 0xec, 0x06, 0x0e, - 0x3b, 0x0d, 0x9e, 0x75, - 0x9d, 0x8b, 0x92, 0x53, - 0x41, 0x1a, 0x03, 0x1f, - 0xdc, 0x57, 0x43, 0x08, - 0x08, 0x4a, 0xa4, 0x18, - 0x22, 0x99, 0x97, 0x4e, - 0x7e, 0x77, 0x1b, 0x59, - 0x28, 0xaa, 0x98, 0x16, - 0xe2, 0xed, 0x89, 0x6c, - 0xeb, 0x1e, 0x94, 0x49, - 0x66, 0x14, 0xd7, 0x23, - 0x20, 0xef, 0xa1, 0x4c, - }; - } - - - namespace t8 { - int internal_rounds = 12; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x1b, 0x69, 0xa3, 0x17, - 0x79, 0x98, 0x84, 0x00, - 0xe3, 0x62, 0x7b, 0x1f, - 0x10, 0x7b, 0x96, 0x05, - 0x5a, 0x86, 0x7c, 0x42, - 0xae, 0x41, 0x33, 0x3f, - 0x5b, 0x0f, 0x49, 0x52, - 0xc5, 0x46, 0x88, 0x46, - }; - - unsigned char round_constants[] = { - 0xde, 0x8c, 0x97, 0x11, - 0xcd, 0xaa, 0x1b, 0x53, - 0x61, 0x3e, 0xfc, 0x62, - 0xd7, 0x43, 0x54, 0x6c, - 0xea, 0x7b, 0x3f, 0x21, - 0x50, 0xaf, 0x1d, 0x44, - 0xca, 0xb0, 0x73, 0x5b, - 0x8a, 0x45, 0xac, 0x37, - 0x2a, 0x45, 0x8d, 0x57, - 0x69, 0xd8, 0x05, 0x11, - 0x21, 0x25, 0x97, 0x5f, - 0x88, 0xc6, 0x8a, 0x63, - 0x6c, 0x20, 0xbb, 0x22, - 0x4b, 0xb7, 0x2b, 0x3d, - 0x9e, 0x65, 0xb3, 0x08, - 0x35, 0x40, 0x99, 0x5c, - 0xb5, 0x23, 0xf8, 0x0b, - 0x71, 0x6f, 0x12, 0x0f, - 0x22, 0x0e, 0x3f, 0x4b, - 0x79, 0xc9, 0x74, 0x32, - 0xde, 0x2f, 0x7e, 0x2a, - 0x43, 0xfc, 0x5e, 0x4c, - 0x78, 0x0a, 0xc1, 0x51, - 0x9b, 0x54, 0x63, 0x1b, - 0x23, 0x48, 0x6b, 0x3f, - 0xa4, 0x9d, 0x3d, 0x4e, - 0x1c, 0xe9, 0x9f, 0x66, - 0x31, 0x72, 0xf2, 0x51, - 0x6c, 0x6c, 0xeb, 0x47, - 0xdb, 0x68, 0xbe, 0x2c, - 0x0d, 0xb2, 0x09, 0x3a, - 0x02, 0x3c, 0xcd, 0x19, - 0x5e, 0xb9, 0x69, 0x3d, - 0x02, 0x8f, 0xe0, 0x47, - 0x24, 0x1f, 0xd5, 0x73, - 0x99, 0x9a, 0x68, 0x18, - 0x9d, 0x8b, 0x7d, 0x2d, - 0x71, 0xc9, 0x19, 0x2a, - 0x15, 0xbd, 0xfe, 0x0a, - 0xff, 0xa7, 0xb8, 0x69, - 0x3b, 0x0d, 0xb2, 0x1b, - 0x59, 0x27, 0xfa, 0x24, - 0x1d, 0xf7, 0x34, 0x73, - 0xb1, 0x30, 0xe2, 0x09, - 0x0d, 0xf5, 0x94, 0x07, - 0x12, 0x22, 0x4d, 0x07, - 0x20, 0x8f, 0x6c, 0x1a, - 0xf5, 0xe8, 0xab, 0x1b, - 0x2e, 0x5c, 0xd1, 0x16, - 0xfc, 0xb5, 0xfb, 0x49, - 0x83, 0xf3, 0xcd, 0x1e, - 0xbc, 0xc7, 0x3d, 0x06, - 0x85, 0x4a, 0x5d, 0x54, - 0x4c, 0x50, 0x49, 0x6a, - 0x0c, 0x67, 0x43, 0x38, - 0x69, 0x05, 0x6e, 0x58, - 0xdd, 0x01, 0xa8, 0x2a, - 0x42, 0xaf, 0xb4, 0x38, - 0xae, 0xcc, 0x37, 0x59, - 0xa6, 0x72, 0x7d, 0x2d, - 0xd1, 0x5b, 0x1a, 0x51, - 0x54, 0xf5, 0xc9, 0x0d, - 0x6e, 0x28, 0x3e, 0x2c, - 0xbe, 0x45, 0x2b, 0x30, - 0xcc, 0x5f, 0xe0, 0x6e, - 0x46, 0x86, 0x63, 0x27, - 0xa2, 0xc4, 0x6f, 0x2b, - 0x13, 0xb9, 0x10, 0x4c, - 0xd2, 0x71, 0x04, 0x42, - 0xd2, 0x08, 0xb9, 0x29, - 0xd5, 0x7f, 0xf9, 0x33, - 0xc5, 0x4f, 0x83, 0x06, - 0xed, 0xe4, 0xbe, 0x70, - 0x41, 0x75, 0xe5, 0x4f, - 0x99, 0x9e, 0x0e, 0x62, - 0xe6, 0xad, 0x55, 0x63, - }; - } - - - namespace t12 { - int internal_rounds = 10; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x7f, 0xd1, 0xe6, 0x5c, - 0x07, 0x01, 0x2b, 0x39, - 0x4c, 0x12, 0xbd, 0x0c, - 0xb6, 0x99, 0xf0, 0x06, - 0xb4, 0xeb, 0xb3, 0x6c, - 0xb1, 0x69, 0x30, 0x36, - 0x37, 0x7c, 0xe4, 0x4d, - 0x0b, 0x5a, 0xaf, 0x06, - 0x5e, 0x04, 0xa0, 0x52, - 0x0d, 0x38, 0x9f, 0x3e, - 0x1e, 0xc1, 0xf8, 0x0d, - 0x85, 0x29, 0xa3, 0x63, - }; - - unsigned char round_constants[] = { - 0x88, 0xbb, 0xd3, 0x44, - 0xc9, 0x48, 0x41, 0x77, - 0x5e, 0x42, 0x62, 0x15, - 0x8b, 0x62, 0x70, 0x50, - 0x90, 0x94, 0x79, 0x68, - 0x2e, 0x43, 0x6f, 0x04, - 0x58, 0x43, 0x2e, 0x0f, - 0x49, 0x90, 0xc9, 0x53, - 0x51, 0x60, 0x1c, 0x75, - 0x5a, 0xbc, 0x9e, 0x5b, - 0xf0, 0x93, 0x88, 0x05, - 0x7d, 0xdd, 0xd5, 0x0d, - 0xac, 0x4e, 0x2b, 0x4c, - 0xe0, 0x8b, 0x52, 0x5a, - 0xc3, 0xbc, 0x5f, 0x67, - 0x60, 0x9d, 0x0c, 0x74, - 0xc2, 0xde, 0xc6, 0x4b, - 0xe4, 0xa8, 0xc5, 0x23, - 0x8c, 0xbd, 0x89, 0x31, - 0x5c, 0x18, 0xce, 0x11, - 0x90, 0xbb, 0xb9, 0x68, - 0x8f, 0x9d, 0x0e, 0x67, - 0xbc, 0xad, 0x3e, 0x70, - 0xf0, 0xfa, 0x02, 0x71, - 0xaa, 0x38, 0x68, 0x57, - 0xc6, 0x76, 0x6c, 0x1b, - 0xb4, 0x00, 0xf5, 0x51, - 0xb6, 0x39, 0xd6, 0x15, - 0xc9, 0x45, 0x02, 0x5c, - 0xbb, 0xf3, 0x8d, 0x6f, - 0x2b, 0xc3, 0x0c, 0x4d, - 0x8b, 0xc0, 0xb3, 0x47, - 0x3c, 0x2b, 0x29, 0x19, - 0xbe, 0x30, 0x62, 0x09, - 0x43, 0x9b, 0x3c, 0x17, - 0xfa, 0x58, 0x35, 0x25, - 0xae, 0x7b, 0x78, 0x72, - 0xb3, 0x9d, 0xca, 0x47, - 0x4b, 0x4b, 0x23, 0x44, - 0x93, 0xde, 0x47, 0x4f, - 0x57, 0xf5, 0x09, 0x73, - 0xf8, 0x13, 0x71, 0x0e, - 0x02, 0xa8, 0xcf, 0x5c, - 0x30, 0x4e, 0xf8, 0x45, - 0x5f, 0x06, 0x8a, 0x51, - 0x59, 0xf5, 0x6d, 0x2f, - 0x68, 0x5d, 0x81, 0x69, - 0xa1, 0xe8, 0x3f, 0x27, - 0x63, 0x77, 0x31, 0x43, - 0x9e, 0xc8, 0xe4, 0x74, - 0x7e, 0x2b, 0x16, 0x42, - 0xa5, 0xf9, 0xa3, 0x2e, - 0x04, 0x0c, 0xb3, 0x2b, - 0x74, 0x44, 0x74, 0x24, - 0x0b, 0xa4, 0xe8, 0x69, - 0x70, 0x1c, 0x95, 0x50, - 0xbf, 0x91, 0xff, 0x47, - 0xc6, 0x98, 0x7f, 0x5d, - 0xc0, 0xac, 0xce, 0x2b, - 0x3b, 0x2f, 0xa2, 0x32, - 0x64, 0x46, 0x4b, 0x6e, - 0x7c, 0x90, 0xd5, 0x45, - 0x10, 0x3f, 0xa6, 0x2a, - 0x6d, 0xb3, 0xed, 0x48, - 0xf9, 0xa0, 0xe8, 0x4c, - 0xfe, 0x73, 0x06, 0x19, - 0x23, 0x83, 0x87, 0x74, - 0x5b, 0x3e, 0x95, 0x0c, - 0xb8, 0x1b, 0x25, 0x6c, - 0x31, 0x00, 0x1b, 0x6a, - 0x44, 0x31, 0xc8, 0x66, - 0x9a, 0x7c, 0xe5, 0x29, - 0xbc, 0xd4, 0xc4, 0x70, - 0x94, 0x5f, 0xef, 0x4a, - 0x14, 0x70, 0x8a, 0x35, - 0xae, 0x8e, 0x8d, 0x07, - 0x10, 0xe8, 0x6b, 0x41, - 0x86, 0xcb, 0xda, 0x77, - 0xc3, 0x8c, 0x95, 0x36, - 0xbd, 0x5d, 0x12, 0x3d, - 0xf2, 0x3b, 0xe4, 0x6c, - 0xe0, 0xb7, 0xe0, 0x16, - 0x32, 0x32, 0xde, 0x50, - 0xa8, 0xfe, 0x66, 0x34, - 0x6d, 0xba, 0xab, 0x1d, - 0xb6, 0xe2, 0xef, 0x5c, - 0xe4, 0x3d, 0xcb, 0x0f, - 0x6a, 0xa8, 0xd0, 0x16, - 0x47, 0xda, 0xb9, 0x2b, - 0x29, 0x3a, 0xab, 0x1a, - 0x0b, 0xda, 0x0a, 0x2e, - 0xb6, 0xe3, 0x3e, 0x3c, - 0xe3, 0x8b, 0x67, 0x3a, - 0x90, 0xe5, 0xfe, 0x19, - 0x04, 0xff, 0x84, 0x00, - 0x25, 0x6b, 0xae, 0x35, - 0x7d, 0xaf, 0x1e, 0x76, - 0xa7, 0xfc, 0xd3, 0x0e, - 0xc4, 0x59, 0x6b, 0x15, - 0xf5, 0x73, 0xe2, 0x55, - 0xf5, 0x07, 0x19, 0x2f, - 0x1d, 0xdd, 0x30, 0x34, - 0xc6, 0x00, 0x16, 0x10, - 0x47, 0x89, 0xb3, 0x66, - 0xe0, 0x50, 0x22, 0x21, - 0x2c, 0xfa, 0xbe, 0x62, - }; - } - - - namespace t16 { - int internal_rounds = 13; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0x94, 0x2d, 0x63, 0x0a, - 0xb7, 0x57, 0xb6, 0x6d, - 0x9e, 0xdc, 0xfb, 0x56, - 0x8a, 0x3d, 0x2b, 0x05, - 0x01, 0x52, 0x74, 0x33, - 0x8c, 0x10, 0x03, 0x5c, - 0x7b, 0xa3, 0xeb, 0x0b, - 0x8b, 0x2e, 0x8c, 0x25, - 0x39, 0x9f, 0x02, 0x12, - 0xce, 0x09, 0x49, 0x69, - 0x24, 0x17, 0x23, 0x6d, - 0x22, 0xb2, 0xc3, 0x21, - 0xa5, 0x04, 0x09, 0x3c, - 0xda, 0xac, 0xd6, 0x01, - 0x83, 0x5c, 0x70, 0x27, - 0x02, 0xc8, 0x31, 0x52, - }; - - unsigned char round_constants[] = { - 0xaf, 0xb6, 0xcb, 0x69, - 0xf9, 0x93, 0xad, 0x46, - 0x4e, 0x0f, 0xa0, 0x60, - 0xcd, 0x97, 0x12, 0x6b, - 0xfe, 0x9a, 0x18, 0x23, - 0xef, 0x7b, 0x2e, 0x73, - 0xde, 0x46, 0xc2, 0x72, - 0x00, 0x19, 0x94, 0x2c, - 0xde, 0xee, 0x57, 0x05, - 0x6f, 0x49, 0x80, 0x15, - 0x7b, 0xa7, 0x3e, 0x3a, - 0x71, 0xf2, 0xf3, 0x54, - 0x29, 0xb0, 0x49, 0x0f, - 0xe1, 0x2f, 0x87, 0x47, - 0x36, 0x2e, 0x1e, 0x22, - 0x2e, 0x20, 0xb7, 0x1a, - 0xa6, 0x79, 0x77, 0x48, - 0xd8, 0xc9, 0x51, 0x38, - 0xc0, 0x17, 0xdc, 0x38, - 0x49, 0x88, 0x9f, 0x20, - 0xe8, 0xce, 0x8d, 0x26, - 0xda, 0x48, 0x0c, 0x35, - 0x2e, 0xd3, 0x9a, 0x5b, - 0x2b, 0x27, 0x23, 0x05, - 0x5b, 0x05, 0x89, 0x3f, - 0xb2, 0x94, 0xe8, 0x01, - 0xde, 0xed, 0xdd, 0x13, - 0x34, 0xf3, 0x2e, 0x1b, - 0xb4, 0xd8, 0x07, 0x75, - 0x4e, 0xb9, 0xee, 0x6c, - 0xa2, 0x6b, 0xeb, 0x52, - 0x05, 0x29, 0x64, 0x50, - 0x3f, 0x3f, 0x45, 0x05, - 0xfc, 0x9e, 0x34, 0x06, - 0x7c, 0x78, 0x22, 0x69, - 0x9c, 0xff, 0xbf, 0x04, - 0x4a, 0x71, 0x8c, 0x76, - 0x1a, 0xf2, 0x9f, 0x3e, - 0x9c, 0x7c, 0x73, 0x15, - 0x07, 0xc8, 0x29, 0x22, - 0x8c, 0xf8, 0x47, 0x0d, - 0xcc, 0x0e, 0x7e, 0x09, - 0xa0, 0xdb, 0xea, 0x27, - 0xe4, 0x29, 0x7d, 0x2d, - 0xa0, 0xaa, 0x02, 0x35, - 0xd7, 0x5f, 0x47, 0x0f, - 0x49, 0xda, 0xfb, 0x29, - 0xfd, 0xff, 0x8a, 0x01, - 0x18, 0xb6, 0x15, 0x03, - 0xd1, 0x97, 0x44, 0x6d, - 0x9e, 0x1d, 0x17, 0x1b, - 0xbd, 0x1a, 0x86, 0x52, - 0x01, 0x05, 0x5d, 0x2e, - 0x6c, 0x64, 0xc8, 0x3e, - 0x0a, 0x25, 0x5f, 0x6e, - 0xe6, 0xe8, 0x8a, 0x14, - 0x4a, 0xfa, 0xf5, 0x17, - 0x84, 0xd2, 0x66, 0x3e, - 0x3b, 0xaa, 0x51, 0x00, - 0x13, 0x79, 0x3f, 0x48, - 0x15, 0x5f, 0xfe, 0x2c, - 0xca, 0x27, 0x34, 0x02, - 0x15, 0x83, 0xc7, 0x2c, - 0x47, 0xea, 0x36, 0x1e, - 0xc0, 0x53, 0x80, 0x5a, - 0x39, 0xe6, 0x3b, 0x69, - 0x7d, 0x86, 0x58, 0x38, - 0x6b, 0x4f, 0x33, 0x19, - 0xd8, 0x0f, 0x8f, 0x12, - 0xcb, 0x1c, 0x2b, 0x4e, - 0xe0, 0x0c, 0x21, 0x61, - 0x39, 0x89, 0x31, 0x3c, - 0x22, 0x2f, 0x5b, 0x0b, - 0xd5, 0x11, 0xdb, 0x2e, - 0xdf, 0xff, 0x3e, 0x21, - 0x06, 0x46, 0xac, 0x0c, - 0x6d, 0xf1, 0x1a, 0x24, - 0x0d, 0xa8, 0x90, 0x72, - 0x29, 0x53, 0x7e, 0x6f, - 0xa8, 0xc8, 0x8e, 0x59, - 0xa0, 0x59, 0xa8, 0x76, - 0x68, 0xe8, 0x59, 0x65, - 0xaf, 0x83, 0x7b, 0x65, - 0x3f, 0x1d, 0x27, 0x13, - 0x63, 0x60, 0x87, 0x1f, - 0x37, 0xae, 0xee, 0x0a, - 0xa6, 0x9c, 0x6e, 0x70, - 0xee, 0x0c, 0x40, 0x46, - 0x26, 0x5c, 0xa0, 0x72, - 0x9e, 0x9c, 0x58, 0x2c, - 0xa7, 0x37, 0xbd, 0x20, - 0x10, 0x3d, 0x2d, 0x6a, - 0x67, 0x37, 0x52, 0x20, - 0xc4, 0xe9, 0x8f, 0x5b, - 0xd6, 0x01, 0xa5, 0x2a, - 0x3e, 0xac, 0x01, 0x1e, - 0x54, 0xbc, 0x48, 0x14, - 0x1c, 0xad, 0xe5, 0x5c, - 0x4d, 0xa1, 0x18, 0x49, - 0x3f, 0xa8, 0x46, 0x2c, - 0x76, 0x68, 0xcf, 0x4f, - 0xc8, 0xd5, 0xd8, 0x61, - 0xf9, 0x4f, 0xdf, 0x6d, - 0xd3, 0xa4, 0xfd, 0x11, - 0x8f, 0x3a, 0x93, 0x02, - 0x81, 0xaf, 0x0e, 0x17, - 0x4f, 0x31, 0x9c, 0x5a, - 0x90, 0x25, 0xa1, 0x49, - 0xa1, 0x52, 0xec, 0x35, - 0x11, 0x16, 0xeb, 0x58, - 0x65, 0x1e, 0x48, 0x5e, - 0xc9, 0x25, 0x71, 0x36, - 0xba, 0x33, 0xba, 0x0e, - 0xed, 0x8d, 0xc2, 0x1f, - 0xad, 0x99, 0x63, 0x06, - 0xea, 0xc0, 0xbe, 0x0c, - 0xf0, 0x1a, 0xfd, 0x75, - 0x4e, 0xbf, 0xf5, 0x50, - 0x41, 0x5f, 0x3d, 0x64, - 0x18, 0xe7, 0x4f, 0x6f, - 0xde, 0xbb, 0x3c, 0x5b, - 0x3e, 0xfb, 0x3a, 0x1e, - 0x27, 0xb0, 0x6f, 0x29, - 0x7b, 0x54, 0xe1, 0x45, - 0xab, 0xb2, 0x8d, 0x4a, - 0x19, 0x6d, 0x98, 0x59, - 0xa3, 0xdf, 0xbc, 0x30, - 0x32, 0x39, 0xb6, 0x1d, - 0x24, 0x28, 0x7c, 0x1d, - 0x81, 0x36, 0xb3, 0x53, - 0x47, 0xb7, 0x73, 0x06, - 0xa3, 0x98, 0x8a, 0x03, - 0x60, 0xce, 0x5b, 0x2c, - 0xcd, 0x79, 0x19, 0x35, - 0x73, 0xfb, 0x08, 0x50, - 0x78, 0xca, 0x7b, 0x54, - 0x81, 0xf4, 0x1a, 0x71, - 0x64, 0xbf, 0x93, 0x3f, - 0x7b, 0x98, 0x4d, 0x64, - 0x87, 0xcd, 0x8b, 0x3c, - 0xb8, 0x58, 0x87, 0x60, - }; - } - - - namespace t20 { - int internal_rounds = 18; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0xa3, 0x1d, 0x53, 0x60, - 0xdb, 0x32, 0x29, 0x33, - 0x57, 0x5e, 0xf0, 0x61, - 0xf8, 0x50, 0x40, 0x61, - 0xdd, 0xc6, 0x23, 0x43, - 0x47, 0x35, 0xb3, 0x10, - 0xd8, 0xe1, 0x5c, 0x5e, - 0x85, 0x40, 0xa2, 0x19, - 0x93, 0x7e, 0x69, 0x3c, - 0xb9, 0xe6, 0x5d, 0x36, - 0x68, 0x50, 0xf9, 0x71, - 0x28, 0x92, 0x0c, 0x35, - 0x25, 0xd1, 0x83, 0x38, - 0x2e, 0x2e, 0x50, 0x51, - 0x74, 0x81, 0x4a, 0x1a, - 0xb2, 0x81, 0xe0, 0x01, - 0x6a, 0x08, 0xc1, 0x54, - 0x2a, 0xbc, 0x53, 0x12, - 0x34, 0x9b, 0x59, 0x50, - 0xc3, 0x97, 0x5b, 0x02, - }; - - unsigned char round_constants[] = { - 0x97, 0x44, 0xcd, 0x30, - 0x92, 0x7d, 0xcf, 0x67, - 0x86, 0xa7, 0x3c, 0x11, - 0xf7, 0x11, 0xf5, 0x28, - 0x11, 0x03, 0x85, 0x4c, - 0xe8, 0x7b, 0xc0, 0x3d, - 0xb9, 0x14, 0x82, 0x0c, - 0x97, 0x0d, 0x09, 0x18, - 0xfb, 0x35, 0x4f, 0x68, - 0x68, 0xba, 0xee, 0x11, - 0xa7, 0x0c, 0x7d, 0x56, - 0xbc, 0xbe, 0x3f, 0x6d, - 0x60, 0x84, 0x02, 0x47, - 0x33, 0xd4, 0xa8, 0x09, - 0x5c, 0x5d, 0xbc, 0x46, - 0xb2, 0x05, 0x08, 0x11, - 0x82, 0xdc, 0xdd, 0x50, - 0xfa, 0xe4, 0x1e, 0x00, - 0x19, 0x47, 0xed, 0x55, - 0xce, 0xbd, 0x08, 0x47, - 0x2b, 0x0b, 0x53, 0x1a, - 0x00, 0x24, 0x37, 0x6d, - 0x42, 0x1d, 0x10, 0x32, - 0xd0, 0xd7, 0xb0, 0x02, - 0x12, 0xf5, 0x64, 0x30, - 0x64, 0x3c, 0x0f, 0x67, - 0x31, 0x5e, 0x73, 0x35, - 0x44, 0x61, 0x2b, 0x5a, - 0xa6, 0x6b, 0x0d, 0x21, - 0x6a, 0xe6, 0x74, 0x12, - 0x09, 0x3e, 0x2e, 0x21, - 0xf0, 0x2f, 0xe6, 0x57, - 0x74, 0x18, 0x63, 0x6b, - 0x1b, 0x7a, 0x57, 0x6b, - 0xaa, 0xfd, 0x17, 0x0d, - 0x1b, 0xe8, 0xeb, 0x24, - 0xfe, 0xc9, 0xf6, 0x34, - 0x55, 0x5a, 0x1d, 0x59, - 0x42, 0xa9, 0x45, 0x5f, - 0x3f, 0x4f, 0x45, 0x5e, - 0x33, 0x56, 0xb7, 0x23, - 0x70, 0x61, 0x07, 0x55, - 0x82, 0x94, 0xaa, 0x3c, - 0x00, 0xbf, 0x2c, 0x6a, - 0x1b, 0x80, 0x24, 0x72, - 0x64, 0x2d, 0xbb, 0x74, - 0x90, 0x09, 0x6d, 0x2e, - 0x21, 0xb9, 0xea, 0x23, - 0xa8, 0x03, 0x36, 0x66, - 0x7f, 0xe9, 0xe8, 0x03, - 0x3d, 0x5d, 0xdb, 0x04, - 0x27, 0xe8, 0xc1, 0x64, - 0xfe, 0x78, 0xc4, 0x37, - 0x05, 0xc8, 0xef, 0x35, - 0xf9, 0xae, 0x5a, 0x55, - 0x0c, 0xad, 0x63, 0x65, - 0xac, 0xae, 0xc9, 0x14, - 0x72, 0xe1, 0xd6, 0x16, - 0xe9, 0xcb, 0x4f, 0x42, - 0x55, 0x08, 0x38, 0x30, - 0xbf, 0xe5, 0xd4, 0x74, - 0x83, 0xba, 0x75, 0x12, - 0x2d, 0x2a, 0xf3, 0x56, - 0x89, 0x7f, 0xf9, 0x3c, - 0x11, 0x57, 0x0c, 0x44, - 0x8a, 0x7e, 0x68, 0x48, - 0xf8, 0x00, 0x66, 0x49, - 0x9f, 0x42, 0x0e, 0x0b, - 0x26, 0x1e, 0x4b, 0x15, - 0xce, 0xb8, 0x9b, 0x37, - 0xe5, 0x83, 0x76, 0x27, - 0x94, 0xe0, 0x03, 0x44, - 0x40, 0x32, 0x03, 0x12, - 0xd7, 0x06, 0xd0, 0x3b, - 0x1b, 0xee, 0xe6, 0x4e, - 0x4c, 0x76, 0xc6, 0x62, - 0x2f, 0x55, 0x30, 0x72, - 0xe7, 0x1d, 0x34, 0x4f, - 0x49, 0x8c, 0x8f, 0x3c, - 0xe2, 0xbe, 0xc6, 0x6f, - 0x4d, 0xf7, 0x3e, 0x0a, - 0x07, 0x01, 0x71, 0x23, - 0x82, 0x0c, 0x65, 0x24, - 0xd4, 0xeb, 0xba, 0x12, - 0xce, 0xf3, 0xc2, 0x46, - 0xd9, 0x6f, 0x81, 0x57, - 0x1c, 0xe5, 0x3f, 0x44, - 0xab, 0x05, 0x78, 0x2a, - 0xf1, 0x8e, 0x50, 0x3f, - 0x6c, 0x83, 0x0b, 0x15, - 0xfa, 0x4f, 0x01, 0x1c, - 0xbe, 0x86, 0x07, 0x1c, - 0x90, 0x51, 0x64, 0x10, - 0x0a, 0x9c, 0x7b, 0x60, - 0x05, 0x15, 0x96, 0x48, - 0xbb, 0xb7, 0x20, 0x6f, - 0x23, 0xea, 0x5a, 0x5b, - 0x5c, 0x35, 0xe5, 0x1f, - 0xaa, 0x98, 0xef, 0x27, - 0x0b, 0x12, 0x3e, 0x04, - 0xcd, 0x5c, 0x7d, 0x64, - 0xb2, 0x81, 0x5c, 0x45, - 0x3f, 0x7d, 0xc2, 0x6a, - 0x26, 0x61, 0x9f, 0x27, - 0x58, 0x57, 0xa1, 0x76, - 0xa6, 0xb0, 0xc1, 0x58, - 0x01, 0x39, 0xf8, 0x4a, - 0x4f, 0x1d, 0x64, 0x45, - 0x33, 0x8c, 0xfe, 0x1d, - 0x88, 0x0c, 0x72, 0x03, - 0x0d, 0xd4, 0x82, 0x34, - 0x60, 0x89, 0x0d, 0x77, - 0x30, 0xb7, 0x08, 0x61, - 0x84, 0xc0, 0x3f, 0x06, - 0x34, 0x2a, 0x6e, 0x09, - 0xa0, 0x14, 0x05, 0x13, - 0xff, 0x3e, 0xbc, 0x42, - 0x48, 0x58, 0x77, 0x43, - 0xe4, 0x60, 0x29, 0x70, - 0x79, 0x9c, 0xf1, 0x6c, - 0xdd, 0x30, 0x30, 0x4c, - 0x32, 0x79, 0xd2, 0x6d, - 0xe9, 0x0d, 0x35, 0x64, - 0x11, 0x83, 0x0e, 0x48, - 0x2c, 0x63, 0x22, 0x6a, - 0xf6, 0xbc, 0x14, 0x28, - 0xe3, 0x97, 0x71, 0x11, - 0x19, 0xd4, 0x44, 0x48, - 0xa6, 0x81, 0xe1, 0x76, - 0xb4, 0xd7, 0x55, 0x61, - 0x26, 0xed, 0xdb, 0x42, - 0x7b, 0xc2, 0xe9, 0x62, - 0x11, 0x4f, 0x5b, 0x10, - 0x58, 0x58, 0xd4, 0x51, - 0x98, 0x02, 0xfa, 0x33, - 0x28, 0x82, 0xa0, 0x26, - 0x1f, 0x58, 0x14, 0x5f, - 0x37, 0x97, 0xd8, 0x3b, - 0x8f, 0x96, 0x25, 0x23, - 0xba, 0x12, 0x85, 0x41, - 0x2f, 0x44, 0x47, 0x2f, - 0xf5, 0x0a, 0x68, 0x0e, - 0xba, 0x4c, 0x42, 0x0c, - 0xae, 0x85, 0x38, 0x6d, - 0xbe, 0x8c, 0x9c, 0x0a, - 0xa9, 0xf2, 0x85, 0x24, - 0x7f, 0x35, 0x99, 0x1f, - 0x31, 0xd5, 0x58, 0x0f, - 0xb1, 0xf6, 0xd6, 0x32, - 0x6d, 0x87, 0x26, 0x2c, - 0x8e, 0xd3, 0xe3, 0x2d, - 0xc9, 0x6c, 0x7b, 0x5a, - 0xf0, 0xc7, 0xae, 0x1c, - 0x75, 0xf5, 0xa1, 0x4a, - 0xcd, 0x0c, 0xaf, 0x77, - 0x5c, 0xbe, 0xd3, 0x19, - 0xd8, 0x19, 0xa6, 0x0a, - 0x78, 0xea, 0xcf, 0x04, - 0x76, 0xab, 0xb3, 0x0d, - 0xa3, 0xfa, 0xdf, 0x2d, - 0x67, 0x67, 0x24, 0x52, - 0x18, 0xf0, 0xd7, 0x50, - 0x9f, 0xeb, 0x8c, 0x31, - 0x06, 0xa5, 0x69, 0x2e, - 0xfa, 0xd1, 0xd9, 0x4b, - 0x9a, 0x57, 0x0b, 0x2a, - 0xc2, 0x38, 0x12, 0x56, - 0x9a, 0xbb, 0x77, 0x28, - 0x79, 0x2c, 0xed, 0x06, - 0xba, 0x98, 0x99, 0x22, - 0x85, 0x01, 0x7f, 0x73, - 0x31, 0xad, 0x9a, 0x60, - 0x51, 0x35, 0xee, 0x2c, - 0x17, 0xbd, 0x34, 0x44, - 0x6f, 0x54, 0xa8, 0x1d, - 0xe6, 0xfb, 0x32, 0x35, - 0x6d, 0xb9, 0x37, 0x26, - 0x84, 0x40, 0xb0, 0x02, - }; - } - - - namespace t24 { - int internal_rounds = 21; - - int alpha = 7; - - unsigned char mat_diag_m_1[] = { - 0xf0, 0x33, 0x91, 0x40, - 0xa1, 0xa8, 0x67, 0x16, - 0xb6, 0xc7, 0xa6, 0x06, - 0x0e, 0x16, 0x53, 0x6f, - 0xd1, 0x11, 0x3b, 0x27, - 0x5d, 0x6c, 0x17, 0x03, - 0xf9, 0xbb, 0xf9, 0x72, - 0x91, 0xba, 0xce, 0x73, - 0x1d, 0xf8, 0xde, 0x5c, - 0x85, 0x32, 0x39, 0x01, - 0x06, 0xee, 0xda, 0x46, - 0xa6, 0x7b, 0x5d, 0x06, - 0x6f, 0x2d, 0xd7, 0x52, - 0xe0, 0x05, 0xdd, 0x05, - 0x63, 0x4b, 0xab, 0x3b, - 0x42, 0x38, 0xda, 0x6a, - 0xec, 0xfb, 0xc5, 0x2f, - 0xb0, 0x61, 0x0d, 0x77, - 0xe9, 0xaa, 0x15, 0x57, - 0x90, 0x0e, 0xef, 0x03, - 0x70, 0xc7, 0xb6, 0x75, - 0x5f, 0xdf, 0x2a, 0x24, - 0x4c, 0xca, 0xd0, 0x00, - 0x88, 0xe3, 0xc0, 0x36, - }; - - unsigned char round_constants[] = { - 0x37, 0x0c, 0xa2, 0x0f, - 0x97, 0xbb, 0x95, 0x07, - 0x9c, 0x0b, 0xc6, 0x12, - 0x8e, 0xd8, 0xab, 0x0e, - 0xca, 0x85, 0x64, 0x09, - 0x27, 0x35, 0x09, 0x07, - 0x50, 0x4e, 0x1d, 0x1b, - 0xce, 0x1a, 0xa0, 0x30, - 0x5a, 0x6f, 0xd8, 0x3b, - 0x28, 0x7c, 0xaf, 0x69, - 0x5f, 0x77, 0x94, 0x3f, - 0xe8, 0x60, 0x15, 0x73, - 0xcd, 0x0e, 0x5a, 0x46, - 0x07, 0xf8, 0x4e, 0x57, - 0x70, 0x48, 0xfd, 0x62, - 0x44, 0xfe, 0xcc, 0x52, - 0x14, 0x2b, 0x77, 0x14, - 0x71, 0xf3, 0xed, 0x4d, - 0x7c, 0xcd, 0x0a, 0x26, - 0x58, 0xdc, 0x51, 0x1f, - 0x32, 0x55, 0x12, 0x75, - 0x7b, 0x4d, 0x6a, 0x68, - 0x79, 0xc1, 0xba, 0x54, - 0x06, 0x77, 0x94, 0x31, - 0x3b, 0x9d, 0x79, 0x29, - 0x90, 0xae, 0x01, 0x6e, - 0x64, 0x7a, 0x3a, 0x20, - 0xbe, 0x25, 0x7e, 0x4f, - 0x77, 0x3f, 0x50, 0x72, - 0x69, 0x3b, 0xbd, 0x45, - 0xb4, 0xd6, 0x9b, 0x76, - 0x08, 0x7f, 0x86, 0x5a, - 0x82, 0xa0, 0xdb, 0x4f, - 0x18, 0x43, 0x1c, 0x25, - 0x01, 0x62, 0xf0, 0x28, - 0x3a, 0xc4, 0x88, 0x67, - 0x99, 0x6a, 0x6d, 0x4c, - 0xa8, 0x84, 0x77, 0x35, - 0x51, 0xf0, 0xba, 0x2a, - 0xe6, 0x7d, 0x0f, 0x77, - 0x84, 0xb7, 0x94, 0x17, - 0x7a, 0xc5, 0x96, 0x47, - 0x10, 0x7a, 0x4b, 0x72, - 0xa7, 0x89, 0x99, 0x44, - 0xf1, 0x5c, 0x93, 0x64, - 0xac, 0x4a, 0xe1, 0x59, - 0xb8, 0x0b, 0x62, 0x0e, - 0x3b, 0xa3, 0xf5, 0x3a, - 0x0e, 0xcc, 0x65, 0x44, - 0x8f, 0xf6, 0x9d, 0x01, - 0x68, 0xd0, 0xf8, 0x4a, - 0x82, 0x4f, 0x78, 0x08, - 0xae, 0xde, 0xef, 0x0c, - 0x67, 0xa4, 0x37, 0x63, - 0x16, 0x7a, 0xfa, 0x32, - 0xd6, 0x62, 0x6f, 0x48, - 0x80, 0x74, 0x6a, 0x38, - 0x4a, 0x7c, 0xf1, 0x20, - 0xa8, 0x0d, 0xe5, 0x54, - 0x03, 0xcf, 0x12, 0x20, - 0x50, 0x29, 0xe5, 0x5f, - 0xcd, 0xb6, 0xaf, 0x09, - 0x4e, 0x04, 0x23, 0x25, - 0xef, 0xd0, 0x54, 0x5c, - 0x3c, 0x1f, 0xc0, 0x71, - 0xfb, 0xc4, 0xb2, 0x60, - 0x79, 0xb3, 0x50, 0x40, - 0xa5, 0x70, 0x6a, 0x5e, - 0xf5, 0x43, 0x85, 0x41, - 0x56, 0xbe, 0xde, 0x71, - 0x94, 0x29, 0xad, 0x1a, - 0x83, 0xa4, 0x68, 0x33, - 0x3a, 0x6f, 0xa8, 0x07, - 0xf1, 0x3f, 0xa4, 0x5e, - 0x0e, 0x78, 0x43, 0x24, - 0xf7, 0x44, 0xe4, 0x4c, - 0x82, 0x98, 0x6f, 0x14, - 0x89, 0xb0, 0x32, 0x31, - 0x56, 0xa8, 0x7e, 0x19, - 0xc3, 0x30, 0x70, 0x66, - 0xdc, 0xd5, 0x17, 0x23, - 0xa7, 0x48, 0x2c, 0x0c, - 0x66, 0xdf, 0xb2, 0x56, - 0xe9, 0x81, 0xbd, 0x67, - 0x19, 0xfb, 0xcd, 0x4f, - 0x32, 0xef, 0xaa, 0x4b, - 0x0a, 0xd3, 0x28, 0x03, - 0x0d, 0x76, 0x35, 0x62, - 0x12, 0x29, 0x43, 0x12, - 0x58, 0xe2, 0x49, 0x0a, - 0x70, 0x1b, 0x0e, 0x03, - 0x03, 0xeb, 0xca, 0x48, - 0xe9, 0xd9, 0xe4, 0x49, - 0xc6, 0xb5, 0x51, 0x10, - 0xbe, 0xdb, 0x36, 0x6a, - 0xa5, 0x27, 0xff, 0x4c, - 0xc2, 0x8e, 0xa7, 0x1d, - 0x24, 0x09, 0x0b, 0x73, - 0xf3, 0x6c, 0xb5, 0x3e, - 0x73, 0x30, 0xd9, 0x5b, - 0x97, 0x4c, 0x20, 0x37, - 0x89, 0x2d, 0x64, 0x51, - 0xe8, 0x43, 0xe9, 0x66, - 0xde, 0x72, 0x3e, 0x1a, - 0xe9, 0xb1, 0xbe, 0x70, - 0x3f, 0x3b, 0xff, 0x30, - 0xc4, 0xd1, 0x40, 0x42, - 0x8d, 0x7b, 0x64, 0x12, - 0x65, 0x69, 0xd8, 0x65, - 0x7c, 0x4d, 0xef, 0x49, - 0x97, 0x56, 0x78, 0x47, - 0x9f, 0x96, 0xb3, 0x46, - 0x0e, 0x7a, 0x7b, 0x5c, - 0x60, 0xfc, 0x78, 0x70, - 0x82, 0xd4, 0x22, 0x4f, - 0xee, 0x9a, 0x2a, 0x48, - 0x9d, 0x83, 0xeb, 0x6b, - 0xad, 0x59, 0x29, 0x03, - 0x6a, 0xaf, 0x18, 0x2b, - 0x8c, 0xdc, 0xd3, 0x55, - 0xc8, 0x26, 0xbd, 0x43, - 0x5f, 0x59, 0x41, 0x0c, - 0xe2, 0xd2, 0x48, 0x70, - 0x83, 0x89, 0xdb, 0x00, - 0xd7, 0x63, 0xf5, 0x2a, - 0x8f, 0x75, 0x84, 0x6e, - 0xe1, 0x64, 0x1d, 0x61, - 0xe2, 0x77, 0x99, 0x1f, - 0x0a, 0x3a, 0x16, 0x64, - 0x7b, 0xc2, 0x5f, 0x5c, - 0x61, 0x25, 0xe2, 0x02, - 0xdb, 0x75, 0x2d, 0x3a, - 0x1a, 0xb7, 0xa7, 0x1b, - 0x64, 0x3f, 0x34, 0x34, - 0x5d, 0xb3, 0x06, 0x74, - 0x99, 0x82, 0xdf, 0x19, - 0x0a, 0x48, 0xf4, 0x6f, - 0xc8, 0x81, 0x4a, 0x51, - 0xce, 0x52, 0xab, 0x57, - 0x52, 0x9f, 0xd6, 0x6a, - 0x0d, 0x0e, 0x0c, 0x3e, - 0x14, 0x61, 0x12, 0x48, - 0xcc, 0x62, 0x9d, 0x2a, - 0x23, 0x1f, 0x44, 0x17, - 0xbb, 0x62, 0x57, 0x48, - 0x74, 0x86, 0x21, 0x2f, - 0x4a, 0xc6, 0xfd, 0x06, - 0xf2, 0xb7, 0x61, 0x08, - 0xe6, 0xee, 0x36, 0x3b, - 0x40, 0x10, 0xa1, 0x70, - 0x37, 0x17, 0xb3, 0x04, - 0x72, 0xa8, 0x22, 0x37, - 0x63, 0x1c, 0x35, 0x2a, - 0xdc, 0x60, 0x35, 0x62, - 0xb2, 0x4a, 0x58, 0x62, - 0x04, 0x7c, 0x2c, 0x38, - 0xc7, 0xed, 0xf9, 0x3b, - 0x51, 0xfe, 0x38, 0x0e, - 0x10, 0x3b, 0x6f, 0x37, - 0x78, 0xe1, 0x81, 0x53, - 0xc7, 0x61, 0xfc, 0x3a, - 0x4d, 0xcb, 0x1b, 0x5c, - 0x1f, 0xce, 0x43, 0x66, - 0xc1, 0xf1, 0x0a, 0x2d, - 0xcc, 0x83, 0xf5, 0x08, - 0x0f, 0xf6, 0x6f, 0x5d, - 0xe5, 0xc1, 0x24, 0x63, - 0xb7, 0x2f, 0x41, 0x74, - 0x2e, 0x19, 0xc0, 0x70, - 0x41, 0xf1, 0x72, 0x0b, - 0x11, 0xa1, 0x67, 0x40, - 0x4f, 0x8c, 0x38, 0x57, - 0xec, 0x09, 0x10, 0x35, - 0x59, 0xc1, 0x74, 0x09, - 0xb3, 0x58, 0x9a, 0x53, - 0xff, 0x0c, 0x8c, 0x03, - 0x92, 0x03, 0x6c, 0x47, - 0x5f, 0xc1, 0x7b, 0x3f, - 0x2c, 0xdd, 0x91, 0x44, - 0x55, 0xef, 0x1f, 0x4d, - 0xe3, 0x6a, 0x93, 0x04, - 0xd4, 0x4d, 0x21, 0x58, - 0xad, 0x6a, 0x3c, 0x68, - 0x6b, 0xf1, 0x42, 0x1b, - 0x35, 0x91, 0xc7, 0x6d, - 0xec, 0x71, 0x4e, 0x2d, - 0xea, 0x46, 0x29, 0x3e, - 0xdb, 0xe8, 0xdc, 0x59, - 0x2a, 0x89, 0xee, 0x6c, - 0x50, 0x73, 0xf0, 0x47, - 0x93, 0xce, 0x06, 0x71, - 0xa9, 0xa7, 0xd4, 0x3b, - 0x6a, 0x63, 0xfe, 0x2b, - 0xe9, 0x11, 0x00, 0x43, - 0x6a, 0xd6, 0x1c, 0x00, - 0x5b, 0xaf, 0x7f, 0x30, - 0xfe, 0xf3, 0x9e, 0x0d, - 0x3a, 0x04, 0x40, 0x6d, - 0x0c, 0x47, 0x8f, 0x2e, - 0xe8, 0x65, 0x68, 0x1b, - 0x01, 0x6c, 0x0e, 0x0c, - 0x1f, 0x98, 0x41, 0x4d, - 0x3d, 0x9d, 0x3b, 0x42, - 0xcc, 0x08, 0x04, 0x41, - 0x84, 0x08, 0x3f, 0x26, - 0xd0, 0xbb, 0x11, 0x53, - 0xd8, 0x58, 0xae, 0x4d, - 0xea, 0x1c, 0x40, 0x30, - 0x75, 0xa5, 0xaf, 0x09, - 0x42, 0x5b, 0x3d, 0x4b, - 0x37, 0x0b, 0xac, 0x63, - 0x14, 0xbb, 0xe5, 0x5f, - 0xd4, 0xe9, 0x44, 0x52, - }; - } - -} -#endif diff --git a/icicle/include/poseidon2/constants/bn254_poseidon2.h b/icicle/include/poseidon2/constants/bn254_poseidon2.h deleted file mode 100644 index f4e056a17..000000000 --- a/icicle/include/poseidon2/constants/bn254_poseidon2.h +++ /dev/null @@ -1,1365 +0,0 @@ -#pragma once -#ifndef BN254_POSEIDON2_H -#define BN254_POSEIDON2_H - -namespace poseidon2_constants_bn254 { - - namespace t2 { - int internal_rounds = 56; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - }; - - unsigned char round_constants[] = { - 0xa7, 0xd7, 0xab, 0xd0, 0xdb, 0xc0, 0x7d, 0x6c, 0x1b, 0xdd, 0x4c, 0x53, 0x77, 0xa1, 0x1a, 0xa7, 0x38, 0xba, 0x4c, 0x29, 0xba, 0xaa, 0x1f, 0xfe, 0xd4, 0x9b, 0x8e, 0xc6, 0x9e, 0x6e, 0xc4, 0x09, - 0x81, 0xcb, 0x04, 0xa6, 0xff, 0x83, 0x1d, 0x3c, 0x34, 0xb8, 0x05, 0xe4, 0x3a, 0x2b, 0x14, 0xc5, 0xcf, 0x35, 0x31, 0x7f, 0x93, 0xed, 0x97, 0x2a, 0xc4, 0xee, 0x96, 0x08, 0x53, 0x56, 0x03, 0x0c, - 0x30, 0x4a, 0x15, 0xcc, 0x77, 0xa9, 0x7e, 0x31, 0xbd, 0x68, 0x62, 0xbd, 0xaa, 0xa5, 0x0e, 0xa0, 0xf4, 0x4c, 0xb5, 0x2b, 0x18, 0x51, 0x2e, 0x14, 0xd1, 0x8a, 0x69, 0x35, 0xd9, 0xa1, 0x28, 0x1e, - 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0x2a, 0xa0, 0x99, 0xb7, 0x4b, 0xb6, 0x5b, 0x1d, 0x4d, 0x4e, 0x2d, 0x74, 0x3d, 0x14, 0xe2, 0x79, 0xb9, 0xfe, 0xf4, 0x79, 0x4b, 0x74, 0x87, 0xd6, 0xad, 0xe9, 0x94, 0x95, 0xdc, 0xc4, 0xbf, 0x28, - }; - } - - - namespace t12 { - int internal_rounds = 57; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xfa, 0x4f, 0x8f, 0xdd, 0x79, 0xad, 0x75, 0x10, 0xc3, 0x18, 0x12, 0xf6, 0x6f, 0x14, 0x1f, 0xea, 0xa2, 0x13, 0xea, 0x85, 0xc8, 0xf2, 0xe2, 0xb8, 0x80, 0xfe, 0x0b, 0xf4, 0x98, 0x1e, 0xbb, 0x20, - 0xb2, 0x9f, 0x64, 0x5c, 0x5e, 0x8d, 0x5f, 0x8f, 0x51, 0xa6, 0x5f, 0x91, 0x49, 0xf2, 0x8a, 0x1f, 0xfa, 0x02, 0x0f, 0xd9, 0x77, 0x0c, 0x4c, 0xa1, 0x8f, 0x67, 0x8d, 0x5c, 0xd0, 0xab, 0xd6, 0x1e, - 0x46, 0xcf, 0xe2, 0xb4, 0xf9, 0x81, 0xcb, 0xc7, 0x96, 0x07, 0x7e, 0x77, 0x81, 0x2b, 0x38, 0x04, 0xc5, 0xbb, 0xd6, 0x75, 0x39, 0x06, 0xf5, 0x66, 0xd5, 0x47, 0x8b, 0x65, 0x18, 0x7b, 0x10, 0x29, - 0x23, 0xe9, 0x9a, 0x47, 0x9c, 0x1d, 0x09, 0x17, 0xeb, 0xdb, 0xf5, 0x6f, 0x98, 0x46, 0x56, 0x7c, 0xc2, 0xc2, 0x29, 0xf0, 0x4f, 0x80, 0xc9, 0x6f, 0xd7, 0x9d, 0x06, 0xec, 0xc3, 0xee, 0x4c, 0x13, - 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}; - } - - - namespace t16 { - int internal_rounds = 57; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xe4, 0x68, 0x57, 0x23, 0xae, 0xa1, 0xb5, 0x33, 0x2b, 0xfb, 0xd9, 0xa7, 0x0c, 0x1c, 0xf2, 0x3e, 0x6a, 0x36, 0x7c, 0x13, 0xcd, 0xb7, 0xc1, 0x09, 0xa7, 0xe1, 0x0a, 0x0e, 0x7c, 0xaf, 0x9a, 0x26, - 0xe3, 0x1d, 0xeb, 0x30, 0x0c, 0xa2, 0x82, 0xfb, 0xb2, 0xec, 0x35, 0x2f, 0x79, 0xd0, 0x0d, 0xa1, 0x7d, 0xb2, 0xf3, 0xfa, 0xed, 0x29, 0x72, 0x31, 0x4e, 0x61, 0x32, 0x40, 0xe0, 0x3e, 0x54, 0x30, - 0xc4, 0x5f, 0x3c, 0xb8, 0xb8, 0x69, 0x84, 0xd6, 0x4b, 0xad, 0x32, 0xe3, 0x02, 0xd0, 0xf1, 0x42, 0xe6, 0x5c, 0xc7, 0xef, 0x44, 0xfd, 0x3f, 0xd7, 0xd8, 0xb7, 0x60, 0x31, 0xb1, 0x16, 0x74, 0x01, - 0xaf, 0x9e, 0x93, 0x14, 0x06, 0x49, 0x81, 0x6c, 0xd3, 0x02, 0xd9, 0x66, 0xb9, 0x73, 0xd7, 0x43, 0xc4, 0xd3, 0x90, 0x34, 0x5d, 0xbc, 0xb6, 0xab, 0x1a, 0x3f, 0xa4, 0x38, 0xf4, 0x03, 0xb1, 0x09, - 0x30, 0xdb, 0xfc, 0x83, 0x49, 0x61, 0x13, 0x15, 0x18, 0xab, 0x51, 0x94, 0x48, 0xc4, 0x62, 0x14, 0x26, 0x0d, 0x83, 0x2c, 0xe4, 0xbd, 0x55, 0xda, 0x82, 0xa8, 0x1a, 0xa2, 0x1e, 0xe8, 0xf9, 0x08, - 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0xd5, 0x2a, 0x5d, 0xab, 0x65, 0xe1, 0x4a, 0x07, 0x6f, 0x40, 0xd1, 0x19, 0x45, 0x7a, 0xc6, 0xe2, 0x4b, 0x48, 0xf7, 0x0b, 0x97, 0x58, 0xcb, 0x66, 0x0d, 0xbd, 0xe6, 0xaf, 0x38, 0x77, 0x8b, 0x0f, - 0x1a, 0xc7, 0x28, 0xb1, 0xa7, 0x3b, 0xfe, 0xc6, 0x7a, 0x7f, 0xef, 0x76, 0x84, 0xa8, 0x02, 0xb9, 0xaa, 0xc3, 0x0a, 0xbb, 0x11, 0x72, 0x6d, 0xcc, 0xcd, 0xac, 0x73, 0xe6, 0x90, 0xbe, 0x4d, 0x29, - 0x39, 0xe0, 0x4a, 0x3d, 0xee, 0xa4, 0x3a, 0xfa, 0x2c, 0xce, 0x4f, 0x9b, 0x42, 0xc9, 0x40, 0xcf, 0x3c, 0x71, 0xd7, 0xd7, 0xf9, 0xe3, 0xac, 0x4b, 0xb1, 0x33, 0xd5, 0xca, 0xce, 0xf9, 0xc3, 0x05, - 0xe1, 0xd7, 0x10, 0x05, 0xf4, 0x38, 0x40, 0x0f, 0xb8, 0x26, 0x11, 0xb4, 0xca, 0x1d, 0x4f, 0xad, 0xb6, 0x28, 0xee, 0x24, 0x8f, 0x87, 0x87, 0x37, 0x2a, 0xdf, 0xc3, 0x2a, 0x87, 0xff, 0xcb, 0x26, - 0xce, 0xbd, 0x79, 0xe3, 0x61, 0x55, 0x60, 0x6a, 0x6d, 0xca, 0x71, 0xaa, 0x17, 0xe7, 0x05, 0xc0, 0x0d, 0x10, 0xba, 0x39, 0x92, 0xde, 0x4e, 0x42, 0xc1, 0xb9, 0x87, 0xc9, 0x93, 0xb4, 0xa0, 0x1b, - }; - - unsigned char round_constants[] = { - 0xff, 0xac, 0xce, 0xaa, 0xfb, 0xad, 0xf0, 0x63, 0xa2, 0xdf, 0x8f, 0x44, 0x57, 0xc5, 0xde, 0x2c, 0x00, 0x6f, 0x32, 0xc7, 0x72, 0xc6, 0xb3, 0xe8, 0xf5, 0xf9, 0xba, 0xe2, 0x0a, 0xc5, 0xa8, 0x11, - 0x43, 0x4e, 0xc3, 0x6a, 0x59, 0x54, 0x8e, 0x94, 0x33, 0x42, 0xd1, 0xa7, 0x5a, 0x71, 0x3f, 0x0e, 0x92, 0xe8, 0x40, 0xe8, 0x0e, 0xf0, 0x54, 0x87, 0xc1, 0xaa, 0x3d, 0x37, 0xc4, 0xf6, 0x2a, 0x06, - 0x99, 0x50, 0x73, 0xde, 0xa1, 0x62, 0xf6, 0x1d, 0x0a, 0x56, 0x9d, 0x1f, 0x54, 0x8b, 0xe1, 0x4e, 0x46, 0x36, 0xb9, 0x79, 0xad, 0x62, 0x2f, 0x00, 0xdc, 0xc8, 0x69, 0x27, 0x74, 0x68, 0x76, 0x06, - 0x04, 0x29, 0x72, 0x87, 0x45, 0xf3, 0xf4, 0xea, 0x20, 0x79, 0xcf, 0xa1, 0x82, 0x62, 0x5a, 0xb8, 0xa7, 0x05, 0xc6, 0xc5, 0x23, 0x0f, 0xd6, 0xfd, 0xc4, 0xed, 0xb5, 0xce, 0xab, 0x66, 0x25, 0x09, - 0x8a, 0xac, 0xa8, 0xee, 0xdd, 0xc4, 0x41, 0x6d, 0x6f, 0x8a, 0x92, 0xf4, 0xe8, 0x52, 0xf9, 0x98, 0x7f, 0x93, 0x3d, 0x0d, 0xb8, 0x0e, 0xb9, 0xd6, 0x69, 0xb5, 0x89, 0xf0, 0x8a, 0x67, 0xce, 0x1d, - 0xc3, 0xad, 0xa2, 0xca, 0x02, 0x1d, 0x05, 0xa6, 0xb7, 0x5e, 0xb6, 0xe6, 0xf6, 0x1b, 0x9e, 0xc7, 0xd4, 0x26, 0x47, 0x88, 0xfc, 0x27, 0x60, 0xdb, 0x75, 0x51, 0xe5, 0x83, 0xd9, 0x0b, 0x0f, 0x0a, - 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int internal_rounds = 57; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x95, 0x94, 0xf6, 0xe2, 0x1e, 0x94, 0x1e, 0x70, 0xad, 0xd3, 0x22, 0x26, 0x86, 0xc8, 0x99, 0xef, 0xcc, 0x63, 0x27, 0xfc, 0xd5, 0xb1, 0x18, 0x22, 0x86, 0xd4, 0x1d, 0xd0, 0x5c, 0x16, 0x47, 0x12, - 0x6c, 0x0e, 0xc2, 0xf2, 0xb7, 0xc6, 0xf6, 0x08, 0xd2, 0xdf, 0xbe, 0x9e, 0xd0, 0x12, 0x29, 0xf3, 0x86, 0x84, 0xe7, 0x5e, 0xa0, 0xfb, 0x05, 0xf2, 0x34, 0xf1, 0xb0, 0xe3, 0x46, 0xcc, 0x50, 0x05, - 0xa8, 0x3c, 0xcb, 0xfd, 0xb5, 0x09, 0x0b, 0x35, 0x77, 0x7e, 0x2f, 0xd4, 0x70, 0x3a, 0x95, 0xd2, 0x1c, 0xfb, 0xc8, 0x0c, 0x65, 0x6f, 0xf3, 0xc3, 0x9c, 0xf3, 0x39, 0x1c, 0xa0, 0xf1, 0x64, 0x0e, - 0x1d, 0x21, 0xdd, 0x57, 0x20, 0x03, 0xcf, 0x4f, 0x8b, 0xea, 0x49, 0x43, 0x10, 0x9f, 0x2e, 0xd5, 0x86, 0x41, 0xe8, 0xd9, 0xb6, 0xee, 0x93, 0x89, 0x0f, 0x4d, 0x4c, 0x74, 0x4c, 0x57, 0x97, 0x18, - 0x0e, 0xed, 0x36, 0xbe, 0x94, 0x74, 0x14, 0x50, 0x8e, 0x4d, 0x70, 0x8b, 0x76, 0x88, 0x3b, 0xc9, 0xf2, 0x50, 0x77, 0x27, 0x1d, 0xee, 0xff, 0x32, 0x73, 0xf4, 0x8b, 0x9c, 0x4b, 0xb6, 0xb1, 0x13, - 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0x1a, 0x4b, 0x9e, 0x10, 0xf8, 0xe4, 0x34, 0x8a, 0x32, 0x11, 0x4f, 0xb6, 0xe0, 0x8a, 0x16, 0xf9, 0x28, 0xea, 0x7a, 0x27, 0x2e, 0xfa, 0x75, 0x18, 0xfa, 0xbb, 0x0f, 0x03, 0xd0, 0x90, 0x54, 0x30, - 0x10, 0xd5, 0xa5, 0xf2, 0xf9, 0xc6, 0x5c, 0xfa, 0xdf, 0x08, 0xdb, 0x94, 0x6d, 0xa0, 0x00, 0x6b, 0x45, 0xb9, 0x86, 0x39, 0x72, 0xb2, 0x50, 0x5a, 0xb5, 0xa0, 0x29, 0x5e, 0x37, 0x5b, 0x67, 0x02, - 0xcd, 0x44, 0x0f, 0xe1, 0x7a, 0x30, 0x01, 0x6b, 0x26, 0x1c, 0x7f, 0xcb, 0xc4, 0x3f, 0x1c, 0xb7, 0x65, 0xdc, 0xa0, 0x4b, 0xfa, 0xea, 0xd8, 0xd4, 0x49, 0x86, 0x71, 0xe6, 0xa1, 0x54, 0x8b, 0x2d, - }; - - unsigned char round_constants[] = { - 0x13, 0x90, 0x8b, 0x85, 0xd2, 0x26, 0x36, 0x53, 0x99, 0x1c, 0xd0, 0x36, 0x23, 0xd8, 0x13, 0x32, 0xd4, 0xc5, 0x7c, 0x09, 0x47, 0x8b, 0x76, 0xfe, 0x58, 0x29, 0x86, 0x80, 0x1e, 0xf3, 0x65, 0x28, - 0x02, 0xe1, 0xb0, 0xad, 0xe2, 0xbb, 0x78, 0xdf, 0x21, 0x7d, 0xcf, 0x8b, 0xc7, 0x99, 0xfd, 0xea, 0xe7, 0x88, 0x74, 0xa1, 0xad, 0xcb, 0xfa, 0x89, 0x6f, 0x25, 0x41, 0x84, 0x20, 0x58, 0x64, 0x04, - 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0x0a, 0x38, 0x34, 0xe4, 0x4d, 0x6b, 0xfe, 0xcd, 0x36, 0xec, 0x57, 0xa8, 0x2e, 0x01, 0xf6, 0x4c, 0x8f, 0x1f, 0xdf, 0xc2, 0xff, 0x3e, 0x34, 0x40, 0x5b, 0xcf, 0xfd, 0x7d, 0x94, 0x14, 0x68, 0x2b, - 0x46, 0xde, 0xb7, 0x5d, 0xbf, 0xb9, 0x71, 0x52, 0xff, 0x39, 0xcc, 0xe8, 0x21, 0x09, 0x3c, 0x8f, 0xdc, 0x81, 0x3c, 0xad, 0x10, 0x1c, 0x46, 0x71, 0x7b, 0x65, 0xcd, 0x4d, 0x54, 0x45, 0x64, 0x2c, - 0xec, 0xa1, 0x23, 0x72, 0xbf, 0x01, 0x21, 0xe2, 0x04, 0x12, 0x28, 0x66, 0xa3, 0x0d, 0x10, 0x51, 0x4c, 0xd7, 0x1e, 0x46, 0xb0, 0x28, 0xf1, 0xaf, 0x68, 0x0a, 0x45, 0x00, 0x7f, 0xc7, 0x1c, 0x1f, - 0x15, 0x54, 0x6a, 0x2a, 0x56, 0xf2, 0xb8, 0xe9, 0x84, 0x4d, 0xed, 0x54, 0x25, 0xe1, 0x81, 0xa8, 0x85, 0x0e, 0x13, 0xf6, 0x6a, 0xbd, 0x23, 0x37, 0x0d, 0xc9, 0xf6, 0x21, 0xa4, 0xb3, 0x12, 0x30, - 0xe3, 0xc8, 0x32, 0x3e, 0x01, 0x75, 0xe6, 0x3e, 0xfb, 0x7d, 0x39, 0x20, 0x91, 0xf6, 0x33, 0xaa, 0xc4, 0xaa, 0x73, 0x5f, 0x44, 0x1d, 0xb1, 0x6d, 0x7b, 0x51, 0xb2, 0x3f, 0x0f, 0xfa, 0x6a, 0x20, - }; - } - - - namespace t24 { - int internal_rounds = 57; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xf9, 0x2c, 0x67, 0xca, 0xaa, 0x08, 0x62, 0xa9, 0x85, 0xac, 0x27, 0x7b, 0xb3, 0xc0, 0xb5, 0x67, 0x6b, 0x7c, 0xc0, 0x0a, 0xd8, 0xb5, 0x46, 0x88, 0xa0, 0xc2, 0x70, 0x52, 0x5e, 0x50, 0x1d, 0x07, - 0xbe, 0xaf, 0x39, 0xcd, 0x23, 0x45, 0x6a, 0x45, 0x29, 0xf8, 0x91, 0x9e, 0x9a, 0x53, 0x56, 0x4f, 0x73, 0x21, 0x57, 0x32, 0xab, 0x0b, 0xe2, 0xc4, 0xb9, 0x5a, 0xf3, 0x59, 0x36, 0xf7, 0xe8, 0x16, - 0xc6, 0x5e, 0x7d, 0xb7, 0xc5, 0x14, 0xe1, 0x15, 0xf2, 0xcf, 0x91, 0xa7, 0x79, 0x07, 0x1f, 0x81, 0xda, 0x38, 0xb1, 0xaf, 0x55, 0x86, 0xa9, 0x54, 0x05, 0xc7, 0xbd, 0xcf, 0xee, 0xea, 0x33, 0x00, - 0x8a, 0x8c, 0x4b, 0xeb, 0x9f, 0xe7, 0xc4, 0xd8, 0x7e, 0x31, 0xf7, 0x0d, 0xa5, 0x38, 0x44, 0x2a, 0xb7, 0xb0, 0x4c, 0x93, 0x78, 0xfa, 0xe1, 0xcd, 0x05, 0x3c, 0x8a, 0xcf, 0x39, 0xe9, 0x63, 0x01, - 0x67, 0x51, 0x04, 0xab, 0xed, 0x6f, 0xba, 0x52, 0x15, 0x34, 0xef, 0x7b, 0x5e, 0x17, 0x46, 0x3f, 0xb5, 0x8c, 0x30, 0x4f, 0xaa, 0x41, 0x25, 0x62, 0x58, 0x7c, 0x75, 0x17, 0x21, 0xbc, 0xe3, 0x00, - 0x5a, 0xd8, 0x3e, 0x53, 0x53, 0xb7, 0x2a, 0x5b, 0xcf, 0x8e, 0x49, 0x2f, 0x78, 0x1c, 0x0a, 0xcc, 0x5e, 0xfe, 0xb9, 0x08, 0x94, 0x3c, 0xd7, 0xc8, 0x34, 0x9b, 0x60, 0xc5, 0x4a, 0x17, 0x66, 0x0f, - 0x30, 0x17, 0x51, 0x7f, 0xc2, 0xe0, 0xd9, 0xa1, 0x89, 0xe2, 0x1f, 0x53, 0xc3, 0x7b, 0xc7, 0xca, 0x1e, 0x84, 0x37, 0x92, 0x21, 0x88, 0x68, 0x31, 0xc0, 0xf8, 0x43, 0x85, 0x18, 0xa4, 0x11, 0x00, - 0x84, 0xf7, 0x5c, 0x70, 0xbe, 0xd6, 0x15, 0x0f, 0x98, 0xec, 0x3f, 0xee, 0x31, 0xda, 0x80, 0xaf, 0xf9, 0x96, 0xd2, 0x34, 0x06, 0xee, 0x41, 0x46, 0x65, 0x4a, 0x20, 0x5b, 0xa3, 0xcd, 0x79, 0x2a, - 0xc0, 0x27, 0x65, 0x89, 0x3a, 0x29, 0x23, 0x2c, 0xfd, 0x69, 0x45, 0xe7, 0x23, 0x59, 0x5a, 0x54, 0x8f, 0x1d, 0x0e, 0x40, 0xf3, 0x94, 0x94, 0x1f, 0x1a, 0xaf, 0xac, 0x00, 0xc4, 0x39, 0x55, 0x08, - 0x0c, 0x77, 0xf1, 0x9c, 0x38, 0x2f, 0xef, 0xa2, 0xb8, 0xe2, 0xd2, 0x70, 0x11, 0x69, 0x9d, 0x69, 0xf8, 0xdd, 0xdb, 0xff, 0xb3, 0x22, 0x52, 0x39, 0x39, 0xc5, 0x2f, 0xc3, 0x16, 0x84, 0xfe, 0x01, - 0xcf, 0x15, 0x24, 0x16, 0x2b, 0x79, 0xae, 0xa2, 0xa6, 0x46, 0x07, 0x7b, 0x94, 0x04, 0x22, 0xab, 0xe6, 0xf7, 0xbf, 0x71, 0xbd, 0x97, 0x5d, 0x17, 0xa1, 0x6b, 0x12, 0x96, 0xac, 0xf5, 0x11, 0x1c, - 0x2e, 0x3f, 0x5e, 0xe3, 0xe0, 0x2a, 0x57, 0xb2, 0x9a, 0xe8, 0xac, 0x24, 0x01, 0x89, 0x04, 0x67, 0xba, 0x5b, 0xe5, 0xab, 0x12, 0x80, 0xcb, 0x5f, 0x19, 0x84, 0x7c, 0x0e, 0x45, 0xee, 0x12, 0x2b, - 0x42, 0x5d, 0x53, 0xa0, 0xbd, 0xda, 0xe0, 0x59, 0x77, 0x66, 0x86, 0x64, 0xa2, 0x66, 0x89, 0x8c, 0x78, 0x51, 0x71, 0x71, 0xd3, 0x28, 0xa1, 0xfc, 0xb9, 0xa6, 0x6b, 0x90, 0x51, 0xdf, 0x65, 0x01, - 0xc7, 0x0c, 0x24, 0xaa, 0x33, 0x95, 0xd4, 0x50, 0xaa, 0x8e, 0xd3, 0x77, 0x75, 0xfd, 0x4f, 0xae, 0x83, 0x20, 0xad, 0xfb, 0xd6, 0x63, 0x6c, 0x51, 0xd9, 0xbc, 0x52, 0xd9, 0x88, 0x90, 0x99, 0x10, - 0x8c, 0x7e, 0x28, 0x5a, 0xaa, 0xb3, 0xb6, 0x43, 0xa9, 0xd6, 0x94, 0xe1, 0x41, 0x7f, 0x2d, 0x1f, 0x73, 0x54, 0x04, 0x7f, 0xe8, 0xdb, 0xb8, 0x7a, 0x69, 0xc3, 0x23, 0x53, 0x44, 0xe4, 0xab, 0x16, - 0xcf, 0x1b, 0xb4, 0x27, 0xa3, 0x65, 0x9f, 0xf0, 0x93, 0xaf, 0x3a, 0xa0, 0x54, 0x78, 0xd4, 0x30, 0x6a, 0xef, 0x9c, 0x8b, 0x75, 0x7b, 0x42, 0x9b, 0x57, 0xb7, 0xa5, 0x47, 0x98, 0x34, 0x47, 0x0a, - 0x57, 0x62, 0xec, 0x29, 0x6d, 0x74, 0x1b, 0x17, 0x1e, 0xf4, 0x6a, 0xc9, 0xff, 0xbf, 0x3c, 0x21, 0x55, 0x9b, 0x23, 0x60, 0x2a, 0x63, 0xf1, 0x70, 0xb0, 0x87, 0x9d, 0x7d, 0x48, 0x71, 0x8c, 0x2c, - 0x1b, 0xa9, 0xda, 0x17, 0x5c, 0x2f, 0x13, 0x3c, 0x0a, 0xa3, 0x52, 0xeb, 0x53, 0x70, 0xd4, 0x5b, 0x70, 0xfc, 0xd3, 0x6f, 0x0c, 0xc1, 0x15, 0xac, 0xc3, 0x2a, 0xc9, 0x23, 0x17, 0xeb, 0x72, 0x01, - 0x83, 0x05, 0x7e, 0x21, 0x3f, 0xaf, 0x48, 0xd0, 0xbb, 0x3d, 0xf3, 0x38, 0x8e, 0xe4, 0x25, 0xae, 0x02, 0xcc, 0x3b, 0x9b, 0xd7, 0xe5, 0xdf, 0xfe, 0x1d, 0xcd, 0x6e, 0x0c, 0x98, 0x03, 0x8a, 0x00, - 0x42, 0xc6, 0x51, 0xce, 0xe0, 0xe2, 0xc5, 0x58, 0xd4, 0xbc, 0xef, 0x6d, 0xae, 0x86, 0x8c, 0xbc, 0xd0, 0x87, 0xd1, 0x40, 0x86, 0xd8, 0xa8, 0x1d, 0x0e, 0xd8, 0x83, 0x3d, 0x84, 0x92, 0x52, 0x00, - 0x2e, 0x40, 0x99, 0xf6, 0x69, 0x1c, 0xac, 0xbb, 0x27, 0x76, 0xe3, 0xbb, 0x2e, 0x6a, 0x10, 0x6e, 0x4b, 0x44, 0x9c, 0xc2, 0xff, 0xee, 0x14, 0x48, 0xc4, 0x56, 0xeb, 0x12, 0x9d, 0x3b, 0xad, 0x14, - 0x49, 0xda, 0x81, 0xc0, 0xd5, 0x85, 0x05, 0x00, 0x2e, 0x4c, 0x66, 0x18, 0x2b, 0xf8, 0x11, 0xbb, 0x17, 0xdc, 0x08, 0xf5, 0xb9, 0x81, 0xe4, 0x5d, 0xf7, 0xf1, 0xa3, 0x14, 0x3b, 0x08, 0x74, 0x2a, - 0xff, 0xc6, 0x88, 0xf2, 0xb9, 0x40, 0xb2, 0x29, 0x50, 0x23, 0x32, 0x8f, 0xf0, 0x56, 0x8a, 0x04, 0x5b, 0x78, 0xdf, 0x68, 0x4d, 0x61, 0x1b, 0xe9, 0x08, 0x28, 0x34, 0xef, 0xde, 0x45, 0x7a, 0x0b, - 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-namespace poseidon2_constants_m31 { - - namespace t2 { - int internal_rounds = 25; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x01, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, - }; - - unsigned char round_constants[] = { - 0xb4, 0x5a, 0x7e, 0x4c, - 0x13, 0xa1, 0x81, 0x59, - 0x0f, 0xae, 0x6a, 0x77, - 0x47, 0xb6, 0xf3, 0x23, - 0xa3, 0x99, 0x3f, 0x33, - 0x09, 0xb9, 0xe2, 0x78, - 0xd4, 0x7f, 0x45, 0x72, - 0x8a, 0xe1, 0xfe, 0x67, - 0x5d, 0x1e, 0x9c, 0x74, - 0xa5, 0x3d, 0x15, 0x20, - 0x9c, 0x36, 0x8a, 0x28, - 0x1a, 0x48, 0xbb, 0x39, - 0x33, 0x90, 0x4d, 0x58, - 0x89, 0x86, 0xb3, 0x4f, - 0xc8, 0xda, 0x69, 0x27, - 0xc0, 0xc5, 0x50, 0x13, - 0x89, 0xe6, 0xbd, 0x31, - 0x82, 0x9c, 0x39, 0x4d, - 0xba, 0xe4, 0x51, 0x31, - 0x21, 0x7c, 0xc6, 0x56, - 0x36, 0xae, 0xae, 0x12, - 0x90, 0x08, 0x7b, 0x7e, - 0x2c, 0x52, 0xfc, 0x28, - 0x76, 0x6e, 0x76, 0x74, - 0x30, 0x83, 0xd5, 0x57, - 0x35, 0xea, 0x14, 0x34, - 0x41, 0xc4, 0xe5, 0x38, - 0x12, 0x97, 0x36, 0x1e, - 0xf3, 0xd9, 0xb6, 0x15, - 0x0a, 0x49, 0x08, 0x48, - 0x06, 0xf3, 0xd3, 0x0a, - 0x9e, 0x4d, 0xdd, 0x70, - 0x0f, 0xa8, 0xf5, 0x47, - 0x7d, 0x0a, 0x0f, 0x67, - 0xf5, 0x15, 0x95, 0x06, - 0x6b, 0xc7, 0xe1, 0x33, - 0xeb, 0xa4, 0x88, 0x13, - 0xb2, 0x2d, 0x4f, 0x37, - 0x9a, 0xf7, 0x48, 0x06, - 0x69, 0xc1, 0x18, 0x34, - 0x81, 0x51, 0x73, 0x60, - 0x19, 0x5d, 0x03, 0x71, - 0xaf, 0x79, 0x18, 0x56, - 0x93, 0x61, 0x9d, 0x64, - 0xa3, 0xb0, 0xfd, 0x26, - 0x1d, 0x81, 0x50, 0x0a, - 0x13, 0xc9, 0x91, 0x1e, - 0xd9, 0xcd, 0x3b, 0x29, - 0x10, 0xdb, 0xdc, 0x01, - }; - } - - - namespace t3 { - int internal_rounds = 19; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x01, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, - }; - - unsigned char round_constants[] = { - 0x0a, 0x42, 0x30, 0x66, - 0xb5, 0xb6, 0x77, 0x6e, - 0xf3, 0xad, 0x5b, 0x08, - 0x3c, 0x7b, 0xd1, 0x06, - 0xbb, 0xa8, 0x2c, 0x2d, - 0x13, 0x5d, 0x58, 0x3e, - 0x9c, 0xd2, 0x96, 0x7f, - 0x89, 0x0c, 0xc6, 0x02, - 0x5d, 0xc7, 0x73, 0x3d, - 0x92, 0x53, 0x68, 0x44, - 0xb7, 0xb9, 0x45, 0x75, - 0xe4, 0xb4, 0xab, 0x14, - 0x37, 0x85, 0xbc, 0x18, - 0xc1, 0x93, 0x4f, 0x20, - 0x64, 0xb3, 0x81, 0x40, - 0x9b, 0x9f, 0xbe, 0x5c, - 0xa1, 0x90, 0x9f, 0x7f, - 0xc2, 0x7b, 0xbf, 0x3c, - 0xab, 0x6e, 0x8a, 0x26, - 0x11, 0x34, 0x24, 0x6f, - 0xf8, 0x54, 0x75, 0x2c, - 0x61, 0x2c, 0xab, 0x5f, - 0xae, 0x76, 0x13, 0x11, - 0xbe, 0x0e, 0xfe, 0x2c, - 0x29, 0xe7, 0xb2, 0x36, - 0xd3, 0x9a, 0x90, 0x29, - 0xd6, 0x20, 0xb8, 0x28, - 0x5d, 0x3e, 0x32, 0x54, - 0x2e, 0x4d, 0x6e, 0x78, - 0xd3, 0x30, 0x4f, 0x32, - 0x17, 0x3d, 0xf9, 0x65, - 0xb2, 0x73, 0x8c, 0x4f, - 0x34, 0xfe, 0xd9, 0x23, - 0xd1, 0x9b, 0x90, 0x17, - 0x54, 0x4a, 0x86, 0x74, - 0x84, 0xda, 0x20, 0x71, - 0x76, 0x83, 0x23, 0x4c, - 0x1e, 0x79, 0xd0, 0x28, - 0xb1, 0x4a, 0x4f, 0x15, - 0x7e, 0x49, 0xcd, 0x27, - 0x16, 0x8f, 0xd4, 0x32, - 0xb6, 0x68, 0x5e, 0x00, - 0x9b, 0x83, 0x6a, 0x68, - 0xfb, 0xc8, 0x24, 0x3c, - 0xd5, 0x22, 0x07, 0x53, - 0x9a, 0x79, 0xfe, 0x23, - 0x5f, 0xc0, 0x5d, 0x23, - 0x00, 0xa5, 0x12, 0x19, - 0xb0, 0xc2, 0xb5, 0x5b, - 0xd7, 0xd7, 0x42, 0x57, - 0x06, 0x76, 0x47, 0x37, - 0x71, 0x04, 0x60, 0x1d, - 0x2c, 0x8b, 0x7a, 0x42, - 0xcd, 0xec, 0x89, 0x7d, - 0x19, 0x10, 0x8e, 0x37, - }; - } - - - namespace t4 { - int internal_rounds = 22; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x7c, 0x10, 0x15, 0x0f, - 0x64, 0xfa, 0x28, 0x46, - 0xb8, 0x12, 0x41, 0x25, - 0xcb, 0x04, 0xa0, 0x08, - }; - - unsigned char round_constants[] = { - 0x22, 0x2c, 0xaf, 0x16, - 0x6b, 0xcd, 0x04, 0x78, - 0x4a, 0x23, 0x6e, 0x24, - 0x12, 0xcd, 0xd3, 0x5d, - 0x5c, 0xfb, 0x1b, 0x32, - 0x8b, 0x35, 0x50, 0x59, - 0x46, 0xa4, 0x64, 0x72, - 0x5e, 0x32, 0xa0, 0x78, - 0xc1, 0x02, 0xc5, 0x56, - 0xdf, 0x26, 0x9d, 0x45, - 0xe9, 0x80, 0xa8, 0x73, - 0xfe, 0x4b, 0xec, 0x41, - 0x2f, 0x74, 0x43, 0x55, - 0x75, 0x82, 0xf6, 0x72, - 0xb1, 0x08, 0xed, 0x19, - 0x79, 0xda, 0x7c, 0x14, - 0xca, 0xe4, 0x7e, 0x63, - 0xb6, 0x81, 0xd1, 0x0a, - 0xe5, 0xa3, 0xcb, 0x06, - 0x1a, 0x58, 0x19, 0x6d, - 0x18, 0xe5, 0x0c, 0x6e, - 0xba, 0xfc, 0xec, 0x67, - 0x3a, 0x16, 0x38, 0x13, - 0xed, 0x88, 0x73, 0x1b, - 0x46, 0x5b, 0x96, 0x33, - 0x53, 0xa5, 0x8f, 0x4e, - 0xf4, 0x11, 0x54, 0x30, - 0xe7, 0x4c, 0x75, 0x58, - 0x3d, 0x54, 0x80, 0x43, - 0xec, 0x78, 0x3f, 0x4f, - 0xa9, 0xaf, 0x2b, 0x7e, - 0x27, 0x6f, 0xe9, 0x60, - 0x74, 0x8e, 0x15, 0x30, - 0xc0, 0xac, 0xdb, 0x64, - 0xdd, 0xee, 0x16, 0x35, - 0xcc, 0x43, 0x06, 0x20, - 0x35, 0x34, 0x4c, 0x71, - 0x39, 0xae, 0xa7, 0x35, - 0x80, 0xd3, 0xa8, 0x22, - 0xb5, 0xc7, 0xb1, 0x17, - 0x89, 0x85, 0x58, 0x14, - 0x1f, 0x32, 0x25, 0x63, - 0x4f, 0x2d, 0xa1, 0x18, - 0x68, 0x2c, 0xd6, 0x1c, - 0xfc, 0xba, 0xf5, 0x1e, - 0x12, 0x30, 0x3f, 0x08, - 0xc0, 0x4d, 0x33, 0x6e, - 0x4d, 0x57, 0x0d, 0x59, - 0x07, 0x3a, 0x21, 0x14, - 0xc4, 0xe1, 0xc3, 0x74, - 0xbd, 0xc4, 0x7f, 0x4b, - 0x3d, 0x25, 0x29, 0x3a, - 0x30, 0x82, 0x4b, 0x01, - 0x33, 0x80, 0x22, 0x13, - }; - } - - - namespace t8 { - int internal_rounds = 13; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x56, 0x8b, 0x12, 0x03, - 0xfe, 0x3a, 0x7d, 0x4d, - 0xaa, 0x42, 0x09, 0x35, - 0xed, 0xc3, 0xa8, 0x2e, - 0x48, 0x0b, 0xe8, 0x5c, - 0x80, 0xfd, 0x5a, 0x76, - 0xfb, 0x06, 0x8f, 0x19, - 0x16, 0x2a, 0x1b, 0x7c, - }; - - unsigned char round_constants[] = { - 0x54, 0x07, 0x91, 0x12, - 0xbe, 0x49, 0xed, 0x36, - 0x97, 0x74, 0xc9, 0x07, - 0xed, 0x1f, 0xd7, 0x40, - 0x3c, 0x31, 0xee, 0x2d, - 0x70, 0x44, 0xde, 0x26, - 0xe2, 0x5a, 0x7a, 0x5d, - 0xff, 0x33, 0x1e, 0x1f, - 0x61, 0x0c, 0xc0, 0x13, - 0xf6, 0xe3, 0xc4, 0x0c, - 0xa0, 0x2b, 0xc3, 0x54, - 0x4a, 0xe5, 0x77, 0x4c, - 0x04, 0xcd, 0x51, 0x79, - 0xa2, 0xfa, 0x05, 0x4c, - 0xa0, 0x9d, 0x47, 0x6e, - 0x30, 0x77, 0x11, 0x38, - 0xce, 0xc4, 0x74, 0x73, - 0xea, 0x1a, 0x28, 0x50, - 0x55, 0x2e, 0x23, 0x48, - 0x1e, 0x9d, 0x53, 0x36, - 0xc1, 0x4a, 0x5a, 0x5e, - 0x08, 0x44, 0xea, 0x77, - 0x67, 0xa0, 0xef, 0x1c, - 0xef, 0x05, 0xce, 0x09, - 0x81, 0x4c, 0xd2, 0x26, - 0x15, 0x3b, 0x87, 0x4c, - 0x65, 0xea, 0xd2, 0x77, - 0xb3, 0x8b, 0xb2, 0x56, - 0x8c, 0x9f, 0x2b, 0x2b, - 0xf9, 0xe3, 0x57, 0x4a, - 0xa7, 0x45, 0xc4, 0x32, - 0xc7, 0x41, 0x2f, 0x78, - 0x91, 0x58, 0x20, 0x6c, - 0x5e, 0x3f, 0x50, 0x67, - 0x3f, 0x59, 0x6c, 0x75, - 0x9a, 0x47, 0x62, 0x04, - 0xca, 0xc9, 0x71, 0x42, - 0xce, 0xf3, 0x1a, 0x4d, - 0x99, 0x99, 0x0e, 0x18, - 0xdb, 0x28, 0x53, 0x02, - 0x99, 0x8c, 0x8b, 0x39, - 0x5c, 0x75, 0xf4, 0x03, - 0x5f, 0x4b, 0xc7, 0x68, - 0x81, 0x8c, 0x54, 0x5f, - 0x7f, 0x62, 0x7a, 0x73, - 0x3d, 0xee, 0x77, 0x61, - 0xc7, 0x67, 0x75, 0x48, - 0xf4, 0x94, 0x7f, 0x06, - 0x7d, 0x08, 0xd1, 0x2c, - 0x54, 0x90, 0x4d, 0x30, - 0xab, 0x2b, 0x0e, 0x49, - 0x18, 0x9e, 0x65, 0x5c, - 0xe3, 0xcc, 0x47, 0x7c, - 0xb5, 0x87, 0xa3, 0x61, - 0xf0, 0xde, 0x57, 0x38, - 0x7f, 0x46, 0x24, 0x26, - 0xc8, 0x3c, 0xf5, 0x7e, - 0x58, 0xd3, 0x2a, 0x1d, - 0xf2, 0xaf, 0x6c, 0x79, - 0xfd, 0xc4, 0xea, 0x10, - 0xa5, 0x54, 0x4b, 0x7f, - 0x00, 0x6d, 0x08, 0x35, - 0x5f, 0x00, 0xb5, 0x41, - 0x3e, 0x9f, 0xab, 0x49, - 0xfe, 0xf6, 0x0c, 0x7d, - 0x39, 0x48, 0x90, 0x13, - 0xcd, 0x28, 0xdc, 0x67, - 0x3b, 0x2a, 0x32, 0x28, - 0x95, 0x6b, 0x1d, 0x29, - 0x04, 0x63, 0x5e, 0x4b, - 0x3f, 0x79, 0x4b, 0x5e, - 0xd4, 0xe5, 0x3d, 0x1c, - 0x0e, 0x15, 0xcd, 0x52, - 0x5b, 0x05, 0x52, 0x78, - 0x2b, 0x12, 0x71, 0x3f, - 0x08, 0xa0, 0x8b, 0x40, - 0xd9, 0xde, 0xd7, 0x76, - }; - } - - - namespace t12 { - int internal_rounds = 12; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0x66, 0x0d, 0xa2, 0x4a, - 0x8c, 0x08, 0x67, 0x37, - 0x8a, 0xbd, 0x82, 0x5e, - 0x8e, 0x01, 0x33, 0x28, - 0x0f, 0x30, 0x42, 0x59, - 0x6c, 0x8a, 0x1b, 0x46, - 0x85, 0x3b, 0x64, 0x3c, - 0x5c, 0xc0, 0x08, 0x4c, - 0xce, 0x76, 0x9f, 0x56, - 0xb1, 0xdb, 0x28, 0x47, - 0xeb, 0x80, 0x8d, 0x75, - 0x3c, 0x82, 0xfa, 0x33, - }; - - unsigned char round_constants[] = { - 0xe0, 0xc0, 0xb6, 0x68, - 0xb5, 0x57, 0xc2, 0x4e, - 0x48, 0xf3, 0xd9, 0x50, - 0x53, 0xcb, 0x34, 0x22, - 0xee, 0xb9, 0xe4, 0x04, - 0xfe, 0x81, 0x59, 0x30, - 0xf9, 0xae, 0x52, 0x33, - 0xa2, 0x33, 0x3c, 0x1c, - 0x8a, 0xf5, 0xac, 0x7a, - 0x97, 0xa8, 0xcb, 0x06, - 0xc0, 0x6f, 0xf4, 0x7a, - 0x08, 0xeb, 0xe5, 0x51, - 0x20, 0x6e, 0x4c, 0x09, - 0x39, 0x0e, 0x46, 0x61, - 0xb7, 0x11, 0x32, 0x69, - 0xe2, 0xb2, 0xca, 0x18, - 0x0e, 0x3d, 0xab, 0x05, - 0x78, 0xb7, 0xc0, 0x61, - 0x71, 0x8d, 0xf6, 0x7f, - 0x5c, 0xb3, 0xed, 0x7e, - 0xc6, 0xa0, 0x1b, 0x06, - 0x70, 0x47, 0x63, 0x26, - 0xa4, 0x02, 0x01, 0x3f, - 0x98, 0xe2, 0xae, 0x4a, - 0xe8, 0xa9, 0xe7, 0x38, - 0x0f, 0x8c, 0xf9, 0x06, - 0xc1, 0x6e, 0x51, 0x75, - 0xc5, 0x57, 0x28, 0x69, - 0x5c, 0x2c, 0x91, 0x1d, - 0xff, 0x84, 0x6d, 0x05, - 0x61, 0x70, 0x2d, 0x2f, - 0xcf, 0x6a, 0xa1, 0x2d, - 0xf8, 0xa6, 0x2a, 0x09, - 0x6c, 0x57, 0xe7, 0x1a, - 0xb1, 0x68, 0xa8, 0x3b, - 0x1b, 0x9a, 0x19, 0x17, - 0x76, 0x0e, 0xad, 0x3c, - 0xe3, 0x37, 0xf2, 0x42, - 0xbc, 0x53, 0x04, 0x7d, - 0xca, 0xc5, 0x8d, 0x08, - 0xf9, 0x8c, 0x28, 0x07, - 0x28, 0x18, 0x1b, 0x5f, - 0xf1, 0xbe, 0x28, 0x44, - 0x7d, 0xdc, 0xce, 0x29, - 0x41, 0x63, 0xe6, 0x49, - 0xd1, 0xa1, 0x72, 0x20, - 0x3e, 0x95, 0x03, 0x68, - 0xd3, 0x35, 0xbc, 0x53, - 0xbd, 0xd9, 0x3d, 0x2b, - 0xe6, 0x95, 0xef, 0x7a, - 0xd0, 0x46, 0x5b, 0x0e, - 0x91, 0x2e, 0x82, 0x7d, - 0xcd, 0xff, 0xa3, 0x74, - 0xe1, 0x40, 0x13, 0x2d, - 0x9a, 0x27, 0x9f, 0x05, - 0x93, 0x7d, 0x90, 0x54, - 0xce, 0xdb, 0x46, 0x3b, - 0x54, 0xf5, 0xaa, 0x77, - 0x09, 0xea, 0xf5, 0x6c, - 0xd7, 0xee, 0x1d, 0x5f, - 0xe5, 0x2a, 0x46, 0x3c, - 0x1f, 0xbb, 0xbd, 0x14, - 0x18, 0xd8, 0x9d, 0x36, - 0xa6, 0xf8, 0x82, 0x0d, - 0xe0, 0x0d, 0xff, 0x1b, - 0x4a, 0xe9, 0xe6, 0x5d, - 0x5a, 0xd6, 0x48, 0x3d, - 0x84, 0x6d, 0xcf, 0x06, - 0x6a, 0xcc, 0x48, 0x7b, - 0xd3, 0x57, 0x7b, 0x44, - 0xc5, 0x78, 0xd5, 0x76, - 0xf7, 0xb1, 0xe6, 0x5b, - 0xa4, 0x1f, 0x9e, 0x53, - 0xc3, 0x78, 0xd2, 0x3b, - 0x1c, 0x1c, 0xbc, 0x09, - 0x20, 0x95, 0xb0, 0x54, - 0xfb, 0x78, 0xd5, 0x6d, - 0xa1, 0xc3, 0x17, 0x78, - 0xef, 0x90, 0x19, 0x12, - 0xa9, 0x0f, 0x46, 0x48, - 0x89, 0xa4, 0xf7, 0x10, - 0x30, 0xeb, 0xc1, 0x2d, - 0x5f, 0xd4, 0xae, 0x6b, - 0xeb, 0xac, 0x88, 0x1f, - 0xf5, 0x49, 0x67, 0x77, - 0xa0, 0xe0, 0xf0, 0x29, - 0x0f, 0x07, 0xaf, 0x39, - 0xa6, 0xad, 0xa0, 0x3b, - 0x7c, 0x03, 0x9d, 0x1c, - 0x8e, 0x53, 0xec, 0x67, - 0x42, 0x95, 0x26, 0x47, - 0x18, 0x37, 0x90, 0x31, - 0x7c, 0x99, 0x7c, 0x3f, - 0xdc, 0xee, 0xda, 0x1e, - 0xc8, 0x88, 0x65, 0x6e, - 0x0e, 0x16, 0xdc, 0x13, - 0xb3, 0x89, 0x65, 0x7a, - 0xe7, 0x2a, 0xa4, 0x0b, - 0x85, 0x73, 0x5c, 0x40, - 0x7f, 0x2e, 0xa5, 0x3f, - 0xd1, 0x72, 0xcb, 0x20, - 0xca, 0x5c, 0x93, 0x65, - 0xa0, 0xce, 0xb5, 0x6c, - 0x04, 0xdd, 0xfc, 0x37, - 0x1d, 0x83, 0xbb, 0x56, - 0x1f, 0xdd, 0xe6, 0x17, - 0x29, 0x87, 0x94, 0x2a, - 0x61, 0x5b, 0xae, 0x68, - }; - } - - - namespace t16 { - int internal_rounds = 14; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xc4, 0x0a, 0xb8, 0x07, - 0x33, 0xcb, 0xd9, 0x6b, - 0x9f, 0x3f, 0xee, 0x48, - 0x19, 0xdd, 0x63, 0x4f, - 0xb3, 0x46, 0xc5, 0x18, - 0x8b, 0x9e, 0xf8, 0x5a, - 0xe8, 0x3d, 0xf2, 0x4f, - 0xf6, 0xaa, 0x78, 0x4f, - 0xd4, 0xc6, 0xbd, 0x53, - 0x3e, 0x82, 0x59, 0x5c, - 0x72, 0x1c, 0x47, 0x2a, - 0x79, 0x5e, 0x97, 0x4c, - 0xd4, 0x64, 0xdc, 0x58, - 0x5d, 0x31, 0xe9, 0x06, - 0x86, 0x22, 0xf3, 0x2c, - 0x5d, 0x75, 0xb6, 0x2f, - }; - - unsigned char round_constants[] = { - 0x52, 0xab, 0x8b, 0x76, - 0x7d, 0xab, 0xe0, 0x70, - 0x8a, 0x6c, 0x26, 0x3d, - 0x45, 0x20, 0xa4, 0x6d, - 0x22, 0xef, 0x0f, 0x60, - 0x6b, 0xce, 0xda, 0x41, - 0xd4, 0xbd, 0xf9, 0x64, - 0xfe, 0xd4, 0x42, 0x5d, - 0x6d, 0x51, 0xb1, 0x76, - 0x17, 0xa7, 0xc9, 0x6f, - 0xb6, 0x4f, 0xac, 0x70, - 0xf6, 0x4e, 0x19, 0x00, - 0xe2, 0x44, 0xb6, 0x22, - 0xd5, 0x16, 0x79, 0x1f, - 0xe2, 0x1b, 0x58, 0x47, - 0x23, 0xa1, 0x10, 0x27, - 0x67, 0xe8, 0x84, 0x62, - 0xfe, 0x3a, 0x8d, 0x01, - 0xf3, 0x9e, 0xf9, 0x5d, - 0x7b, 0x46, 0x1e, 0x4c, - 0xbc, 0x6a, 0x6f, 0x56, - 0x27, 0xe4, 0x94, 0x29, - 0x42, 0x6d, 0x8a, 0x53, - 0xcf, 0xf2, 0x7b, 0x5d, - 0xab, 0x2d, 0xda, 0x7f, - 0xc4, 0x54, 0xd8, 0x0f, - 0xca, 0x2f, 0x92, 0x46, - 0xa1, 0x63, 0x77, 0x3d, - 0xca, 0x05, 0xfd, 0x19, - 0x43, 0xbb, 0x4b, 0x0a, - 0x51, 0x58, 0x07, 0x15, - 0x76, 0x3d, 0x90, 0x3d, - 0xf7, 0x0f, 0x29, 0x2d, - 0xa0, 0x9f, 0x80, 0x40, - 0xec, 0xc6, 0xda, 0x59, - 0xa2, 0x27, 0x79, 0x12, - 0xa0, 0x0e, 0xbf, 0x6b, - 0x0f, 0x14, 0x94, 0x02, - 0x76, 0x29, 0x74, 0x24, - 0x81, 0xc0, 0x84, 0x6e, - 0x4a, 0x4f, 0x48, 0x22, - 0x59, 0xae, 0x4c, 0x35, - 0xe1, 0xff, 0x53, 0x04, - 0xcc, 0xa3, 0x47, 0x3f, - 0x4e, 0x20, 0x88, 0x00, - 0x09, 0xe1, 0x66, 0x60, - 0x80, 0x4b, 0x7c, 0x3b, - 0x5d, 0x66, 0x55, 0x6b, - 0x97, 0xb8, 0xc4, 0x3b, - 0x78, 0xf3, 0x5b, 0x73, - 0x42, 0xaf, 0x8d, 0x50, - 0x2b, 0xfc, 0x84, 0x18, - 0x4c, 0xf2, 0x14, 0x72, - 0x0a, 0xbe, 0x98, 0x74, - 0x40, 0xe6, 0x60, 0x1a, - 0x28, 0xf9, 0x03, 0x33, - 0x76, 0x63, 0xb4, 0x29, - 0x68, 0xbb, 0x96, 0x5c, - 0xa5, 0x97, 0xd0, 0x65, - 0x9f, 0x8e, 0x35, 0x1d, - 0x17, 0x90, 0x9a, 0x4a, - 0x76, 0xcf, 0x24, 0x47, - 0x0f, 0xf7, 0x7a, 0x34, - 0x9a, 0xe5, 0x77, 0x1e, - 0xbf, 0xc4, 0x7e, 0x7f, - 0x6f, 0x92, 0x21, 0x04, - 0x69, 0xe6, 0x98, 0x51, - 0x48, 0x31, 0xdb, 0x34, - 0xfd, 0xba, 0x68, 0x43, - 0x7f, 0x5c, 0x68, 0x66, - 0x9a, 0x24, 0xd3, 0x78, - 0x81, 0x78, 0x18, 0x60, - 0x7a, 0xd6, 0xda, 0x76, - 0x37, 0xb4, 0x90, 0x06, - 0x11, 0x53, 0xa9, 0x1e, - 0x9a, 0x36, 0xe5, 0x40, - 0xfc, 0x03, 0xf1, 0x38, - 0x21, 0x6a, 0x22, 0x1d, - 0x13, 0x06, 0x09, 0x57, - 0x08, 0x21, 0xa4, 0x1f, - 0x50, 0xef, 0xbb, 0x17, - 0x1c, 0xe1, 0xf7, 0x1f, - 0xca, 0x24, 0x7b, 0x04, - 0x75, 0x02, 0x14, 0x4e, - 0xf5, 0x86, 0xa0, 0x4f, - 0x9c, 0x30, 0x9b, 0x07, - 0x47, 0xbd, 0x59, 0x11, - 0xe5, 0xe4, 0x37, 0x6d, - 0xce, 0x8d, 0x5d, 0x07, - 0xa0, 0x1c, 0x12, 0x12, - 0x40, 0x7c, 0x6a, 0x7f, - 0xba, 0x82, 0xe1, 0x68, - 0x1b, 0x20, 0x93, 0x54, - 0x0e, 0xa8, 0x44, 0x04, - 0xc6, 0xf4, 0x64, 0x00, - 0xe6, 0xab, 0x67, 0x64, - 0x62, 0x57, 0x97, 0x66, - 0x9b, 0x8f, 0xf6, 0x2a, - 0xbe, 0x33, 0x5b, 0x34, - 0x7f, 0xd4, 0x70, 0x1b, - 0x17, 0xb7, 0x3d, 0x05, - 0xcb, 0x89, 0x11, 0x38, - 0xf8, 0x15, 0xb9, 0x43, - 0x94, 0x36, 0xdf, 0x20, - 0x26, 0x9d, 0x45, 0x0f, - 0x7b, 0xe9, 0xa0, 0x77, - 0x39, 0xe7, 0x73, 0x2f, - 0xf9, 0xc2, 0x76, 0x18, - 0x9a, 0xe2, 0xa0, 0x65, - 0xbe, 0xef, 0xab, 0x4c, - 0x68, 0x12, 0xbd, 0x5a, - 0x60, 0xa7, 0x34, 0x4d, - 0x99, 0x17, 0x77, 0x12, - 0xac, 0xc9, 0xa0, 0x69, - 0x55, 0x1e, 0x09, 0x39, - 0xd0, 0x1c, 0x61, 0x7f, - 0xda, 0x55, 0xf0, 0x3a, - 0xdf, 0xbb, 0xc0, 0x7a, - 0x24, 0x3a, 0x0f, 0x6e, - 0xf7, 0xb6, 0xe3, 0x41, - 0x6d, 0x75, 0xb3, 0x49, - 0x38, 0xc5, 0x8b, 0x56, - 0xd8, 0x79, 0xc0, 0x20, - 0x2c, 0xc7, 0x01, 0x17, - 0x6c, 0xdc, 0x70, 0x76, - 0x35, 0x90, 0x43, 0x5a, - 0x0e, 0xe0, 0x93, 0x7c, - 0x4d, 0xbb, 0x1f, 0x56, - 0x7b, 0x90, 0x78, 0x11, - 0x06, 0x74, 0x73, 0x02, - 0xf1, 0x24, 0xfb, 0x32, - 0x0a, 0xb6, 0x23, 0x63, - 0x18, 0x24, 0xb1, 0x6a, - 0xea, 0x9c, 0xc9, 0x42, - 0x97, 0x0b, 0x5a, 0x15, - 0xaa, 0xc6, 0xd1, 0x53, - 0x47, 0x03, 0xd2, 0x2b, - 0x73, 0x3d, 0x9b, 0x27, - 0x70, 0x3c, 0x5f, 0x4f, - 0x6c, 0xaf, 0x45, 0x02, - 0xd3, 0x59, 0x83, 0x23, - 0x59, 0x6a, 0x96, 0x49, - }; - } - - - namespace t20 { - int internal_rounds = 18; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xff, 0xe7, 0xda, 0x01, - 0x2a, 0xf8, 0xf6, 0x7f, - 0xac, 0x31, 0xa9, 0x34, - 0x4b, 0x36, 0x15, 0x56, - 0x42, 0xd1, 0x21, 0x7a, - 0x6c, 0x89, 0x54, 0x5c, - 0x48, 0x68, 0x0b, 0x2d, - 0xb3, 0xad, 0xbc, 0x03, - 0x80, 0xa4, 0x99, 0x4a, - 0x6f, 0x96, 0x1d, 0x3d, - 0xcf, 0x2f, 0x31, 0x1f, - 0x25, 0xa4, 0xd5, 0x2f, - 0x6e, 0x53, 0x77, 0x50, - 0xbe, 0x34, 0x65, 0x6e, - 0x87, 0x13, 0x1e, 0x28, - 0x42, 0xb0, 0xd4, 0x23, - 0xb7, 0x6f, 0x19, 0x17, - 0xcd, 0xea, 0x92, 0x5e, - 0x08, 0x3c, 0xa2, 0x28, - 0x8c, 0x99, 0x8a, 0x2e, - }; - - unsigned char round_constants[] = { - 0x97, 0x44, 0xcd, 0x30, - 0x92, 0x7d, 0xcf, 0x67, - 0x86, 0xa7, 0x3c, 0x11, - 0xf7, 0x11, 0xf5, 0x28, - 0x11, 0x03, 0x85, 0x4c, - 0xe8, 0x7b, 0xc0, 0x3d, - 0xb9, 0x14, 0x82, 0x0c, - 0x97, 0x0d, 0x09, 0x18, - 0xfb, 0x35, 0x4f, 0x68, - 0x8e, 0x47, 0x4e, 0x7f, - 0x68, 0xba, 0xee, 0x11, - 0xa7, 0x0c, 0x7d, 0x56, - 0xbc, 0xbe, 0x3f, 0x6d, - 0x60, 0x84, 0x02, 0x47, - 0x33, 0xd4, 0xa8, 0x09, - 0x5c, 0x5d, 0xbc, 0x46, - 0xb2, 0x05, 0x08, 0x11, - 0x82, 0xdc, 0xdd, 0x50, - 0xfa, 0xe4, 0x1e, 0x00, - 0x19, 0x47, 0xed, 0x55, - 0xce, 0xbd, 0x08, 0x47, - 0x2b, 0x0b, 0x53, 0x1a, - 0x00, 0x24, 0x37, 0x6d, - 0x42, 0x1d, 0x10, 0x32, - 0xd0, 0xd7, 0xb0, 0x02, - 0x12, 0xf5, 0x64, 0x30, - 0x64, 0x3c, 0x0f, 0x67, - 0x31, 0x5e, 0x73, 0x35, - 0x44, 0x61, 0x2b, 0x5a, - 0xa6, 0x6b, 0x0d, 0x21, - 0x6a, 0xe6, 0x74, 0x12, - 0x09, 0x3e, 0x2e, 0x21, - 0xf0, 0x2f, 0xe6, 0x57, - 0x74, 0x18, 0x63, 0x6b, - 0x1b, 0x7a, 0x57, 0x6b, - 0xaa, 0xfd, 0x17, 0x0d, - 0x1b, 0xe8, 0xeb, 0x24, - 0xfe, 0xc9, 0xf6, 0x34, - 0x55, 0x5a, 0x1d, 0x59, - 0x42, 0xa9, 0x45, 0x5f, - 0x3f, 0x4f, 0x45, 0x5e, - 0x33, 0x56, 0xb7, 0x23, - 0x70, 0x61, 0x07, 0x55, - 0x82, 0x94, 0xaa, 0x3c, - 0x00, 0xbf, 0x2c, 0x6a, - 0x1b, 0x80, 0x24, 0x72, - 0x64, 0x2d, 0xbb, 0x74, - 0x90, 0x09, 0x6d, 0x2e, - 0xf5, 0x7b, 0x31, 0x78, - 0x21, 0xb9, 0xea, 0x23, - 0xa8, 0x03, 0x36, 0x66, - 0x7f, 0xe9, 0xe8, 0x03, - 0x3d, 0x5d, 0xdb, 0x04, - 0x27, 0xe8, 0xc1, 0x64, - 0xfe, 0x78, 0xc4, 0x37, - 0x05, 0xc8, 0xef, 0x35, - 0xf9, 0xae, 0x5a, 0x55, - 0x0c, 0xad, 0x63, 0x65, - 0xac, 0xae, 0xc9, 0x14, - 0x72, 0xe1, 0xd6, 0x16, - 0xe9, 0xcb, 0x4f, 0x42, - 0x55, 0x08, 0x38, 0x30, - 0xbf, 0xe5, 0xd4, 0x74, - 0x83, 0xba, 0x75, 0x12, - 0x2d, 0x2a, 0xf3, 0x56, - 0x89, 0x7f, 0xf9, 0x3c, - 0x11, 0x57, 0x0c, 0x44, - 0x8a, 0x7e, 0x68, 0x48, - 0xf8, 0x00, 0x66, 0x49, - 0x9f, 0x42, 0x0e, 0x0b, - 0x26, 0x1e, 0x4b, 0x15, - 0xce, 0xb8, 0x9b, 0x37, - 0xe5, 0x83, 0x76, 0x27, - 0x94, 0xe0, 0x03, 0x44, - 0x40, 0x32, 0x03, 0x12, - 0xd7, 0x06, 0xd0, 0x3b, - 0x1b, 0xee, 0xe6, 0x4e, - 0x4c, 0x76, 0xc6, 0x62, - 0x2f, 0x55, 0x30, 0x72, - 0xe7, 0x1d, 0x34, 0x4f, - 0x49, 0x8c, 0x8f, 0x3c, - 0xe2, 0xbe, 0xc6, 0x6f, - 0x4d, 0xf7, 0x3e, 0x0a, - 0x07, 0x01, 0x71, 0x23, - 0x82, 0x0c, 0x65, 0x24, - 0xd4, 0xeb, 0xba, 0x12, - 0xce, 0xf3, 0xc2, 0x46, - 0xd9, 0x6f, 0x81, 0x57, - 0x1c, 0xe5, 0x3f, 0x44, - 0xab, 0x05, 0x78, 0x2a, - 0xf1, 0x8e, 0x50, 0x3f, - 0x6c, 0x83, 0x0b, 0x15, - 0xfa, 0x4f, 0x01, 0x1c, - 0xbe, 0x86, 0x07, 0x1c, - 0x90, 0x51, 0x64, 0x10, - 0x0a, 0x9c, 0x7b, 0x60, - 0x05, 0x15, 0x96, 0x48, - 0xbb, 0xb7, 0x20, 0x6f, - 0x23, 0xea, 0x5a, 0x5b, - 0x5c, 0x35, 0xe5, 0x1f, - 0xaa, 0x98, 0xef, 0x27, - 0x0b, 0x12, 0x3e, 0x04, - 0xcd, 0x5c, 0x7d, 0x64, - 0xb2, 0x81, 0x5c, 0x45, - 0x3f, 0x7d, 0xc2, 0x6a, - 0x26, 0x61, 0x9f, 0x27, - 0x58, 0x57, 0xa1, 0x76, - 0xa6, 0xb0, 0xc1, 0x58, - 0x01, 0x39, 0xf8, 0x4a, - 0x4f, 0x1d, 0x64, 0x45, - 0x33, 0x8c, 0xfe, 0x1d, - 0x88, 0x0c, 0x72, 0x03, - 0x0d, 0xd4, 0x82, 0x34, - 0x60, 0x89, 0x0d, 0x77, - 0x30, 0xb7, 0x08, 0x61, - 0x84, 0xc0, 0x3f, 0x06, - 0x34, 0x2a, 0x6e, 0x09, - 0xa0, 0x14, 0x05, 0x13, - 0xff, 0x3e, 0xbc, 0x42, - 0x48, 0x58, 0x77, 0x43, - 0xe4, 0x60, 0x29, 0x70, - 0x79, 0x9c, 0xf1, 0x6c, - 0xdd, 0x30, 0x30, 0x4c, - 0x32, 0x79, 0xd2, 0x6d, - 0xe9, 0x0d, 0x35, 0x64, - 0x11, 0x83, 0x0e, 0x48, - 0x2c, 0x63, 0x22, 0x6a, - 0xf6, 0xbc, 0x14, 0x28, - 0xe3, 0x97, 0x71, 0x11, - 0x19, 0xd4, 0x44, 0x48, - 0xa6, 0x81, 0xe1, 0x76, - 0xb4, 0xd7, 0x55, 0x61, - 0x26, 0xed, 0xdb, 0x42, - 0x7c, 0xdb, 0xc7, 0x78, - 0x7b, 0xc2, 0xe9, 0x62, - 0x11, 0x4f, 0x5b, 0x10, - 0x58, 0x58, 0xd4, 0x51, - 0x98, 0x02, 0xfa, 0x33, - 0x28, 0x82, 0xa0, 0x26, - 0x1f, 0x58, 0x14, 0x5f, - 0x37, 0x97, 0xd8, 0x3b, - 0x8f, 0x96, 0x25, 0x23, - 0xba, 0x12, 0x85, 0x41, - 0x20, 0x69, 0x41, 0x7e, - 0x2f, 0x44, 0x47, 0x2f, - 0xf5, 0x0a, 0x68, 0x0e, - 0xba, 0x4c, 0x42, 0x0c, - 0xae, 0x85, 0x38, 0x6d, - 0xbe, 0x8c, 0x9c, 0x0a, - 0xa9, 0xf2, 0x85, 0x24, - 0x7f, 0x35, 0x99, 0x1f, - 0x31, 0xd5, 0x58, 0x0f, - 0xb1, 0xf6, 0xd6, 0x32, - 0x6d, 0x87, 0x26, 0x2c, - 0x8e, 0xd3, 0xe3, 0x2d, - 0xc9, 0x6c, 0x7b, 0x5a, - 0xf0, 0xc7, 0xae, 0x1c, - 0x75, 0xf5, 0xa1, 0x4a, - 0xcd, 0x0c, 0xaf, 0x77, - 0x5c, 0xbe, 0xd3, 0x19, - 0xd8, 0x19, 0xa6, 0x0a, - 0x78, 0xea, 0xcf, 0x04, - 0x76, 0xab, 0xb3, 0x0d, - 0xa3, 0xfa, 0xdf, 0x2d, - 0x67, 0x67, 0x24, 0x52, - 0xf8, 0x2d, 0x63, 0x78, - 0x41, 0x46, 0xd5, 0x7b, - 0x18, 0xf0, 0xd7, 0x50, - 0x9f, 0xeb, 0x8c, 0x31, - 0x06, 0xa5, 0x69, 0x2e, - 0xfa, 0xd1, 0xd9, 0x4b, - 0x9a, 0x57, 0x0b, 0x2a, - 0xc2, 0x38, 0x12, 0x56, - 0x9a, 0xbb, 0x77, 0x28, - 0x79, 0x2c, 0xed, 0x06, - 0xba, 0x98, 0x99, 0x22, - 0x85, 0x01, 0x7f, 0x73, - 0x31, 0xad, 0x9a, 0x60, - }; - } - - - namespace t24 { - int internal_rounds = 22; - - int alpha = 5; - - unsigned char mat_diag_m_1[] = { - 0xea, 0xec, 0xd7, 0x0c, - 0x1e, 0x07, 0x4a, 0x07, - 0xfe, 0xae, 0xaa, 0x66, - 0x65, 0x4b, 0x33, 0x1c, - 0xb0, 0x4c, 0x09, 0x4d, - 0xa9, 0x79, 0x85, 0x2f, - 0x19, 0x47, 0x09, 0x27, - 0xcd, 0x93, 0x00, 0x71, - 0xac, 0x35, 0xd4, 0x53, - 0x0f, 0xa1, 0x06, 0x73, - 0x05, 0x89, 0x56, 0x57, - 0xdf, 0xd8, 0x67, 0x49, - 0x7e, 0x62, 0x74, 0x7f, - 0x59, 0x14, 0x82, 0x06, - 0x24, 0x75, 0x8d, 0x47, - 0x28, 0xc2, 0xac, 0x1e, - 0x40, 0x11, 0x1f, 0x65, - 0xef, 0x97, 0xf3, 0x42, - 0xd6, 0x9a, 0xe4, 0x24, - 0xff, 0x56, 0xa3, 0x5c, - 0xe9, 0x00, 0xf3, 0x42, - 0x84, 0x10, 0xd0, 0x2f, - 0x83, 0x64, 0xa8, 0x28, - 0xb4, 0x02, 0x44, 0x10, - }; - - unsigned char round_constants[] = { - 0x61, 0xba, 0xea, 0x1f, - 0x54, 0x44, 0x22, 0x53, - 0xe2, 0xb9, 0xce, 0x6b, - 0xb4, 0xf9, 0x19, 0x50, - 0x92, 0x65, 0x72, 0x48, - 0xa8, 0xd0, 0x22, 0x2b, - 0xf9, 0xbb, 0x51, 0x61, - 0x21, 0x4b, 0x47, 0x2f, - 0x37, 0xf3, 0xb5, 0x2e, - 0x87, 0x5d, 0x64, 0x3b, - 0xf0, 0xce, 0x42, 0x09, - 0x52, 0x8c, 0x22, 0x65, - 0x0f, 0xb3, 0xff, 0x78, - 0xc8, 0x37, 0x28, 0x4d, - 0x4f, 0xac, 0x17, 0x0e, - 0x86, 0x66, 0x54, 0x05, - 0xcc, 0x06, 0x6c, 0x04, - 0xb6, 0xc3, 0x51, 0x0b, - 0x63, 0xb7, 0x8d, 0x56, - 0xe4, 0x34, 0xb3, 0x38, - 0xf0, 0xac, 0xf5, 0x57, - 0x11, 0x26, 0xd3, 0x19, - 0x4b, 0x2f, 0xd0, 0x77, - 0xb8, 0xe9, 0x82, 0x6c, - 0xb6, 0xc1, 0x48, 0x71, - 0x75, 0x7c, 0x06, 0x08, - 0xc9, 0xe8, 0xd1, 0x46, - 0x07, 0x3b, 0x97, 0x30, - 0x3b, 0x4f, 0x61, 0x20, - 0x51, 0xf8, 0x3f, 0x5c, - 0x29, 0x33, 0x50, 0x30, - 0xcc, 0xe7, 0x72, 0x49, - 0xbc, 0xd8, 0xd1, 0x02, - 0xa6, 0xbf, 0xd5, 0x09, - 0xc0, 0x04, 0x71, 0x09, - 0x34, 0x9a, 0xa4, 0x7b, - 0xfc, 0xc2, 0x07, 0x4a, - 0x69, 0xee, 0xc1, 0x24, - 0x41, 0xab, 0xa6, 0x28, - 0xa0, 0x08, 0x91, 0x5d, - 0xc7, 0x51, 0x78, 0x3a, - 0xf9, 0x95, 0xd4, 0x1d, - 0xf4, 0x9f, 0xb4, 0x12, - 0x60, 0x57, 0xad, 0x7b, - 0xc2, 0x64, 0xed, 0x5f, - 0x6c, 0xc9, 0xf5, 0x66, - 0x02, 0xbd, 0xaf, 0x7e, - 0x3b, 0x59, 0xb3, 0x39, - 0x49, 0x3b, 0x65, 0x4a, - 0xc1, 0x1d, 0x09, 0x75, - 0xe0, 0x88, 0xe4, 0x56, - 0x55, 0xa3, 0x04, 0x17, - 0xf3, 0x4f, 0x5e, 0x74, - 0x6e, 0xf1, 0x2e, 0x39, - 0xdf, 0x3f, 0xe3, 0x31, - 0x66, 0x8c, 0xc2, 0x02, - 0x3a, 0x08, 0xc3, 0x36, - 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0x18, 0x6a, 0x5a, 0x46, - }; - } - -} -#endif diff --git a/icicle/include/poseidon2/constants/poseidon2_rust_params.sage b/icicle/include/poseidon2/constants/poseidon2_rust_params.sage deleted file mode 100644 index 1a8618616..000000000 --- a/icicle/include/poseidon2/constants/poseidon2_rust_params.sage +++ /dev/null @@ -1,708 +0,0 @@ -# Remark: This script contains functionality for GF(2^n), but currently works only over GF(p)! A few small adaptations are needed for GF(2^n). -from sage.rings.polynomial.polynomial_gf2x import GF2X_BuildIrred_list -from math import * -import itertools - -CURVE_NAME = "m31" - -########################################################################### -# p = 18446744069414584321 # GoldiLocks -# p = 2013265921 # BabyBear -p = 2**31 - 1 # M31 -# p = 52435875175126190479447740508185965837690552500527637822603658699938581184513 # BLS12-381 -# p = 21888242871839275222246405745257275088548364400416034343698204186575808495617 # BN254/BN256 -# p = 28948022309329048855892746252171976963363056481941560715954676764349967630337 # Pasta (Pallas) -# p = 28948022309329048855892746252171976963363056481941647379679742748393362948097 # Pasta (Vesta) - -n = len(p.bits()) # bit -# t = 12 # GoldiLocks (t = 12 for sponge, t = 8 for compression) -# t = 16 # BabyBear (t = 24 for sponge, t = 16 for compression) -# t = 3 # BN254/BN256, BLS12-381, Pallas, Vesta (t = 3 for sponge, t = 2 for compression) - -TS = [2, 3, 4, 8, 12, 16, 20, 24] - -FIELD = 1 -SBOX = 0 -FIELD_SIZE = n - -def get_alpha(p): - for alpha in range(3, p): - if gcd(alpha, p-1) == 1: - break - return alpha - -alpha = get_alpha(p) - -def get_sbox_cost(R_F, R_P, N, t): - return int(t * R_F + R_P) - -def get_size_cost(R_F, R_P, N, t): - n = ceil(float(N) / t) - return int((N * R_F) + (n * R_P)) - -def poseidon_calc_final_numbers_fixed(p, t, alpha, M, security_margin): - # [Min. S-boxes] Find best possible for t and N - n = ceil(log(p, 2)) - N = int(n * t) - cost_function = get_sbox_cost - ret_list = [] - (R_F, R_P) = find_FD_round_numbers(p, t, alpha, M, cost_function, security_margin) - min_sbox_cost = cost_function(R_F, R_P, N, t) - ret_list.append(R_F) - ret_list.append(R_P) - ret_list.append(min_sbox_cost) - - # [Min. Size] Find best possible for t and N - # Minimum number of S-boxes for fixed n results in minimum size also (round numbers are the same)! - min_size_cost = get_size_cost(R_F, R_P, N, t) - ret_list.append(min_size_cost) - - return ret_list # [R_F, R_P, min_sbox_cost, min_size_cost] - -def find_FD_round_numbers(p, t, alpha, M, cost_function, security_margin): - n = ceil(log(p, 2)) - N = int(n * t) - - sat_inequiv = sat_inequiv_alpha - - R_P = 0 - R_F = 0 - min_cost = float("inf") - max_cost_rf = 0 - # Brute-force approach - for R_P_t in range(1, 500): - for R_F_t in range(4, 100): - if R_F_t % 2 == 0: - if (sat_inequiv(p, t, R_F_t, R_P_t, alpha, M) == True): - if security_margin == True: - R_F_t += 2 - R_P_t = int(ceil(float(R_P_t) * 1.075)) - cost = cost_function(R_F_t, R_P_t, N, t) - if (cost < min_cost) or ((cost == min_cost) and (R_F_t < max_cost_rf)): - R_P = ceil(R_P_t) - R_F = ceil(R_F_t) - min_cost = cost - max_cost_rf = R_F - return (int(R_F), int(R_P)) - -def sat_inequiv_alpha(p, t, R_F, R_P, alpha, M): - N = int(FIELD_SIZE * NUM_CELLS) - - if alpha > 0: - R_F_1 = 6 if M <= ((floor(log(p, 2) - ((alpha-1)/2.0))) * (t + 1)) else 10 # Statistical - R_F_2 = 1 + ceil(log(2, alpha) * min(M, FIELD_SIZE)) + ceil(log(t, alpha)) - R_P # Interpolation - R_F_3 = (log(2, alpha) * min(M, log(p, 2))) - R_P # Groebner 1 - R_F_4 = t - 1 + log(2, alpha) * min(M / float(t + 1), log(p, 2) / float(2)) - R_P # Groebner 2 - R_F_5 = (t - 2 + (M / float(2 * log(alpha, 2))) - R_P) / float(t - 1) # Groebner 3 - R_F_max = max(ceil(R_F_1), ceil(R_F_2), ceil(R_F_3), ceil(R_F_4), ceil(R_F_5)) - - # Addition due to https://eprint.iacr.org/2023/537.pdf - r_temp = floor(t / 3.0) - over = (R_F - 1) * t + R_P + r_temp + r_temp * (R_F / 2.0) + R_P + alpha - under = r_temp * (R_F / 2.0) + R_P + alpha - binom_log = log(binomial(over, under), 2) - if binom_log == inf: - binom_log = M + 1 - cost_gb4 = ceil(2 * binom_log) # Paper uses 2.3727, we are more conservative here - - return ((R_F >= R_F_max) and (cost_gb4 >= M)) - else: - print("Invalid value for alpha!") - exit(1) - -def grain_sr_generator(): - bit_sequence = INIT_SEQUENCE - for _ in range(0, 160): - new_bit = bit_sequence[62] ^^ bit_sequence[51] ^^ bit_sequence[38] ^^ bit_sequence[23] ^^ bit_sequence[13] ^^ bit_sequence[0] - bit_sequence.pop(0) - bit_sequence.append(new_bit) - - while True: - new_bit = bit_sequence[62] ^^ bit_sequence[51] ^^ bit_sequence[38] ^^ bit_sequence[23] ^^ bit_sequence[13] ^^ bit_sequence[0] - bit_sequence.pop(0) - bit_sequence.append(new_bit) - while new_bit == 0: - new_bit = bit_sequence[62] ^^ bit_sequence[51] ^^ bit_sequence[38] ^^ bit_sequence[23] ^^ bit_sequence[13] ^^ bit_sequence[0] - bit_sequence.pop(0) - bit_sequence.append(new_bit) - new_bit = bit_sequence[62] ^^ bit_sequence[51] ^^ bit_sequence[38] ^^ bit_sequence[23] ^^ bit_sequence[13] ^^ bit_sequence[0] - bit_sequence.pop(0) - bit_sequence.append(new_bit) - new_bit = bit_sequence[62] ^^ bit_sequence[51] ^^ bit_sequence[38] ^^ bit_sequence[23] ^^ bit_sequence[13] ^^ bit_sequence[0] - bit_sequence.pop(0) - bit_sequence.append(new_bit) - yield new_bit - -def grain_random_bits(num_bits): - random_bits = [next(grain_gen) for i in range(0, num_bits)] - # random_bits.reverse() ## Remove comment to start from least significant bit - random_int = int("".join(str(i) for i in random_bits), 2) - return random_int - -def init_generator(field, sbox, n, t, R_F, R_P): - # Generate initial sequence based on parameters - bit_list_field = [_ for _ in (bin(FIELD)[2:].zfill(2))] - bit_list_sbox = [_ for _ in (bin(SBOX)[2:].zfill(4))] - bit_list_n = [_ for _ in (bin(FIELD_SIZE)[2:].zfill(12))] - bit_list_t = [_ for _ in (bin(NUM_CELLS)[2:].zfill(12))] - bit_list_R_F = [_ for _ in (bin(R_F)[2:].zfill(10))] - bit_list_R_P = [_ for _ in (bin(R_P)[2:].zfill(10))] - bit_list_1 = [1] * 30 - global INIT_SEQUENCE - INIT_SEQUENCE = bit_list_field + bit_list_sbox + bit_list_n + bit_list_t + bit_list_R_F + bit_list_R_P + bit_list_1 - INIT_SEQUENCE = [int(_) for _ in INIT_SEQUENCE] - -def generate_constants(field, n, t, R_F, R_P, prime_number): - round_constants = [] - # num_constants = (R_F + R_P) * t # Poseidon - num_constants = (R_F * t) + R_P # Poseidon2 - - if field == 0: - for i in range(0, num_constants): - random_int = grain_random_bits(n) - round_constants.append(random_int) - elif field == 1: - for i in range(0, num_constants): - random_int = grain_random_bits(n) - while random_int >= prime_number: - # print("[Info] Round constant is not in prime field! Taking next one.") - random_int = grain_random_bits(n) - round_constants.append(random_int) - # Add (t-1) zeroes for Poseidon2 if partial round - if i >= ((R_F/2) * t) and i < (((R_F/2) * t) + R_P): - round_constants.extend([0] * (t-1)) - return round_constants - -def print_round_constants(round_constants, n, field): - print("Number of round constants:", len(round_constants)) - - if field == 0: - print("Round constants for GF(2^n):") - elif field == 1: - print("Round constants for GF(p):") - hex_length = int(ceil(float(n) / 4)) + 2 # +2 for "0x" - print(["{0:#0{1}x}".format(entry, hex_length) for entry in round_constants]) - -def create_mds_p(n, t): - M = matrix(F, t, t) - - # Sample random distinct indices and assign to xs and ys - while True: - flag = True - rand_list = [F(grain_random_bits(n)) for _ in range(0, 2*t)] - while len(rand_list) != len(set(rand_list)): # Check for duplicates - rand_list = [F(grain_random_bits(n)) for _ in range(0, 2*t)] - xs = rand_list[:t] - ys = rand_list[t:] - # xs = [F(ele) for ele in range(0, t)] - # ys = [F(ele) for ele in range(t, 2*t)] - for i in range(0, t): - for j in range(0, t): - if (flag == False) or ((xs[i] + ys[j]) == 0): - flag = False - else: - entry = (xs[i] + ys[j])^(-1) - M[i, j] = entry - if flag == False: - continue - return M - -def generate_vectorspace(round_num, M, M_round, NUM_CELLS): - t = NUM_CELLS - s = 1 - V = VectorSpace(F, t) - if round_num == 0: - return V - elif round_num == 1: - return V.subspace(V.basis()[s:]) - else: - mat_temp = matrix(F) - for i in range(0, round_num-1): - add_rows = [] - for j in range(0, s): - add_rows.append(M_round[i].rows()[j][s:]) - mat_temp = matrix(mat_temp.rows() + add_rows) - r_k = mat_temp.right_kernel() - extended_basis_vectors = [] - for vec in r_k.basis(): - extended_basis_vectors.append(vector([0]*s + list(vec))) - S = V.subspace(extended_basis_vectors) - - return S - -def subspace_times_matrix(subspace, M, NUM_CELLS): - t = NUM_CELLS - V = VectorSpace(F, t) - subspace_basis = subspace.basis() - new_basis = [] - for vec in subspace_basis: - new_basis.append(M * vec) - new_subspace = V.subspace(new_basis) - return new_subspace - -# Returns True if the matrix is considered secure, False otherwise -def algorithm_1(M, NUM_CELLS): - t = NUM_CELLS - s = 1 - r = floor((t - s) / float(s)) - - # Generate round matrices - M_round = [] - for j in range(0, t+1): - M_round.append(M^(j+1)) - - for i in range(1, r+1): - mat_test = M^i - entry = mat_test[0, 0] - mat_target = matrix.circulant(vector([entry] + ([F(0)] * (t-1)))) - - if (mat_test - mat_target) == matrix.circulant(vector([F(0)] * (t))): - return [False, 1] - - S = generate_vectorspace(i, M, M_round, t) - V = VectorSpace(F, t) - - basis_vectors= [] - for eigenspace in mat_test.eigenspaces_right(format='galois'): - if (eigenspace[0] not in F): - continue - vector_subspace = eigenspace[1] - intersection = S.intersection(vector_subspace) - basis_vectors += intersection.basis() - IS = V.subspace(basis_vectors) - - if IS.dimension() >= 1 and IS != V: - return [False, 2] - for j in range(1, i+1): - S_mat_mul = subspace_times_matrix(S, M^j, t) - if S == S_mat_mul: - print("S.basis():\n", S.basis()) - return [False, 3] - - return [True, 0] - -# Returns True if the matrix is considered secure, False otherwise -def algorithm_2(M, NUM_CELLS): - t = NUM_CELLS - s = 1 - - V = VectorSpace(F, t) - trail = [None, None] - test_next = False - I = range(0, s) - I_powerset = list(sage.misc.misc.powerset(I))[1:] - for I_s in I_powerset: - test_next = False - new_basis = [] - for l in I_s: - new_basis.append(V.basis()[l]) - IS = V.subspace(new_basis) - for i in range(s, t): - new_basis.append(V.basis()[i]) - full_iota_space = V.subspace(new_basis) - for l in I_s: - v = V.basis()[l] - while True: - delta = IS.dimension() - v = M * v - IS = V.subspace(IS.basis() + [v]) - if IS.dimension() == t or IS.intersection(full_iota_space) != IS: - test_next = True - break - if IS.dimension() <= delta: - break - if test_next == True: - break - if test_next == True: - continue - return [False, [IS, I_s]] - - return [True, None] - -# Returns True if the matrix is considered secure, False otherwise -def algorithm_3(M, NUM_CELLS): - t = NUM_CELLS - s = 1 - - V = VectorSpace(F, t) - - l = 4*t - for r in range(2, l+1): - next_r = False - res_alg_2 = algorithm_2(M^r, t) - if res_alg_2[0] == False: - return [False, None] - - # if res_alg_2[1] == None: - # continue - # IS = res_alg_2[1][0] - # I_s = res_alg_2[1][1] - # for j in range(1, r): - # IS = subspace_times_matrix(IS, M, t) - # I_j = [] - # for i in range(0, s): - # new_basis = [] - # for k in range(0, t): - # if k != i: - # new_basis.append(V.basis()[k]) - # iota_space = V.subspace(new_basis) - # if IS.intersection(iota_space) != iota_space: - # single_iota_space = V.subspace([V.basis()[i]]) - # if IS.intersection(single_iota_space) == single_iota_space: - # I_j.append(i) - # else: - # next_r = True - # break - # if next_r == True: - # break - # if next_r == True: - # continue - # return [False, [IS, I_j, r]] - - return [True, None] - -def check_minpoly_condition(M, NUM_CELLS): - max_period = 2*NUM_CELLS - all_fulfilled = True - M_temp = M - for i in range(1, max_period + 1): - if not ((M_temp.minimal_polynomial().degree() == NUM_CELLS) and (M_temp.minimal_polynomial().is_irreducible() == True)): - all_fulfilled = False - break - M_temp = M * M_temp - return all_fulfilled - -def generate_matrix(FIELD, FIELD_SIZE, NUM_CELLS): - if FIELD == 0: - print("Matrix generation not implemented for GF(2^n).") - exit(1) - elif FIELD == 1: - mds_matrix = create_mds_p(FIELD_SIZE, NUM_CELLS) - result_1 = algorithm_1(mds_matrix, NUM_CELLS) - result_2 = algorithm_2(mds_matrix, NUM_CELLS) - result_3 = algorithm_3(mds_matrix, NUM_CELLS) - while result_1[0] == False or result_2[0] == False or result_3[0] == False: - mds_matrix = create_mds_p(FIELD_SIZE, NUM_CELLS) - result_1 = algorithm_1(mds_matrix, NUM_CELLS) - result_2 = algorithm_2(mds_matrix, NUM_CELLS) - result_3 = algorithm_3(mds_matrix, NUM_CELLS) - return mds_matrix - -def generate_matrix_full(NUM_CELLS): - M = None - if t == 2: - M = matrix.circulant(vector([F(2), F(1)])) - elif t == 3: - M = matrix.circulant(vector([F(2), F(1), F(1)])) - elif t == 4: - M = matrix(F, [[F(5), F(7), F(1), F(3)], [F(4), F(6), F(1), F(1)], [F(1), F(3), F(5), F(7)], [F(1), F(1), F(4), F(6)]]) - elif (t % 4) == 0: - M = matrix(F, t, t) - # M_small = matrix.circulant(vector([F(3), F(2), F(1), F(1)])) - M_small = matrix(F, [[F(5), F(7), F(1), F(3)], [F(4), F(6), F(1), F(1)], [F(1), F(3), F(5), F(7)], [F(1), F(1), F(4), F(6)]]) - small_num = t // 4 - for i in range(0, small_num): - for j in range(0, small_num): - if i == j: - M[i*4:(i+1)*4,j*4:(j+1)*4] = 2* M_small - else: - M[i*4:(i+1)*4,j*4:(j+1)*4] = M_small - else: - print("Error: No matrix for these parameters.") - exit() - return M - -def generate_matrix_partial(FIELD, FIELD_SIZE, NUM_CELLS): ## TODO: Prioritize small entries - entry_max_bit_size = FIELD_SIZE - if FIELD == 0: - print("Matrix generation not implemented for GF(2^n).") - exit(1) - elif FIELD == 1: - M = None - if t == 2: - M = matrix(F, [[F(2), F(1)], [F(1), F(3)]]) - elif t == 3: - M = matrix(F, [[F(2), F(1), F(1)], [F(1), F(2), F(1)], [F(1), F(1), F(3)]]) - else: - M_circulant = matrix.circulant(vector([F(0)] + [F(1) for _ in range(0, NUM_CELLS - 1)])) - M_diagonal = matrix.diagonal([F(grain_random_bits(entry_max_bit_size)) for _ in range(0, NUM_CELLS)]) - M = M_circulant + M_diagonal - # while algorithm_1(M, NUM_CELLS)[0] == False or algorithm_2(M, NUM_CELLS)[0] == False or algorithm_3(M, NUM_CELLS)[0] == False: - while check_minpoly_condition(M, NUM_CELLS) == False: - M_diagonal = matrix.diagonal([F(grain_random_bits(entry_max_bit_size)) for _ in range(0, NUM_CELLS)]) - M = M_circulant + M_diagonal - - if(algorithm_1(M, NUM_CELLS)[0] == False or algorithm_2(M, NUM_CELLS)[0] == False or algorithm_3(M, NUM_CELLS)[0] == False): - print("Error: Generated partial matrix is not secure w.r.t. subspace trails.") - exit() - return M - -def generate_matrix_partial_small_entries(FIELD, FIELD_SIZE, NUM_CELLS): - if FIELD == 0: - print("Matrix generation not implemented for GF(2^n).") - exit(1) - elif FIELD == 1: - M_circulant = matrix.circulant(vector([F(0)] + [F(1) for _ in range(0, NUM_CELLS - 1)])) - combinations = list(itertools.product(range(2, 6), repeat=NUM_CELLS)) - for entry in combinations: - M = M_circulant + matrix.diagonal(vector(F, list(entry))) - print(M) - # if M.is_invertible() == False or algorithm_1(M, NUM_CELLS)[0] == False or algorithm_2(M, NUM_CELLS)[0] == False or algorithm_3(M, NUM_CELLS)[0] == False: - if M.is_invertible() == False or check_minpoly_condition(M, NUM_CELLS) == False: - continue - return M - -def matrix_partial_m_1(matrix_partial, NUM_CELLS): - M_circulant = matrix.identity(F, NUM_CELLS) - return matrix_partial - M_circulant - -def print_linear_layer(M, n, t): - print("n:", n) - print("t:", t) - print("N:", (n * t)) - print("Result Algorithm 1:\n", algorithm_1(M, NUM_CELLS)) - print("Result Algorithm 2:\n", algorithm_2(M, NUM_CELLS)) - print("Result Algorithm 3:\n", algorithm_3(M, NUM_CELLS)) - hex_length = int(ceil(float(n) / 4)) + 2 # +2 for "0x" - print("Prime number:", "0x" + hex(PRIME_NUMBER)) - matrix_string = "[" - for i in range(0, t): - matrix_string += str(["{0:#0{1}x}".format(int(entry), hex_length) for entry in M[i]]) - if i < (t-1): - matrix_string += "," - matrix_string += "]" - print("MDS matrix:\n", matrix_string) - -def calc_equivalent_matrices(MDS_matrix_field): - # Following idea: Split M into M' * M'', where M'' is "cheap" and M' can move before the partial nonlinear layer - # The "previous" matrix layer is then M * M'. Due to the construction of M', the M[0,0] and v values will be the same for the new M' (and I also, obviously) - # Thus: Compute the matrices, store the w_hat and v_hat values - - MDS_matrix_field_transpose = MDS_matrix_field.transpose() - - w_hat_collection = [] - v_collection = [] - v = MDS_matrix_field_transpose[[0], list(range(1,t))] - - M_mul = MDS_matrix_field_transpose - M_i = matrix(F, t, t) - for i in range(R_P_FIXED - 1, -1, -1): - M_hat = M_mul[list(range(1,t)), list(range(1,t))] - w = M_mul[list(range(1,t)), [0]] - v = M_mul[[0], list(range(1,t))] - v_collection.append(v.list()) - w_hat = M_hat.inverse() * w - w_hat_collection.append(w_hat.list()) - - # Generate new M_i, and multiplication M * M_i for "previous" round - M_i = matrix.identity(t) - M_i[list(range(1,t)), list(range(1,t))] = M_hat - M_mul = MDS_matrix_field_transpose * M_i - - return M_i, v_collection, w_hat_collection, MDS_matrix_field_transpose[0, 0] - -def calc_equivalent_constants(constants, MDS_matrix_field): - constants_temp = [constants[index:index+t] for index in range(0, len(constants), t)] - - MDS_matrix_field_transpose = MDS_matrix_field.transpose() - - # Start moving round constants up - # Calculate c_i' = M^(-1) * c_(i+1) - # Split c_i': Add c_i'[0] AFTER the S-box, add the rest to c_i - # I.e.: Store c_i'[0] for each of the partial rounds, and make c_i = c_i + c_i' (where now c_i'[0] = 0) - num_rounds = R_F_FIXED + R_P_FIXED - R_f = R_F_FIXED / 2 - for i in range(num_rounds - 2 - R_f, R_f - 1, -1): - inv_cip1 = list(vector(constants_temp[i+1]) * MDS_matrix_field_transpose.inverse()) - constants_temp[i] = list(vector(constants_temp[i]) + vector([0] + inv_cip1[1:])) - constants_temp[i+1] = [inv_cip1[0]] + [0] * (t-1) - - return constants_temp - -def poseidon(input_words, matrix, round_constants): - - R_f = int(R_F_FIXED / 2) - - round_constants_counter = 0 - - state_words = list(input_words) - - # First full rounds - for r in range(0, R_f): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - for i in range(0, t): - state_words[i] = (state_words[i])^alpha - state_words = list(matrix * vector(state_words)) - - # Middle partial rounds - for r in range(0, R_P_FIXED): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - state_words[0] = (state_words[0])^alpha - state_words = list(matrix * vector(state_words)) - - # Last full rounds - for r in range(0, R_f): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - for i in range(0, t): - state_words[i] = (state_words[i])^alpha - state_words = list(matrix * vector(state_words)) - - return state_words - -def poseidon2(input_words, matrix_full, matrix_partial, round_constants): - - R_f = int(R_F_FIXED / 2) - - round_constants_counter = 0 - - state_words = list(input_words) - - # First matrix mul - state_words = list(matrix_full * vector(state_words)) - - # First full rounds - for r in range(0, R_f): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - for i in range(0, t): - state_words[i] = (state_words[i])^alpha - state_words = list(matrix_full * vector(state_words)) - - # Middle partial rounds - for r in range(0, R_P_FIXED): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - state_words[0] = (state_words[0])^alpha - state_words = list(matrix_partial * vector(state_words)) - - # Last full rounds - for r in range(0, R_f): - # Round constants, nonlinear layer, matrix multiplication - for i in range(0, t): - state_words[i] = state_words[i] + round_constants[round_constants_counter] - round_constants_counter += 1 - for i in range(0, t): - state_words[i] = (state_words[i])^alpha - state_words = list(matrix_full * vector(state_words)) - - return state_words - -def to_bytes(value, indent): - l = len(hex(p - 1)) - if l % 2 == 1: - l = l + 1 - value = hex(int(value))[2:] - value = value.zfill(l - 2) - value_bytes = reversed(["0x" + value[i:i + 2] for i in range(0, len(value), 2)]) - print(" " * indent + ", ".join(value_bytes) + ",") - -print("#pragma once") -print(f"#ifndef {CURVE_NAME.upper()}_POSEIDON2_H") -print(f"#define {CURVE_NAME.upper()}_POSEIDON2_H") -print() -print(f"namespace poseidon2_constants_{CURVE_NAME} {{") - -for t in TS: - NUM_CELLS = t - R_F_FIXED, R_P_FIXED, _, _ = poseidon_calc_final_numbers_fixed(p, t, alpha, 128, True) - if t == 16: - R_P_FIXED = 14 - - INIT_SEQUENCE = [] - - PRIME_NUMBER = p - - F = GF(PRIME_NUMBER) - - grain_gen = grain_sr_generator() - - # Init - init_generator(FIELD, SBOX, FIELD_SIZE, NUM_CELLS, R_F_FIXED, R_P_FIXED) - - # Round constants - round_constants = generate_constants(FIELD, FIELD_SIZE, NUM_CELLS, R_F_FIXED, R_P_FIXED, PRIME_NUMBER) - # print_round_constants(round_constants, FIELD_SIZE, FIELD) - - # Matrix - # MDS = generate_matrix(FIELD, FIELD_SIZE, NUM_CELLS) - MATRIX_FULL = generate_matrix_full(NUM_CELLS) - MATRIX_PARTIAL = generate_matrix_partial(FIELD, FIELD_SIZE, NUM_CELLS) - MATRIX_PARTIAL_DIAGONAL_M_1 = [matrix_partial_m_1(MATRIX_PARTIAL, NUM_CELLS)[i,i] for i in range(0, NUM_CELLS)] - - print() - print(f" namespace t{t} {{") - print(f" int internal_rounds = {R_P_FIXED};") - print() - print(f" int alpha = {alpha};") - print() - - # # MDS - # print("pub static ref MDS{}: Vec> = vec![".format(t)) - # for vec in MDS: - # print("vec![", end="") - # for val in vec: - # to_hex(val) - # print("],") - # print("];") - # print() - - # Efficient partial matrix (diagonal - 1) - print(" unsigned char mat_diag_m_1[] = {") - for val in MATRIX_PARTIAL_DIAGONAL_M_1: - to_bytes(val, 6) - print(" };") - print() - - # # Efficient partial matrix (full) - # print(" unsigned char mat_internal[] = {") - # for vec in MATRIX_PARTIAL: - # for val in vec: - # to_bytes(val, 6) - # print() - # print(" };") - # print() - - # Round constants - print(" unsigned char round_constants[] = {") - for (i,val) in enumerate(round_constants): - if (i % t == 0 or (i < (R_F_FIXED / 2) * t) or (i > (R_F_FIXED / 2 + R_P_FIXED) * t)): - to_bytes(val, 6) - print(" };") - print(" }") - print() - - # state_in = vector([F(i) for i in range(t)]) - # # state_out = poseidon(state_in, MDS, round_constants) - # state_out = poseidon2(state_in, MATRIX_FULL, MATRIX_PARTIAL, round_constants) - - # for (i,val) in enumerate(state_in): - # if i % t == 0: - # print("vec![", end="") - # to_bytes(val) - # if i % t == t - 1: - # print("],") - # print("];") - - # for (i,val) in enumerate(state_out): - # if i % t == 0: - # print("vec![", end="") - # to_bytes(val) - # if i % t == t - 1: - # print("],") - # print("];") - -print("}") -print("#endif") diff --git a/icicle/include/poseidon2/kernels.cuh b/icicle/include/poseidon2/kernels.cuh deleted file mode 100644 index 5f34be922..000000000 --- a/icicle/include/poseidon2/kernels.cuh +++ /dev/null @@ -1,367 +0,0 @@ -#pragma once -#ifndef POSEIDON2_KERNELS_H -#define POSEIDON2_KERNELS_H - -#include "utils/utils.h" -#include "hash/hash.cuh" -#include "matrix/matrix.cuh" -#include "poseidon2/constants.cuh" -#include "gpu-utils/modifiers.cuh" - -using matrix::Matrix; - -namespace poseidon2 { - static DEVICE_INLINE unsigned int d_next_pow_of_two(unsigned int v) - { - v--; - v |= v >> 1; - v |= v >> 2; - v |= v >> 4; - v |= v >> 8; - v |= v >> 16; - v++; - return v; - } - - template - DEVICE_INLINE S sbox_el(S element, const int alpha) - { - S result2 = S::sqr(element); - switch (alpha) { - case 3: - return result2 * element; - case 5: - return S::sqr(result2) * element; - case 7: - return S::sqr(result2) * result2 * element; - case 11: - return S::sqr(S::sqr(result2)) * result2 * element; - } - } - - template - DEVICE_INLINE void sbox(S state[T], const int alpha) - { - for (int i = 0; i < T; i++) { - state[i] = sbox_el(state[i], alpha); - } - } - - template - DEVICE_INLINE void add_rc(S state[T], size_t rc_offset, const S* rc) - { - for (int i = 0; i < T; i++) { - state[i] = state[i] + rc[rc_offset + i]; - } - } - - template - __device__ void mds_light_4x4(S s[4]) - { - S t0 = s[0] + s[1]; - S t1 = s[2] + s[3]; - S t2 = s[1] + s[1] + t1; - S t3 = s[3] + s[3] + t0; - S t4 = S::template mul_unsigned<4>(t1) + t3; - S t5 = S::template mul_unsigned<4>(t0) + t2; - s[0] = t3 + t5; - s[1] = t5; - s[2] = t2 + t4; - s[3] = t4; - } - - // Multiply a 4-element vector x by: - // [ 2 3 1 1 ] - // [ 1 2 3 1 ] - // [ 1 1 2 3 ] - // [ 3 1 1 2 ]. - // https://github.com/Plonky3/Plonky3/blob/main/poseidon2/src/matrix.rs#L36 - template - __device__ void mds_light_plonky_4x4(S s[4]) - { - S t01 = s[0] + s[1]; - S t23 = s[2] + s[3]; - S t0123 = t01 + t23; - S t01123 = t0123 + s[1]; - S t01233 = t0123 + s[3]; - s[3] = t01233 + S::template mul_unsigned<2>(s[0]); - s[1] = t01123 + S::template mul_unsigned<2>(s[2]); - s[0] = t01123 + t01; - s[2] = t01233 + t23; - } - - template - __device__ void mds_light(S state[T], MdsType mds) - { - S sum; - switch (T) { - case 2: - // Matrix circ(2, 1) - // [2, 1] - // [1, 2] - sum = state[0] + state[1]; - state[0] = state[0] + sum; - state[1] = state[1] + sum; - break; - case 3: - // Matrix circ(2, 1, 1) - // [2, 1, 1] - // [1, 2, 1] - // [1, 1, 2] - sum = state[0] + state[1] + state[2]; - state[0] = state[0] + sum; - state[1] = state[1] + sum; - state[2] = state[2] + sum; - break; - case 4: - case 8: - case 12: - case 16: - case 20: - case 24: - for (int i = 0; i < T; i += 4) { - switch (mds) { - case MdsType::DEFAULT_MDS: - mds_light_4x4(&state[i]); - break; - case MdsType::PLONKY: - mds_light_plonky_4x4(&state[i]); - } - } - - S sums[4] = {state[0], state[1], state[2], state[3]}; - for (int i = 4; i < T; i += 4) { - sums[0] = sums[0] + state[i]; - sums[1] = sums[1] + state[i + 1]; - sums[2] = sums[2] + state[i + 2]; - sums[3] = sums[3] + state[i + 3]; - } - for (int i = 0; i < T; i++) { - state[i] = state[i] + sums[i % 4]; - } - break; - } - } - - template - __device__ void internal_round(S state[T], size_t rc_offset, const Poseidon2Constants& constants) - { - S element = state[0]; - element = element + constants.round_constants[rc_offset]; - element = sbox_el(element, constants.alpha); - - S sum; - switch (T) { - case 2: - // [2, 1] - // [1, 3] - sum = element + state[1]; - state[0] = element + sum; - state[1] = S::template mul_unsigned<2>(state[1]) + sum; - break; - case 3: - // [2, 1, 1] - // [1, 2, 1] - // [1, 1, 3] - sum = element + state[1] + state[2]; - state[0] = element + sum; - state[1] = state[1] + sum; - state[2] = S::template mul_unsigned<2>(state[2]) + sum; - break; - case 4: - case 8: - case 12: - case 16: - case 20: - case 24: - typename S::Wide wide_sum = S::Wide::from_field(element); - for (int i = 1; i < T; i++) { - wide_sum = wide_sum + S::Wide::from_field(state[i]); - } - sum = S::reduce(wide_sum); - switch (constants.diffusion) { - case DiffusionStrategy::DEFAULT_DIFFUSION: - state[0] = element * constants.internal_matrix_diag[0] + sum; - for (int i = 1; i < T; i++) { - state[i] = state[i] * constants.internal_matrix_diag[i] + sum; - } - break; - case DiffusionStrategy::MONTGOMERY: - state[0] = S::from_montgomery(element * constants.internal_matrix_diag[0] + sum); - for (int i = 1; i < T; i++) { - state[i] = S::from_montgomery(state[i] * constants.internal_matrix_diag[i] + sum); - } - break; - } - } - } - - template - __device__ void permute_state(S state[T], const Poseidon2Constants& constants) - { - unsigned int rn; - - mds_light(state, constants.mds_type); - - size_t rc_offset = 0; - // External rounds - for (rn = 0; rn < constants.external_rounds / 2; rn++) { - add_rc(state, rc_offset, constants.round_constants); - sbox(state, constants.alpha); - mds_light(state, constants.mds_type); - rc_offset += T; - } - - // Internal rounds - for (; rn < constants.external_rounds / 2 + constants.internal_rounds; rn++) { - internal_round(state, rc_offset, constants); - rc_offset++; - } - - // External rounds - for (; rn < constants.external_rounds + constants.internal_rounds; rn++) { - add_rc(state, rc_offset, constants.round_constants); - sbox(state, constants.alpha); - mds_light(state, constants.mds_type); - rc_offset += T; - } - } - - template - __global__ void permutation_kernel( - const S* states, S* states_out, unsigned int number_of_states, const Poseidon2Constants constants) - { - int idx = (blockIdx.x * blockDim.x) + threadIdx.x; - if (idx >= number_of_states) { return; } - - S state[T]; - UNROLL - for (int i = 0; i < T; i++) { - state[i] = states[idx * T + i]; - } - - permute_state(state, constants); - - UNROLL - for (int i = 0; i < T; i++) { - states_out[idx * T + i] = state[i]; - } - } - - template - __global__ void hash_many_kernel( - const S* input, - S* output, - uint64_t number_of_states, - unsigned int input_len, - unsigned int output_len, - const Poseidon2Constants constants) - { - uint64_t idx = (blockIdx.x * blockDim.x) + threadIdx.x; - if (idx >= number_of_states) { return; } - - S state[T] = {0}; - UNROLL - for (int i = 0; i < input_len; i++) { - state[i] = input[idx * input_len + i]; - } - - permute_state(state, constants); - - UNROLL - for (int i = 0; i < output_len; i++) { - output[idx * output_len + i] = state[i]; - } - } - - template - __device__ void absorb_2d_state( - const Matrix* inputs, - S state[T], - unsigned int number_of_inputs, - unsigned int rate, - uint64_t row_idx, - const Poseidon2Constants& constants) - { - unsigned int index = 0; - for (int i = 0; i < number_of_inputs; i++) { - const Matrix* input = inputs + i; - for (int j = 0; j < input->width; j++) { - state[index] = input->values[row_idx * input->width + j]; - index++; - if (index == rate) { - permute_state(state, constants); - index = 0; - } - } - } - - if (index) { permute_state(state, constants); } - } - - template - __global__ void hash_2d_kernel( - const Matrix* inputs, - S* output, - unsigned int number_of_inputs, - unsigned int rate, - unsigned int output_len, - const Poseidon2Constants constants) - { - uint64_t idx = (blockIdx.x * blockDim.x) + threadIdx.x; - if (idx >= inputs[0].height) { return; } - - S state[T] = {0}; - - absorb_2d_state(inputs, state, number_of_inputs, rate, idx, constants); - - UNROLL - for (int i = 0; i < output_len; i++) { - output[idx * output_len + i] = state[i]; - } - } - - template - __global__ void compress_and_inject_kernel( - const Matrix* matrices_to_inject, - unsigned int number_of_inputs, - const S* prev_layer, - S* next_layer, - unsigned int rate, - unsigned int digest_elements, - const Poseidon2Constants constants) - { - int idx = (blockIdx.x * blockDim.x) + threadIdx.x; - uint64_t number_of_rows = d_next_pow_of_two(matrices_to_inject[0].height); - if (idx >= number_of_rows) { return; } - - size_t next_layer_len = matrices_to_inject[0].height; - S state_to_compress[T] = {S::zero()}; - - for (int i = 0; i < digest_elements * 2; i++) { - state_to_compress[i] = prev_layer[idx * 2 * digest_elements + i]; - } - permute_state(state_to_compress, constants); - - S injected_state[T] = {S::zero()}; - if (idx < next_layer_len) { - absorb_2d_state(matrices_to_inject, injected_state, number_of_inputs, rate, idx, constants); - - for (int i = 0; i < digest_elements; i++) { - injected_state[digest_elements + i] = injected_state[i]; - injected_state[i] = state_to_compress[i]; - } - } else { - for (int i = 0; i < digest_elements; i++) { - injected_state[i] = state_to_compress[i]; - } - } - permute_state(injected_state, constants); - - for (int i = 0; i < digest_elements; i++) { - next_layer[idx * digest_elements + i] = injected_state[i]; - } - } -} // namespace poseidon2 - -#endif \ No newline at end of file diff --git a/icicle/include/poseidon2/poseidon2.cuh b/icicle/include/poseidon2/poseidon2.cuh deleted file mode 100644 index 016740e7a..000000000 --- a/icicle/include/poseidon2/poseidon2.cuh +++ /dev/null @@ -1,179 +0,0 @@ -#pragma once -#ifndef POSEIDON2_H -#define POSEIDON2_H - -#include -#include -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "utils/utils.h" - -#include "hash/hash.cuh" -#include "matrix/matrix.cuh" - -#include "poseidon2/constants.cuh" -#include "poseidon2/kernels.cuh" - -using matrix::Matrix; - -/** - * @namespace poseidon2 - * Implementation of the [Poseidon2 hash function](https://eprint.iacr.org/2019/458.pdf) - * Specifically, the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/) - */ -namespace poseidon2 { - template - class Poseidon2 : public hash::Hasher - { - static const int POSEIDON_BLOCK_SIZE = 32; - - static inline int poseidon_number_of_blocks(size_t number_of_states) - { - return number_of_states / POSEIDON_BLOCK_SIZE + static_cast(number_of_states % POSEIDON_BLOCK_SIZE); - } - - public: - const std::size_t device_id; - Poseidon2Constants constants; - - cudaError_t hash_2d( - const Matrix* inputs, - S* output, - unsigned int number_of_inputs, - unsigned int output_len, - uint64_t number_of_rows, - const device_context::DeviceContext& ctx) const override - { -#define P2_HASH_2D_T(width) \ - case width: \ - hash_2d_kernel<<>>( \ - inputs, output, number_of_inputs, this->rate, output_len, this->constants); \ - break; - - switch (this->width) { - P2_HASH_2D_T(2) - P2_HASH_2D_T(3) - P2_HASH_2D_T(4) - P2_HASH_2D_T(8) - P2_HASH_2D_T(12) - P2_HASH_2D_T(16) - P2_HASH_2D_T(20) - P2_HASH_2D_T(24) - default: - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "PoseidonAbsorb2d: #width must be one of [2, 3, 4, 8, 12, 16, 20, 24]"); - } - - CHK_IF_RETURN(cudaPeekAtLastError()); - return CHK_LAST(); - } - - cudaError_t run_hash_many_kernel( - const S* input, - S* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const device_context::DeviceContext& ctx) const override - { -#define P2_HASH_MANY_T(width) \ - case width: \ - hash_many_kernel<<>>( \ - input, output, number_of_states, input_len, output_len, this->constants); \ - break; - - switch (this->width) { - P2_HASH_MANY_T(2) - P2_HASH_MANY_T(3) - P2_HASH_MANY_T(4) - P2_HASH_MANY_T(8) - P2_HASH_MANY_T(12) - P2_HASH_MANY_T(16) - P2_HASH_MANY_T(20) - P2_HASH_MANY_T(24) - default: - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "PoseidonPermutation: #width must be one of [2, 3, 4, 8, 12, 16, 20, 24]"); - } - CHK_IF_RETURN(cudaPeekAtLastError()); - return CHK_LAST(); - } - - cudaError_t compress_and_inject( - const Matrix* matrices_to_inject, - unsigned int number_of_inputs, - uint64_t number_of_rows, - const S* prev_layer, - S* next_layer, - unsigned int digest_elements, - const device_context::DeviceContext& ctx) const override - { -#define P2_COMPRESS_AND_INJECT_T(width) \ - case width: \ - compress_and_inject_kernel \ - <<>>( \ - matrices_to_inject, number_of_inputs, prev_layer, next_layer, this->rate, digest_elements, this->constants); \ - break; - - switch (this->width) { - P2_COMPRESS_AND_INJECT_T(2) - P2_COMPRESS_AND_INJECT_T(3) - P2_COMPRESS_AND_INJECT_T(4) - P2_COMPRESS_AND_INJECT_T(8) - P2_COMPRESS_AND_INJECT_T(12) - P2_COMPRESS_AND_INJECT_T(16) - P2_COMPRESS_AND_INJECT_T(20) - P2_COMPRESS_AND_INJECT_T(24) - default: - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "PoseidonPermutation: #width must be one of [2, 3, 4, 8, 12, 16, 20, 24]"); - } - - CHK_IF_RETURN(cudaPeekAtLastError()); - return CHK_LAST(); - } - - Poseidon2( - unsigned int width, - unsigned int rate, - unsigned int alpha, - unsigned int internal_rounds, - unsigned int external_rounds, - const S* round_constants, - const S* internal_matrix_diag, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx) - : hash::Hasher(width, width, rate, 0), device_id(ctx.device_id) - { - Poseidon2Constants constants; - CHK_STICKY(create_poseidon2_constants( - width, alpha, internal_rounds, external_rounds, round_constants, internal_matrix_diag, mds_type, diffusion, ctx, - &constants)); - this->constants = constants; - } - - Poseidon2( - unsigned int width, - unsigned int rate, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx) - : hash::Hasher(width, width, rate, 0), device_id(ctx.device_id) - { - Poseidon2Constants constants; - CHK_STICKY(init_poseidon2_constants(width, mds_type, diffusion, ctx, &constants)); - this->constants = constants; - } - - ~Poseidon2() - { - auto ctx = device_context::get_default_device_context(); - ctx.device_id = this->device_id; - CHK_STICKY(release_poseidon2_constants(&this->constants, ctx)); - } - }; - -} // namespace poseidon2 - -#endif \ No newline at end of file diff --git a/icicle/include/utils/mont.cuh b/icicle/include/utils/mont.cuh deleted file mode 100644 index d69f30cb8..000000000 --- a/icicle/include/utils/mont.cuh +++ /dev/null @@ -1,45 +0,0 @@ -#pragma once -#ifndef MONT_H -#define MONT_H - -namespace mont { - - namespace { - -#define MAX_THREADS_PER_BLOCK 256 - - template - __global__ void MontgomeryKernel(const E* input, int n, E* output) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { output[tid] = is_into ? E::to_montgomery(input[tid]) : E::from_montgomery(input[tid]); } - } - - template - cudaError_t ConvertMontgomery(const E* d_input, int n, cudaStream_t stream, E* d_output) - { - // Set the grid and block dimensions - int num_threads = MAX_THREADS_PER_BLOCK; - int num_blocks = (n + num_threads - 1) / num_threads; - MontgomeryKernel<<>>(d_input, n, d_output); - - return CHK_LAST(); - } - - } // namespace - - template - cudaError_t to_montgomery(const E* d_input, int n, cudaStream_t stream, E* d_output) - { - return ConvertMontgomery(d_input, n, stream, d_output); - } - - template - cudaError_t from_montgomery(const E* d_input, int n, cudaStream_t stream, E* d_output) - { - return ConvertMontgomery(d_input, n, stream, d_output); - } - -} // namespace mont - -#endif diff --git a/icicle/include/utils/test_functions.cuh b/icicle/include/utils/test_functions.cuh deleted file mode 100644 index 8005a7952..000000000 --- a/icicle/include/utils/test_functions.cuh +++ /dev/null @@ -1,142 +0,0 @@ -#pragma once - -template -__global__ void add_elements_kernel(const T1* x, const T2* y, T1* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - T1 res = x[gid]; - T2 y_gid = y[gid]; - for (int i = 0; i < N_REP; i++) - res = res + y_gid; - result[gid] = res; -} - -template -int vec_add(const T1* x, const T2* y, T1* result, const unsigned count) -{ - add_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, y, result, count); - int error = cudaGetLastError(); - return (error || (N_REP > 1)) ? error : cudaDeviceSynchronize(); -} - -template -__global__ void sub_elements_kernel(const T1* x, const T2* y, T1* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - result[gid] = x[gid] - y[gid]; -} - -template -int vec_sub(const T1* x, const T2* y, T1* result, const unsigned count) -{ - sub_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, y, result, count); - int error = cudaGetLastError(); - return error ? error : cudaDeviceSynchronize(); -} - -template -__global__ void neg_elements_kernel(const T* x, T* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - result[gid] = T::neg(x[gid]); -} - -template -int vec_neg(const T* x, T* result, const unsigned count) -{ - neg_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, result, count); - int error = cudaGetLastError(); - return error ? error : cudaDeviceSynchronize(); -} - -template -__global__ void mul_elements_kernel(const F* x, const G* y, G* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - F x_gid = x[gid]; - G res = y[gid]; - for (int i = 0; i < N_REP; i++) - res = res * x_gid; - result[gid] = res; -} - -template -int vec_mul(const F* x, const G* y, G* result, const unsigned count) -{ - mul_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, y, result, count); - int error = cudaGetLastError(); - return (error || (N_REP > 1)) ? error : cudaDeviceSynchronize(); -} - -template -__global__ void inv_field_elements_kernel(const F* x, F* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - result[gid] = F::inverse(x[gid]); -} - -template -int field_vec_inv(const F* x, F* result, const unsigned count) -{ - inv_field_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, result, count); - int error = cudaGetLastError(); - return error ? error : cudaDeviceSynchronize(); -} - -template -__global__ void sqr_field_elements_kernel(const F* x, F* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - F x_gid = x[gid]; - for (int i = 0; i < N_REP; i++) - x_gid = F::sqr(x_gid); - result[gid] = x_gid; -} - -template -int field_vec_sqr(const F* x, F* result, const unsigned count) -{ - sqr_field_elements_kernel<<<(count - 1) / 256 + 1, 256>>>(x, result, count); - int error = cudaGetLastError(); - return (error || (N_REP > 1)) ? error : cudaDeviceSynchronize(); -} - -template -__global__ void to_affine_points_kernel(const P* x, A* result, const unsigned count) -{ - const unsigned gid = blockIdx.x * blockDim.x + threadIdx.x; - if (gid >= count) return; - result[gid] = P::to_affine(x[gid]); -} - -template -int point_vec_to_affine(const P* x, A* result, const unsigned count) -{ - to_affine_points_kernel<<<(count - 1) / 256 + 1, 256>>>(x, result, count); - int error = cudaGetLastError(); - return error ? error : cudaDeviceSynchronize(); -} - -template -int device_populate_random(T* d_elements, unsigned n) -{ - T* h_elements = (T*)malloc(n * sizeof(T)); - for (unsigned i = 0; i < n; i++) - h_elements[i] = T::rand_host(); - return cudaMemcpy(d_elements, h_elements, sizeof(T) * n, cudaMemcpyHostToDevice); -} - -template -int device_set(T* d_elements, T el, unsigned n) -{ - T* h_elements = (T*)malloc(n * sizeof(T)); - for (unsigned i = 0; i < n; i++) - h_elements[i] = el; - return cudaMemcpy(d_elements, h_elements, sizeof(T) * n, cudaMemcpyHostToDevice); -} diff --git a/icicle/include/utils/utils_kernels.cuh b/icicle/include/utils/utils_kernels.cuh deleted file mode 100644 index a433310fc..000000000 --- a/icicle/include/utils/utils_kernels.cuh +++ /dev/null @@ -1,35 +0,0 @@ -#pragma once -#ifndef UTILS_KERNELS_H -#define UTILS_KERNELS_H - -namespace utils_internal { - template - __global__ void NormalizeKernel(E* arr, S scalar, int n) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { arr[tid] = scalar * arr[tid]; } - } - - template - __global__ void BatchMulKernel( - const E* in_vec, - int n_elements, - int batch_size, - S* scalar_vec, - int step, - int n_scalars, - int logn, - bool bitrev, - E* out_vec) - { - int tid = blockDim.x * blockIdx.x + threadIdx.x; - if (tid < n_elements * batch_size) { - int64_t scalar_id = tid % n_elements; - if (bitrev) scalar_id = __brev(scalar_id) >> (32 - logn); - out_vec[tid] = *(scalar_vec + ((scalar_id * step) % n_scalars)) * in_vec[tid]; - } - } - -} // namespace utils_internal - -#endif \ No newline at end of file diff --git a/icicle/include/vec_ops/vec_ops.cuh b/icicle/include/vec_ops/vec_ops.cuh deleted file mode 100644 index ee624a07f..000000000 --- a/icicle/include/vec_ops/vec_ops.cuh +++ /dev/null @@ -1,139 +0,0 @@ -#pragma once -#ifndef LDE_H -#define LDE_H - -#include "gpu-utils/device_context.cuh" - -/** - * @namespace vec_ops - * This namespace contains methods for performing element-wise arithmetic operations on vectors. - */ -namespace vec_ops { - - /** - * @struct VecOpsConfig - * Struct that encodes NTT parameters to be passed into the [NTT](@ref NTT) function. - */ - struct VecOpsConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream. */ - - bool is_a_on_device; /**< True if `a` is on device and false if it is not. Default value: false. */ - - bool is_b_on_device; /**< True if `b` is on device and false if it is not. Default value: false. */ - - bool is_result_on_device; /**< If true, output is preserved on device, otherwise on host. Default value: false. */ - - bool is_async; /**< Whether to run the vector operations asynchronously. If set to `true`, the function will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the - * function will block the current CPU thread. */ - }; - - /** - * A function that returns the default value of [VecOpsConfig](@ref VecOpsConfig). - * @return Default value of [VecOpsConfig](@ref VecOpsConfig). - */ - static VecOpsConfig - DefaultVecOpsConfig(const device_context::DeviceContext& ctx = device_context::get_default_device_context()) - { - VecOpsConfig config = { - ctx, // ctx - false, // is_a_on_device - false, // is_b_on_device - false, // is_result_on_device - false, // is_async - }; - return config; - } - - /** - * A function that multiplies two vectors element-wise. - * @param vec_a First input vector. - * @param vec_b Second input vector. - * @param n Size of vectors `vec_a` and `vec_b`. - * @param config Configuration of the operation. - * @param result Resulting vector - element-wise product of `vec_a` and `vec_b`, can be the same pointer as `vec_b`. - * @tparam S The type of scalars `vec_a`. - * @tparam E The type of elements `vec_b` and `result`. Often (but not always) `E=S`. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t Mul(const S* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result); - - /** - * A function that adds two vectors element-wise. - * @param vec_a First input vector. - * @param vec_b Second input vector. - * @param n Size of vectors `vec_a` and `vec_b`. - * @param config Configuration of the operation. - * @param result Resulting vector - element-wise sum of `vec_a` and `vec_b`, can be the same pointer as `vec_a` or - * `vec_b`. If inputs are in Montgomery form, the result is too, and vice versa: non-Montgomery inputs produce - * non-Montgomery result. - * @tparam E The type of elements `vec_a`, `vec_b` and `result`. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t Add(const E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result); - - /** - * A function that subtracts two vectors element-wise. - * @param vec_a First input vector. - * @param vec_b Second input vector. - * @param n Size of vectors `vec_a` and `vec_b`. - * @param config Configuration of the operation. - * @param result Resulting vector - element-wise difference of `vec_a` and `vec_b`, can be the same pointer as `vec_a` - * or `vec_b`. If inputs are in Montgomery form, the result is too, and vice versa: non-Montgomery inputs produce - * non-Montgomery result. - * @tparam E The type of elements `vec_a`, `vec_b` and `result`. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t Sub(const E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result); - - /** - * Transposes an input matrix out-of-place inside GPU. - * for example: for ([a[0],a[1],a[2],a[3]], 2, 2) it returns - * [a[0],a[2],a[1],a[3]]. - * @param mat_in array of some object of type E of size row_size * column_size. - * @param arr_out buffer of the same size as `mat_in` to write the transpose matrix into. - * @param row_size size of rows. - * @param column_size size of columns. - * @param ctx Device context. - * @param on_device Whether the input and output are on device. - * @param is_async Whether to run the vector operations asynchronously. - * @tparam E The type of elements `mat_in' and `mat_out`. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - template - cudaError_t transpose_matrix( - const E* mat_in, - E* mat_out, - uint32_t row_size, - uint32_t column_size, - const device_context::DeviceContext& ctx, - bool on_device, - bool is_async); - - struct BitReverseConfig { - device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream. */ - bool is_input_on_device; /**< True if `input` is on device and false if it is not. Default value: false. */ - bool is_output_on_device; /**< True if `output` is on device and false if it is not. Default value: false. */ - bool is_async; /**< Whether to run the vector operations asynchronously. If set to `true`, the function will be - * non-blocking and you'd need to synchronize it explicitly by running - * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the - * function will block the current CPU thread. */ - }; - static BitReverseConfig - DefaultBitReverseConfig(const device_context::DeviceContext& ctx = device_context::get_default_device_context()) - { - BitReverseConfig config = { - ctx, // ctx - false, // is_input_on_device - false, // is_output_on_device - false, // is_async - }; - return config; - } -} // namespace vec_ops - -#endif diff --git a/icicle/src/config_extension.cpp b/icicle/src/config_extension.cpp new file mode 100644 index 000000000..7a8388269 --- /dev/null +++ b/icicle/src/config_extension.cpp @@ -0,0 +1,38 @@ +#include "icicle/config_extension.h" +#include + +using namespace icicle; +extern "C" { + +ConfigExtension* create_config_extension() { return new ConfigExtension(); } + +void destroy_config_extension(ConfigExtension* ext) { delete ext; } + +void config_extension_set_int(ConfigExtension* ext, const char* key, int value) +{ + if (ext) { ext->set(key, value); } +} + +void config_extension_set_bool(ConfigExtension* ext, const char* key, bool value) +{ + if (ext) { ext->set(key, value); } +} + +int config_extension_get_int(const ConfigExtension* ext, const char* key) +{ + if (!ext) { THROW_ICICLE_ERR(eIcicleError::INVALID_DEVICE, "ConfigExtension is null"); } + return ext->get(key); +} + +bool config_extension_get_bool(const ConfigExtension* ext, const char* key) +{ + if (!ext) { THROW_ICICLE_ERR(eIcicleError::INVALID_DEVICE, "ConfigExtension is null"); } + return ext->get(key); +} + +ConfigExtension* clone_config_extension(const ConfigExtension* ext) +{ + if (!ext) { THROW_ICICLE_ERR(eIcicleError::INVALID_DEVICE, "ConfigExtension is null"); } + return ext->clone(); +} +} // extern "C" \ No newline at end of file diff --git a/icicle/src/curves/CMakeLists.txt b/icicle/src/curves/CMakeLists.txt deleted file mode 100644 index ee894bbb9..000000000 --- a/icicle/src/curves/CMakeLists.txt +++ /dev/null @@ -1,29 +0,0 @@ -if (G2) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DG2") -endif () - -set(TARGET icicle_curve) -set(FIELD_TARGET icicle_field) - -set(SRC ${CMAKE_SOURCE_DIR}/src) - -set(CURVE_SOURCE ${SRC}/curves/extern.cu) -if(G2) - list(APPEND CURVE_SOURCE ${SRC}/curves/extern_g2.cu) -endif() -if(MSM) - list(APPEND CURVE_SOURCE ${SRC}/msm/extern.cu) - if(G2) - list(APPEND CURVE_SOURCE ${SRC}/msm/extern_g2.cu) - endif() -endif() -if(ECNTT) - list(APPEND CURVE_SOURCE ${SRC}/ntt/extern_ecntt.cu) - list(APPEND CURVE_SOURCE ${SRC}/ntt/kernel_ntt.cu) -endif() - -add_library(${TARGET} STATIC ${CURVE_SOURCE}) -target_include_directories(${TARGET} PUBLIC ${CMAKE_SOURCE_DIR}/include/) -set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME "ingo_curve_${CURVE}") -target_compile_definitions(${TARGET} PUBLIC CURVE=${CURVE}) -target_link_libraries(${TARGET} PRIVATE ${FIELD_TARGET}) \ No newline at end of file diff --git a/icicle/src/curves/extern.cu b/icicle/src/curves/extern.cu deleted file mode 100644 index 8ea5bce2f..000000000 --- a/icicle/src/curves/extern.cu +++ /dev/null @@ -1,51 +0,0 @@ -#include "curves/curve_config.cuh" - -using namespace curve_config; - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" -#include "utils/mont.cuh" - -extern "C" bool CONCAT_EXPAND(CURVE, eq)(projective_t* point1, projective_t* point2) -{ - return (*point1 == *point2) && - !((point1->x == point_field_t::zero()) && (point1->y == point_field_t::zero()) && - (point1->z == point_field_t::zero())) && - !((point2->x == point_field_t::zero()) && (point2->y == point_field_t::zero()) && - (point2->z == point_field_t::zero())); -} - -extern "C" void CONCAT_EXPAND(CURVE, to_affine)(projective_t* point, affine_t* point_out) -{ - *point_out = projective_t::to_affine(*point); -} - -extern "C" void CONCAT_EXPAND(CURVE, generate_projective_points)(projective_t* points, int size) -{ - projective_t::rand_host_many(points, size); -} - -extern "C" void CONCAT_EXPAND(CURVE, generate_affine_points)(affine_t* points, int size) -{ - projective_t::rand_host_many_affine(points, size); -} - -extern "C" cudaError_t CONCAT_EXPAND(CURVE, affine_convert_montgomery)( - affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} - -extern "C" cudaError_t CONCAT_EXPAND(CURVE, projective_convert_montgomery)( - projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} diff --git a/icicle/src/curves/extern_g2.cu b/icicle/src/curves/extern_g2.cu deleted file mode 100644 index e8daa5e11..000000000 --- a/icicle/src/curves/extern_g2.cu +++ /dev/null @@ -1,51 +0,0 @@ -#include "curves/curve_config.cuh" - -using namespace curve_config; - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" -#include "utils/mont.cuh" - -extern "C" bool CONCAT_EXPAND(CURVE, g2_eq)(g2_projective_t* point1, g2_projective_t* point2) -{ - return (*point1 == *point2) && - !((point1->x == g2_point_field_t::zero()) && (point1->y == g2_point_field_t::zero()) && - (point1->z == g2_point_field_t::zero())) && - !((point2->x == g2_point_field_t::zero()) && (point2->y == g2_point_field_t::zero()) && - (point2->z == g2_point_field_t::zero())); -} - -extern "C" void CONCAT_EXPAND(CURVE, g2_to_affine)(g2_projective_t* point, g2_affine_t* point_out) -{ - *point_out = g2_projective_t::to_affine(*point); -} - -extern "C" void CONCAT_EXPAND(CURVE, g2_generate_projective_points)(g2_projective_t* points, int size) -{ - g2_projective_t::rand_host_many(points, size); -} - -extern "C" void CONCAT_EXPAND(CURVE, g2_generate_affine_points)(g2_affine_t* points, int size) -{ - g2_projective_t::rand_host_many_affine(points, size); -} - -extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_affine_convert_montgomery)( - g2_affine_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} - -extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_projective_convert_montgomery)( - g2_projective_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} \ No newline at end of file diff --git a/icicle/src/curves/ffi_extern.cpp b/icicle/src/curves/ffi_extern.cpp new file mode 100644 index 000000000..db27a9126 --- /dev/null +++ b/icicle/src/curves/ffi_extern.cpp @@ -0,0 +1,68 @@ +#include "icicle/utils/utils.h" +#include "icicle/curves/curve_config.h" + +using namespace curve_config; + +// extern functions for FFI + +/********************************** G1 **********************************/ +extern "C" bool CONCAT_EXPAND(CURVE, eq)(projective_t* point1, projective_t* point2) +{ + return (*point1 == *point2) && + !((point1->x == point_field_t::zero()) && (point1->y == point_field_t::zero()) && + (point1->z == point_field_t::zero())) && + !((point2->x == point_field_t::zero()) && (point2->y == point_field_t::zero()) && + (point2->z == point_field_t::zero())); +} + +extern "C" void CONCAT_EXPAND(CURVE, to_affine)(projective_t* point, affine_t* point_out) +{ + *point_out = projective_t::to_affine(*point); +} + +extern "C" void CONCAT_EXPAND(CURVE, from_affine)(affine_t* point, projective_t* point_out) +{ + *point_out = projective_t::from_affine(*point); +} + +extern "C" void CONCAT_EXPAND(CURVE, generate_projective_points)(projective_t* points, int size) +{ + projective_t::rand_host_many(points, size); +} + +extern "C" void CONCAT_EXPAND(CURVE, generate_affine_points)(affine_t* points, int size) +{ + projective_t::rand_host_many(points, size); +} + +/********************************** G2 **********************************/ +#ifdef G2 +extern "C" bool CONCAT_EXPAND(CURVE, g2_eq)(g2_projective_t* point1, g2_projective_t* point2) +{ + return (*point1 == *point2) && + !((point1->x == g2_point_field_t::zero()) && (point1->y == g2_point_field_t::zero()) && + (point1->z == g2_point_field_t::zero())) && + !((point2->x == g2_point_field_t::zero()) && (point2->y == g2_point_field_t::zero()) && + (point2->z == g2_point_field_t::zero())); +} + +extern "C" void CONCAT_EXPAND(CURVE, g2_to_affine)(g2_projective_t* point, g2_affine_t* point_out) +{ + *point_out = g2_projective_t::to_affine(*point); +} + +extern "C" void CONCAT_EXPAND(CURVE, g2_from_affine)(g2_affine_t* point, g2_projective_t* point_out) +{ + *point_out = g2_projective_t::from_affine(*point); +} + +extern "C" void CONCAT_EXPAND(CURVE, g2_generate_projective_points)(g2_projective_t* points, int size) +{ + g2_projective_t::rand_host_many(points, size); +} + +extern "C" void CONCAT_EXPAND(CURVE, g2_generate_affine_points)(g2_affine_t* points, int size) +{ + g2_projective_t::rand_host_many(points, size); +} +#endif // G2 diff --git a/icicle/src/curves/montgomery_conversion.cpp b/icicle/src/curves/montgomery_conversion.cpp new file mode 100644 index 000000000..54f0c17f9 --- /dev/null +++ b/icicle/src/curves/montgomery_conversion.cpp @@ -0,0 +1,77 @@ +#include "icicle/vec_ops.h" +#include "icicle/curves/montgomery_conversion.h" +#include "icicle/dispatcher.h" +#include "icicle/curves/curve_config.h" + +using namespace curve_config; + +namespace icicle { + + /*************************** AFFINE CONVERT MONTGOMERY ***************************/ + ICICLE_DISPATCHER_INST(AffineConvertMont, affine_convert_montgomery, AffineConvertMontImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, affine_convert_montgomery)( + const affine_t* input, uint64_t n, bool is_into, const VecOpsConfig* config, affine_t* output) + { + return AffineConvertMont::execute(input, n, is_into, *config, output); + } + + template <> + eIcicleError + convert_montgomery(const affine_t* input, uint64_t n, bool is_into, const VecOpsConfig& config, affine_t* output) + { + return CONCAT_EXPAND(CURVE, affine_convert_montgomery)(input, n, is_into, &config, output); + } + +#ifdef G2 + ICICLE_DISPATCHER_INST(AffineG2ConvertMont, g2_affine_convert_montgomery, AffineG2ConvertMontImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, g2_affine_convert_montgomery)( + const g2_affine_t* input, size_t n, bool is_into, const VecOpsConfig* config, g2_affine_t* output) + { + return AffineG2ConvertMont::execute(input, n, is_into, *config, output); + } + +#ifndef G1_AFFINE_SAME_TYPE_AS_G2_AFFINE + template <> + eIcicleError convert_montgomery( + const g2_affine_t* input, uint64_t n, bool is_into, const VecOpsConfig& config, g2_affine_t* output) + { + return CONCAT_EXPAND(CURVE, g2_affine_convert_montgomery)(input, n, is_into, &config, output); + } +#endif //! G1_AFFINE_SAME_TYPE_AS_G2_AFFINE +#endif // G2 + /*************************** PROJECTIVE CONVERT MONTGOMERY ***************************/ + ICICLE_DISPATCHER_INST(ProjectiveConvertMont, projective_convert_montgomery, ProjectiveConvertMontImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, projective_convert_montgomery)( + const projective_t* input, uint64_t n, bool is_into, const VecOpsConfig* config, projective_t* output) + { + return ProjectiveConvertMont::execute(input, n, is_into, *config, output); + } + + template <> + eIcicleError convert_montgomery( + const projective_t* input, uint64_t n, bool is_into, const VecOpsConfig& config, projective_t* output) + { + return CONCAT_EXPAND(CURVE, projective_convert_montgomery)(input, n, is_into, &config, output); + } + +#ifdef G2 + ICICLE_DISPATCHER_INST(ProjectiveG2ConvertMont, g2_projective_convert_montgomery, ProjectiveG2ConvertMontImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, g2_projective_convert_montgomery)( + const g2_projective_t* input, uint64_t n, bool is_into, const VecOpsConfig* config, g2_projective_t* output) + { + return ProjectiveG2ConvertMont::execute(input, n, is_into, *config, output); + } + + template <> + eIcicleError convert_montgomery( + const g2_projective_t* input, uint64_t n, bool is_into, const VecOpsConfig& config, g2_projective_t* output) + { + return CONCAT_EXPAND(CURVE, g2_projective_convert_montgomery)(input, n, is_into, &config, output); + } + +#endif // G2 +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/device_api.cpp b/icicle/src/device_api.cpp new file mode 100644 index 000000000..0383a78d5 --- /dev/null +++ b/icicle/src/device_api.cpp @@ -0,0 +1,161 @@ + +#include +#include +#include +#include +#include +#include + +#include "icicle/runtime.h" +#include "icicle/device_api.h" +#include "icicle/errors.h" +#include "icicle/utils/log.h" +#include "icicle/memory_tracker.h" + +namespace icicle { + + class DeviceAPIRegistry + { + std::unordered_map> apiMap; + + public: + static DeviceAPIRegistry& Global() + { + static DeviceAPIRegistry instance; + return instance; + } + + void register_deviceAPI(const std::string& deviceType, std::shared_ptr api) + { + if (apiMap.find(deviceType) != apiMap.end()) { + ICICLE_LOG_ERROR << "Attempting to register a duplicate API for device type: " << deviceType << ". Skipping"; + return; + } + const bool is_first = apiMap.empty(); + apiMap[deviceType] = api; + if (is_first) { + Device dev{deviceType, 0}; + m_default_device = dev; + api->set_device(dev); + } + } + + std::shared_ptr get_deviceAPI(const Device& device) + { + auto it = apiMap.find(device.type); + if (it == apiMap.end()) { + THROW_ICICLE_ERR(eIcicleError::INVALID_DEVICE, "Device API not found for type: " + std::string(device.type)); + } + return it->second; + } + + std::shared_ptr get_default_deviceAPI() + { + const bool have_default_device = m_default_device.id >= 0; + if (!have_default_device) { return nullptr; } + return get_deviceAPI(m_default_device); + } + + const Device& get_default_device() { return m_default_device; } + + std::vector get_registered_devices_list() + { + std::vector registered_devices; + for (const auto& device : apiMap) { + registered_devices.push_back(device.first); + } + return registered_devices; + } + + bool is_device_registered(const char* device_type) { return apiMap.find(device_type) != apiMap.end(); } + + Device m_default_device{"", -1}; + }; + + MemoryTracker DeviceAPI::sMemTracker; + thread_local Device DeviceAPI::sCurDevice = {"", -1}; + thread_local const DeviceAPI* DeviceAPI::sCurDeviceAPI = nullptr; + + eIcicleError DeviceAPI::set_thread_local_device(const Device& device) + { + DeviceAPI* device_api = get_deviceAPI(device); + if (nullptr == device_api) return eIcicleError::INVALID_DEVICE; + + auto err = device_api->set_device(device); // notifying the device backend about the device set + + if (err == eIcicleError::SUCCESS) { + sCurDevice = device; + sCurDeviceAPI = device_api; + } + return err; + } + + const Device& DeviceAPI::get_thread_local_device() + { + const bool is_device_set = sCurDevice.id >= 0; + if (is_device_set) { return sCurDevice; } + + const Device& default_device = DeviceAPIRegistry::Global().get_default_device(); + if (default_device.id < 0) { + THROW_ICICLE_ERR( + eIcicleError::INVALID_DEVICE, "icicle Device is not set. Make sure to load and initialize a device via call to " + "icicle_set_device(const icicle::Device& device)"); + } + return default_device; + } + + const DeviceAPI* DeviceAPI::get_thread_local_deviceAPI() + { + if (nullptr != sCurDeviceAPI) { return sCurDeviceAPI; } + auto default_deviceAPI = DeviceAPIRegistry::Global().get_default_deviceAPI(); + if (nullptr == default_deviceAPI) { + THROW_ICICLE_ERR( + eIcicleError::INVALID_DEVICE, "icicle Device is not set. Make sure to load and initialize a device via call to " + "icicle_set_device(const icicle::Device& device)"); + } + return default_deviceAPI.get(); + } + + /********************************************************************************** */ + + DeviceAPI* get_deviceAPI(const Device& device) { return DeviceAPIRegistry::Global().get_deviceAPI(device).get(); } + + void register_deviceAPI(const std::string& deviceType, std::shared_ptr api) + { + ICICLE_LOG_DEBUG << " Registering DEVICE: device=" << deviceType; + DeviceAPIRegistry::Global().register_deviceAPI(deviceType, api); + } + + std::vector get_registered_devices_list() + { + return DeviceAPIRegistry::Global().get_registered_devices_list(); + } + + bool is_device_registered(const char* device_type) + { + return DeviceAPIRegistry::Global().is_device_registered(device_type); + } + + eIcicleError get_registered_devices(char* output, size_t output_size) + { + if (output == nullptr) { return eIcicleError::INVALID_POINTER; } + + std::vector devices = get_registered_devices_list(); + std::string concatenated_devices; + + for (const auto& device : devices) { + if (!concatenated_devices.empty()) { concatenated_devices += ","; } + concatenated_devices += device; + } + + if (concatenated_devices.size() + 1 > output_size) { // +1 for null-terminator + return eIcicleError::OUT_OF_MEMORY; + } + + std::strncpy(output, concatenated_devices.c_str(), output_size - 1); + output[output_size - 1] = '\0'; // Ensure null-termination + + return eIcicleError::SUCCESS; + } + +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/ecntt.cpp b/icicle/src/ecntt.cpp new file mode 100644 index 000000000..1e375b1ce --- /dev/null +++ b/icicle/src/ecntt.cpp @@ -0,0 +1,20 @@ + +#include "icicle/backend/ecntt_backend.h" +#include "icicle/dispatcher.h" + +namespace icicle { + ICICLE_DISPATCHER_INST(ECNttExtFieldDispatcher, ecntt, ECNttFieldImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, ecntt)( + const projective_t* input, int size, NTTDir dir, const NTTConfig* config, projective_t* output) + { + return ECNttExtFieldDispatcher::execute(input, size, dir, *config, output); + } + + template <> + eIcicleError + ntt(const projective_t* input, int size, NTTDir dir, const NTTConfig& config, projective_t* output) + { + return CONCAT_EXPAND(FIELD, ecntt)(input, size, dir, &config, output); + } +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/fields/CMakeLists.txt b/icicle/src/fields/CMakeLists.txt deleted file mode 100644 index 1853e319f..000000000 --- a/icicle/src/fields/CMakeLists.txt +++ /dev/null @@ -1,48 +0,0 @@ -if (EXT_FIELD) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -DEXT_FIELD") -endif () - -SET(SUPPORTED_FIELDS_WITHOUT_NTT grumpkin;m31) -SET(SUPPORTED_FIELDS_WITHOUT_POSEIDON2 bls12_381;bls12_377;grumpkin;bw6_761;stark252;m31) - -set(TARGET icicle_field) - -set(SRC ${CMAKE_SOURCE_DIR}/src) - -set(FIELD_SOURCE ${SRC}/fields/extern.cu) -list(APPEND FIELD_SOURCE ${SRC}/vec_ops/extern.cu) -list(APPEND FIELD_SOURCE ${SRC}/merkle-tree/extern.cu) -list(APPEND FIELD_SOURCE ${SRC}/merkle-tree/extern_mmcs.cu) - -if(EXT_FIELD) - list(APPEND FIELD_SOURCE ${SRC}/fields/extern_extension.cu) - if (NOT FIELD IN_LIST SUPPORTED_FIELDS_WITHOUT_NTT) - list(APPEND FIELD_SOURCE ${SRC}/ntt/extern_extension.cu) - endif() - list(APPEND FIELD_SOURCE ${SRC}/vec_ops/extern_extension.cu) -endif() - -set(POLYNOMIAL_SOURCE_FILES - ${SRC}/polynomials/polynomials.cu - ${SRC}/polynomials/cuda_backend/polynomial_cuda_backend.cu - ${SRC}/polynomials/polynomials_c_api.cu) - -# TODO: impl poseidon for small fields. note that it needs to be defined over the extension field! -if (DEFINED CURVE) - list(APPEND FIELD_SOURCE ${SRC}/poseidon/extern.cu) -endif() - -if (NOT FIELD IN_LIST SUPPORTED_FIELDS_WITHOUT_POSEIDON2) - list(APPEND FIELD_SOURCE ${SRC}/poseidon2/extern.cu) -endif() - -if (NOT FIELD IN_LIST SUPPORTED_FIELDS_WITHOUT_NTT) - list(APPEND FIELD_SOURCE ${SRC}/ntt/extern.cu) - list(APPEND FIELD_SOURCE ${SRC}/ntt/kernel_ntt.cu) - list(APPEND FIELD_SOURCE ${POLYNOMIAL_SOURCE_FILES}) # requires NTT -endif() - -add_library(${TARGET} STATIC ${FIELD_SOURCE}) -target_include_directories(${TARGET} PUBLIC ${CMAKE_SOURCE_DIR}/include/) -set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME "ingo_field_${FIELD}") -target_compile_definitions(${TARGET} PUBLIC FIELD=${FIELD}) diff --git a/icicle/src/fields/extern.cu b/icicle/src/fields/extern.cu deleted file mode 100644 index 26c98ac3b..000000000 --- a/icicle/src/fields/extern.cu +++ /dev/null @@ -1,22 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "utils/mont.cuh" -#include "utils/utils.h" -#include "gpu-utils/device_context.cuh" - -extern "C" void CONCAT_EXPAND(FIELD, generate_scalars)(scalar_t* scalars, int size) -{ - scalar_t::rand_host_many(scalars, size); -} - -extern "C" cudaError_t CONCAT_EXPAND(FIELD, scalar_convert_montgomery)( - scalar_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} diff --git a/icicle/src/fields/extern_extension.cu b/icicle/src/fields/extern_extension.cu deleted file mode 100644 index 8bc7bc5e7..000000000 --- a/icicle/src/fields/extern_extension.cu +++ /dev/null @@ -1,22 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "utils/mont.cuh" -#include "utils/utils.h" -#include "gpu-utils/device_context.cuh" - -extern "C" void CONCAT_EXPAND(FIELD, extension_generate_scalars)(extension_t* scalars, int size) -{ - extension_t::rand_host_many(scalars, size); -} - -extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_scalar_convert_montgomery)( - extension_t* d_inout, size_t n, bool is_into, device_context::DeviceContext& ctx) -{ - if (is_into) { - return mont::to_montgomery(d_inout, n, ctx.stream, d_inout); - } else { - return mont::from_montgomery(d_inout, n, ctx.stream, d_inout); - } -} diff --git a/icicle/src/fields/ffi_extern.cpp b/icicle/src/fields/ffi_extern.cpp new file mode 100644 index 000000000..811c317a3 --- /dev/null +++ b/icicle/src/fields/ffi_extern.cpp @@ -0,0 +1,16 @@ +#include "icicle/utils/utils.h" +#include "icicle/fields/field_config.h" + +using namespace field_config; + +extern "C" void CONCAT_EXPAND(FIELD, generate_scalars)(scalar_t* scalars, int size) +{ + scalar_t::rand_host_many(scalars, size); +} + +#ifdef EXT_FIELD +extern "C" void CONCAT_EXPAND(FIELD, extension_generate_scalars)(extension_t* scalars, int size) +{ + extension_t::rand_host_many(scalars, size); +} +#endif // EXT_FIELD diff --git a/icicle/src/hash/CMakeLists.txt b/icicle/src/hash/CMakeLists.txt deleted file mode 100644 index 3f0d532c7..000000000 --- a/icicle/src/hash/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET icicle_hash) - -add_library(${TARGET} STATIC keccak/extern.cu) -target_include_directories(${TARGET} PUBLIC ${CMAKE_SOURCE_DIR}/include/) -set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME "ingo_hash") \ No newline at end of file diff --git a/icicle/src/hash/keccak/.gitignore b/icicle/src/hash/keccak/.gitignore deleted file mode 100644 index 8059bd2f2..000000000 --- a/icicle/src/hash/keccak/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -test_keccak -test_keccak_tree \ No newline at end of file diff --git a/icicle/src/hash/keccak/Makefile b/icicle/src/hash/keccak/Makefile deleted file mode 100644 index c401bf7cd..000000000 --- a/icicle/src/hash/keccak/Makefile +++ /dev/null @@ -1,10 +0,0 @@ -test_keccak_tree: test_tree.cu keccak.cu ../../merkle-tree/merkle.cu - nvcc -DMERKLE_DEBUG -o test_keccak_tree -I../../../include test_tree.cu - ./test_keccak_tree - -test_keccak: test.cu keccak.cu - nvcc -o test_keccak -I../../../include test.cu - ./test_keccak - -clear: - rm test_keccak test_keccak_tree \ No newline at end of file diff --git a/icicle/src/hash/keccak/extern.cu b/icicle/src/hash/keccak/extern.cu deleted file mode 100644 index b1e6d6aaa..000000000 --- a/icicle/src/hash/keccak/extern.cu +++ /dev/null @@ -1,49 +0,0 @@ -#include "utils/utils.h" -#include "gpu-utils/error_handler.cuh" - -#include "hash/hash.cuh" -#include "hash/keccak/keccak.cuh" -#include "keccak.cu" -#include "../../merkle-tree/merkle.cu" -#include "merkle-tree/merkle.cuh" - -namespace keccak { - extern "C" cudaError_t - keccak256_cuda(uint8_t* input, int input_block_size, int number_of_blocks, uint8_t* output, HashConfig& config) - { - return Keccak(KECCAK_256_RATE) - .hash_many(input, (uint64_t*)output, number_of_blocks, input_block_size, KECCAK_256_DIGEST, config); - } - - extern "C" cudaError_t - keccak512_cuda(uint8_t* input, int input_block_size, int number_of_blocks, uint8_t* output, HashConfig& config) - { - return Keccak(KECCAK_512_RATE) - .hash_many(input, (uint64_t*)output, number_of_blocks, input_block_size, KECCAK_512_DIGEST, config); - } - - extern "C" cudaError_t build_keccak256_merkle_tree_cuda( - const uint8_t* leaves, - uint64_t* digests, - unsigned int height, - unsigned int input_block_len, - const merkle_tree::TreeBuilderConfig& tree_config) - { - Keccak keccak(KECCAK_256_RATE); - return merkle_tree::build_merkle_tree( - leaves, digests, height, input_block_len, keccak, keccak, tree_config); - } - - extern "C" cudaError_t build_keccak512_merkle_tree_cuda( - const uint8_t* leaves, - uint64_t* digests, - unsigned int height, - unsigned int input_block_len, - const merkle_tree::TreeBuilderConfig& tree_config) - { - Keccak keccak(KECCAK_512_RATE); - return merkle_tree::build_merkle_tree( - leaves, digests, height, input_block_len, keccak, keccak, tree_config); - } - -} // namespace keccak \ No newline at end of file diff --git a/icicle/src/hash/keccak/keccak.cu b/icicle/src/hash/keccak/keccak.cu deleted file mode 100644 index e805bcf63..000000000 --- a/icicle/src/hash/keccak/keccak.cu +++ /dev/null @@ -1,256 +0,0 @@ -#include -#include "gpu-utils/device_context.cuh" -#include "gpu-utils/error_handler.cuh" -#include "gpu-utils/modifiers.cuh" - -#include "hash/hash.cuh" -#include "hash/keccak/keccak.cuh" - -using namespace hash; - -namespace keccak { - using u64 = uint64_t; - -#define ROTL64(x, y) (((x) << (y)) | ((x) >> (64 - (y)))) - -#define TH_ELT(t, c0, c1, c2, c3, c4, d0, d1, d2, d3, d4) \ - { \ - t = ROTL64((d0 ^ d1 ^ d2 ^ d3 ^ d4), 1) ^ (c0 ^ c1 ^ c2 ^ c3 ^ c4); \ - } - -#define THETA( \ - s00, s01, s02, s03, s04, s10, s11, s12, s13, s14, s20, s21, s22, s23, s24, s30, s31, s32, s33, s34, s40, s41, s42, \ - s43, s44) \ - { \ - TH_ELT(t0, s40, s41, s42, s43, s44, s10, s11, s12, s13, s14); \ - TH_ELT(t1, s00, s01, s02, s03, s04, s20, s21, s22, s23, s24); \ - TH_ELT(t2, s10, s11, s12, s13, s14, s30, s31, s32, s33, s34); \ - TH_ELT(t3, s20, s21, s22, s23, s24, s40, s41, s42, s43, s44); \ - TH_ELT(t4, s30, s31, s32, s33, s34, s00, s01, s02, s03, s04); \ - s00 ^= t0; \ - s01 ^= t0; \ - s02 ^= t0; \ - s03 ^= t0; \ - s04 ^= t0; \ - \ - s10 ^= t1; \ - s11 ^= t1; \ - s12 ^= t1; \ - s13 ^= t1; \ - s14 ^= t1; \ - \ - s20 ^= t2; \ - s21 ^= t2; \ - s22 ^= t2; \ - s23 ^= t2; \ - s24 ^= t2; \ - \ - s30 ^= t3; \ - s31 ^= t3; \ - s32 ^= t3; \ - s33 ^= t3; \ - s34 ^= t3; \ - \ - s40 ^= t4; \ - s41 ^= t4; \ - s42 ^= t4; \ - s43 ^= t4; \ - s44 ^= t4; \ - } - -#define RHOPI( \ - s00, s01, s02, s03, s04, s10, s11, s12, s13, s14, s20, s21, s22, s23, s24, s30, s31, s32, s33, s34, s40, s41, s42, \ - s43, s44) \ - { \ - t0 = ROTL64(s10, (uint64_t)1); \ - s10 = ROTL64(s11, (uint64_t)44); \ - s11 = ROTL64(s41, (uint64_t)20); \ - s41 = ROTL64(s24, (uint64_t)61); \ - s24 = ROTL64(s42, (uint64_t)39); \ - s42 = ROTL64(s04, (uint64_t)18); \ - s04 = ROTL64(s20, (uint64_t)62); \ - s20 = ROTL64(s22, (uint64_t)43); \ - s22 = ROTL64(s32, (uint64_t)25); \ - s32 = ROTL64(s43, (uint64_t)8); \ - s43 = ROTL64(s34, (uint64_t)56); \ - s34 = ROTL64(s03, (uint64_t)41); \ - s03 = ROTL64(s40, (uint64_t)27); \ - s40 = ROTL64(s44, (uint64_t)14); \ - s44 = ROTL64(s14, (uint64_t)2); \ - s14 = ROTL64(s31, (uint64_t)55); \ - s31 = ROTL64(s13, (uint64_t)45); \ - s13 = ROTL64(s01, (uint64_t)36); \ - s01 = ROTL64(s30, (uint64_t)28); \ - s30 = ROTL64(s33, (uint64_t)21); \ - s33 = ROTL64(s23, (uint64_t)15); \ - s23 = ROTL64(s12, (uint64_t)10); \ - s12 = ROTL64(s21, (uint64_t)6); \ - s21 = ROTL64(s02, (uint64_t)3); \ - s02 = t0; \ - } - -#define KHI( \ - s00, s01, s02, s03, s04, s10, s11, s12, s13, s14, s20, s21, s22, s23, s24, s30, s31, s32, s33, s34, s40, s41, s42, \ - s43, s44) \ - { \ - t0 = s00 ^ (~s10 & s20); \ - t1 = s10 ^ (~s20 & s30); \ - t2 = s20 ^ (~s30 & s40); \ - t3 = s30 ^ (~s40 & s00); \ - t4 = s40 ^ (~s00 & s10); \ - s00 = t0; \ - s10 = t1; \ - s20 = t2; \ - s30 = t3; \ - s40 = t4; \ - \ - t0 = s01 ^ (~s11 & s21); \ - t1 = s11 ^ (~s21 & s31); \ - t2 = s21 ^ (~s31 & s41); \ - t3 = s31 ^ (~s41 & s01); \ - t4 = s41 ^ (~s01 & s11); \ - s01 = t0; \ - s11 = t1; \ - s21 = t2; \ - s31 = t3; \ - s41 = t4; \ - \ - t0 = s02 ^ (~s12 & s22); \ - t1 = s12 ^ (~s22 & s32); \ - t2 = s22 ^ (~s32 & s42); \ - t3 = s32 ^ (~s42 & s02); \ - t4 = s42 ^ (~s02 & s12); \ - s02 = t0; \ - s12 = t1; \ - s22 = t2; \ - s32 = t3; \ - s42 = t4; \ - \ - t0 = s03 ^ (~s13 & s23); \ - t1 = s13 ^ (~s23 & s33); \ - t2 = s23 ^ (~s33 & s43); \ - t3 = s33 ^ (~s43 & s03); \ - t4 = s43 ^ (~s03 & s13); \ - s03 = t0; \ - s13 = t1; \ - s23 = t2; \ - s33 = t3; \ - s43 = t4; \ - \ - t0 = s04 ^ (~s14 & s24); \ - t1 = s14 ^ (~s24 & s34); \ - t2 = s24 ^ (~s34 & s44); \ - t3 = s34 ^ (~s44 & s04); \ - t4 = s44 ^ (~s04 & s14); \ - s04 = t0; \ - s14 = t1; \ - s24 = t2; \ - s34 = t3; \ - s44 = t4; \ - } - -#define IOTA(element, rc) \ - { \ - element ^= rc; \ - } - - __device__ const u64 RC[24] = {0x0000000000000001, 0x0000000000008082, 0x800000000000808a, 0x8000000080008000, - 0x000000000000808b, 0x0000000080000001, 0x8000000080008081, 0x8000000000008009, - 0x000000000000008a, 0x0000000000000088, 0x0000000080008009, 0x000000008000000a, - 0x000000008000808b, 0x800000000000008b, 0x8000000000008089, 0x8000000000008003, - 0x8000000000008002, 0x8000000000000080, 0x000000000000800a, 0x800000008000000a, - 0x8000000080008081, 0x8000000000008080, 0x0000000080000001, 0x8000000080008008}; - - __device__ void keccakf(u64 s[KECCAK_STATE_SIZE]) - { - u64 t0, t1, t2, t3, t4; - - for (int i = 0; i < 24; i++) { - THETA( - s[0], s[5], s[10], s[15], s[20], s[1], s[6], s[11], s[16], s[21], s[2], s[7], s[12], s[17], s[22], s[3], s[8], - s[13], s[18], s[23], s[4], s[9], s[14], s[19], s[24]); - RHOPI( - s[0], s[5], s[10], s[15], s[20], s[1], s[6], s[11], s[16], s[21], s[2], s[7], s[12], s[17], s[22], s[3], s[8], - s[13], s[18], s[23], s[4], s[9], s[14], s[19], s[24]); - KHI( - s[0], s[5], s[10], s[15], s[20], s[1], s[6], s[11], s[16], s[21], s[2], s[7], s[12], s[17], s[22], s[3], s[8], - s[13], s[18], s[23], s[4], s[9], s[14], s[19], s[24]); - IOTA(s[0], RC[i]); - } - } - - template - __global__ void - keccak_hash_blocks(const uint8_t* input, int input_block_size, int output_len, int number_of_blocks, uint64_t* output) - { - int sid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (sid >= number_of_blocks) { return; } - - const uint8_t* b_input = input + sid * input_block_size; - uint64_t* b_output = output + sid * output_len; - uint64_t state[KECCAK_STATE_SIZE] = {}; // Initialize with zeroes - - int input_len = input_block_size; - - // absorb - while (input_len >= R) { - for (int i = 0; i < R; i += 8) { - state[i / 8] ^= *(uint64_t*)(b_input + i); - } - keccakf(state); - b_input += R; - input_len -= R; - } - - // last block (if any) - uint8_t last_block[R]; - for (int i = 0; i < input_len; i++) { - last_block[i] = b_input[i]; - } - - // pad 10*1 - last_block[input_len] = 1; - for (int i = 0; i < R - input_len - 1; i++) { - last_block[input_len + i + 1] = 0; - } - // last bit - last_block[R - 1] |= 0x80; - - for (int i = 0; i < R; i += 8) { - state[i / 8] ^= *(uint64_t*)(last_block + i); - } - keccakf(state); - - for (int i = 0; i < output_len; i++) { - b_output[i] = state[i]; - } - } - - cudaError_t Keccak::run_hash_many_kernel( - const uint8_t* input, - uint64_t* output, - unsigned int number_of_states, - unsigned int input_len, - unsigned int output_len, - const device_context::DeviceContext& ctx) const - { - int number_of_threads = 256; - int number_of_gpu_blocks = (number_of_states - 1) / number_of_threads + 1; - - switch (rate) { - case KECCAK_256_RATE: - keccak_hash_blocks<<>>( - input, input_len, output_len, number_of_states, output); - break; - case KECCAK_512_RATE: - keccak_hash_blocks<<>>( - input, input_len, output_len, number_of_states, output); - break; - default: - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "KeccakHash: #rate must be one of [136, 72]"); - } - - CHK_IF_RETURN(cudaPeekAtLastError()); - return CHK_LAST(); - } -} // namespace keccak \ No newline at end of file diff --git a/icicle/src/hash/keccak/test.cu b/icicle/src/hash/keccak/test.cu deleted file mode 100644 index 03293f1ea..000000000 --- a/icicle/src/hash/keccak/test.cu +++ /dev/null @@ -1,67 +0,0 @@ -#include "gpu-utils/device_context.cuh" -#include "extern.cu" - -// #define DEBUG - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include -#include - -using namespace keccak; - -#define D 256 - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -void uint8_to_hex_string(const uint8_t* values, int size) -{ - std::stringstream ss; - - for (int i = 0; i < size; ++i) { - ss << std::hex << std::setw(2) << std::setfill('0') << (int)values[i]; - } - - std::string hexString = ss.str(); - std::cout << hexString << std::endl; -} - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - START_TIMER(allocation_timer); - // Prepare input data of [0, 1, 2 ... (number_of_blocks * input_block_size) - 1] - int number_of_blocks = argc > 1 ? 1 << atoi(argv[1]) : 1024; - int input_block_size = argc > 2 ? atoi(argv[2]) : 136; - - uint8_t* in_ptr = static_cast(malloc(number_of_blocks * input_block_size)); - for (uint64_t i = 0; i < number_of_blocks * input_block_size; i++) { - in_ptr[i] = (uint8_t)i; - } - - END_TIMER(allocation_timer, "Allocate mem and fill input"); - - uint8_t* out_ptr = static_cast(malloc(number_of_blocks * (D / 8))); - - START_TIMER(keccak_timer); - HashConfig config = default_hash_config(); - keccak256_cuda(in_ptr, input_block_size, number_of_blocks, out_ptr, config); - END_TIMER(keccak_timer, "Keccak") - - for (int i = 0; i < number_of_blocks; i++) { -#ifdef DEBUG - uint8_to_hex_string(out_ptr + i * (D / 8), D / 8); -#endif - } - - free(in_ptr); - free(out_ptr); -} - -#endif \ No newline at end of file diff --git a/icicle/src/hash/keccak/test_tree.cu b/icicle/src/hash/keccak/test_tree.cu deleted file mode 100644 index ed1de95fe..000000000 --- a/icicle/src/hash/keccak/test_tree.cu +++ /dev/null @@ -1,91 +0,0 @@ -#include "gpu-utils/device_context.cuh" -#include "merkle-tree/merkle.cuh" -#include "extern.cu" - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include -#include - -using namespace keccak; - -#define D 256 - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -void uint8_to_hex_string(const uint8_t* values, int size) -{ - std::stringstream ss; - - for (int i = 0; i < size; ++i) { - ss << std::hex << std::setw(2) << std::setfill('0') << (int)values[i]; - } - - std::string hexString = ss.str(); - std::cout << hexString << std::endl; -} - -#define A 2 - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - /// Tree of height N and arity A contains \sum{A^i} for i in 0..N-1 elements - uint32_t input_block_len = 136; - uint32_t tree_height = argc > 1 ? atoi(argv[1]) : 10; - uint32_t number_of_leaves = pow(A, tree_height); - uint32_t total_number_of_leaves = number_of_leaves * input_block_len; - - /// Use keep_rows to specify how many rows do you want to store - int keep_rows = argc > 2 ? atoi(argv[2]) : 7; - size_t digests_len = merkle_tree::get_digests_len(keep_rows - 1, A, 1); - - /// Fill leaves with scalars [0, 1, ... 2^tree_height - 1] - START_TIMER(timer_allocation); - uint8_t input = 0; - uint8_t* leaves = static_cast(malloc(total_number_of_leaves)); - for (uint64_t i = 0; i < total_number_of_leaves; i++) { - leaves[i] = (uint8_t)i; - } - END_TIMER(timer_allocation, "Allocated memory for leaves: "); - - /// Allocate memory for digests of {keep_rows} rows of a tree - START_TIMER(timer_digests); - size_t digests_mem = digests_len * sizeof(uint64_t); - uint64_t* digests = static_cast(malloc(digests_mem)); - END_TIMER(timer_digests, "Allocated memory for digests"); - - std::cout << "Memory for leaves = " << total_number_of_leaves / 1024 / 1024 << " MB; " - << total_number_of_leaves / 1024 / 1024 / 1024 << " GB" << std::endl; - std::cout << "Number of leaves = " << number_of_leaves << std::endl; - std::cout << "Total Number of leaves = " << total_number_of_leaves << std::endl; - std::cout << "Memory for digests = " << digests_mem / 1024 / 1024 << " MB; " << digests_mem / 1024 / 1024 / 1024 - << " GB" << std::endl; - std::cout << "Number of digest elements = " << digests_len << std::endl; - - std::cout << "Total RAM consumption = " << (digests_mem + total_number_of_leaves) / 1024 / 1024 << " MB; " - << (digests_mem + total_number_of_leaves) / 1024 / 1024 / 1024 << " GB" << std::endl; - - merkle_tree::TreeBuilderConfig config = merkle_tree::default_merkle_config(); - config.arity = A; - config.keep_rows = keep_rows; - START_TIMER(keccak_timer); - build_keccak256_merkle_tree_cuda(leaves, digests, tree_height, input_block_len, config); - END_TIMER(keccak_timer, "Keccak") - - for (int i = 0; i < digests_len; i++) { - uint64_t root = digests[i]; - std::cout << root << std::endl; - // assert(root == expected[i]); - } - free(digests); - free(leaves); -} - -#endif \ No newline at end of file diff --git a/icicle/src/matrix_ops.cpp b/icicle/src/matrix_ops.cpp new file mode 100644 index 000000000..c4fbc4354 --- /dev/null +++ b/icicle/src/matrix_ops.cpp @@ -0,0 +1,37 @@ +#include "icicle/backend/vec_ops_backend.h" +#include "icicle/dispatcher.h" + +namespace icicle { + /*********************************** TRANSPOSE ***********************************/ + ICICLE_DISPATCHER_INST(MatrixTransposeDispatcher, matrix_transpose, scalarMatrixOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, matrix_transpose)( + const scalar_t* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig* config, scalar_t* mat_out) + { + return MatrixTransposeDispatcher::execute(mat_in, nof_rows, nof_cols, *config, mat_out); + } + + template <> + eIcicleError matrix_transpose( + const scalar_t* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, scalar_t* mat_out) + { + return CONCAT_EXPAND(FIELD, matrix_transpose)(mat_in, nof_rows, nof_cols, &config, mat_out); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(extFieldMatrixTransposeDispatcher, extension_matrix_transpose, extFieldMatrixOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_matrix_transpose)( + const extension_t* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig* config, extension_t* mat_out) + { + return extFieldMatrixTransposeDispatcher::execute(mat_in, nof_rows, nof_cols, *config, mat_out); + } + + template <> + eIcicleError matrix_transpose( + const extension_t* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, extension_t* mat_out) + { + return CONCAT_EXPAND(FIELD, extension_matrix_transpose)(mat_in, nof_rows, nof_cols, &config, mat_out); + } +#endif // EXT_FIELD +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/merkle-tree/.gitignore b/icicle/src/merkle-tree/.gitignore deleted file mode 100644 index ea04aba29..000000000 --- a/icicle/src/merkle-tree/.gitignore +++ /dev/null @@ -1 +0,0 @@ -test_merkle diff --git a/icicle/src/merkle-tree/extern.cu b/icicle/src/merkle-tree/extern.cu deleted file mode 100644 index 060afe870..000000000 --- a/icicle/src/merkle-tree/extern.cu +++ /dev/null @@ -1,25 +0,0 @@ -#include "utils/utils.h" - -#include "gpu-utils/error_handler.cuh" -#include "merkle-tree/merkle.cuh" -#include "merkle.cu" - -#include "hash/hash.cuh" - -#include "fields/field_config.cuh" -using namespace field_config; - -namespace merkle_tree { - extern "C" cudaError_t CONCAT_EXPAND(FIELD, build_merkle_tree)( - const scalar_t* leaves_digests, - scalar_t* digests, - unsigned int height, - unsigned int input_block_len, - const hash::Hasher* compression, - const hash::Hasher* bottom_layer, - const TreeBuilderConfig& tree_config) - { - return build_merkle_tree( - leaves_digests, digests, height, input_block_len, *compression, *bottom_layer, tree_config); - } -} // namespace merkle_tree \ No newline at end of file diff --git a/icicle/src/merkle-tree/extern_mmcs.cu b/icicle/src/merkle-tree/extern_mmcs.cu deleted file mode 100644 index 3b6fc5ccb..000000000 --- a/icicle/src/merkle-tree/extern_mmcs.cu +++ /dev/null @@ -1,26 +0,0 @@ -#include "utils/utils.h" - -#include "gpu-utils/error_handler.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "mmcs.cu" - -#include "hash/hash.cuh" - -#include "fields/field_config.cuh" -using namespace field_config; - -using matrix::Matrix; - -namespace merkle_tree { - extern "C" cudaError_t CONCAT_EXPAND(FIELD, mmcs_commit_cuda)( - const Matrix* leaves, - unsigned int number_of_inputs, - scalar_t* digests, - const hash::Hasher* hasher, - const hash::Hasher* compression, - const TreeBuilderConfig& tree_config) - { - return mmcs_commit(leaves, number_of_inputs, digests, *hasher, *compression, tree_config); - } -} // namespace merkle_tree \ No newline at end of file diff --git a/icicle/src/merkle-tree/merkle.cu b/icicle/src/merkle-tree/merkle.cu deleted file mode 100644 index 53f3b8f6f..000000000 --- a/icicle/src/merkle-tree/merkle.cu +++ /dev/null @@ -1,331 +0,0 @@ -#include "hash/hash.cuh" -#include "merkle-tree/merkle.cuh" - -namespace merkle_tree { - /// Constructs merkle subtree without parallelization - /// The digests are aligned sequentially per row - /// Example: - /// - /// Big tree: - /// - /// 1 <- Root - /// / \ <- Arity = 2 - /// 2 3 <- Digests - /// / \ / \ <- Height = 2 (as the number of edges) - /// 4 5 6 7 <- height^arity leaves - /// | | | | <- Bottom layer hash 1 to 1 - /// a b c d <- Input vector 1x4 - /// - /// Subtree 1 Subtree 2 - /// 2 3 - /// / \ / \ - /// 4 5 6 7 - /// - /// Digests array for subtree 1: - /// [4 5 . . 2 . .] - /// | | | - /// ----- V - /// | Segment (offset = 4, subtree_idx = 0) - /// v - /// Segment (offset = 0, subtree_idx = 0) - /// - /// Digests array for subtree 2: - /// [. . 6 7 . 3 .] - /// | | - /// ----- - /// | - /// v - /// Segment (offset = 0, subtree_idx = 1) - /// - /// Total digests array: - /// [4 5 6 7 2 3 .] - /// - /// Example for custom config: - /// - /// arity = 2 - /// input_block_len = 2 - /// digest_elements = 2 - /// bottom_layer hash width = 4 - /// compression width = 4 - /// height = 2 - /// - /// [a, b] <- Root of the tree - /// | | - /// [a, b, c, d] - /// / \ / \ - /// [i, j, m, n] - /// ┌──┬──────┴──┴──┴──┴──────┬──┐ - /// | | | | - /// [i, j, k, l] [m, n, o, p] <- compression states - /// / \ / \ / \ / \ <- Running permutation - /// [1, 2, 5, 6] [9, 1, 4, 5] <- compression states - /// ┌──┬───┴──┴──┼──┤ ┌──┬───┴──┴──┼──┤ - /// | | | | | | | | <- digest_element * height^arity leaves - /// [1, 2, 3, 4] [5, 6, 7, 8] [9, 1, 2, 3] [4, 5, 6, 7] <- Permuted states - /// / \ / \ / \ / \ / \ / \ / \ / \ <- Running permutation - /// [a, b, 0, 0] [c, d, 0, 0] [e, f, 0, 0] [g, h, 0, 0] <- States of the bottom layer hash - /// | | | | | | | | <- Bottom layer hash 2 to 2 - /// a b c d e f g h <- Input vector 2x4 - /// - /// Input matrix: - /// ┌ ┐ - /// | a b | - /// | c d | - /// | e f | - /// | g h | - /// └ ┘ - - template - cudaError_t build_merkle_subtree( - const L* leaves, - L* d_leaves, - D* digests, - size_t subtree_idx, - size_t subtree_height, - D* big_tree_digests, - size_t start_segment_size, - size_t start_segment_offset, - uint64_t keep_rows, - uint64_t input_block_len, - const Hasher& bottom_layer, - const Hasher& compression, - const TreeBuilderConfig& tree_config, - device_context::DeviceContext& ctx) - { - uint64_t arity = tree_config.arity; - - HashConfig hash_config = default_hash_config(ctx); - hash_config.are_inputs_on_device = true; - hash_config.are_outputs_on_device = true; - hash_config.is_async = true; - - size_t bottom_layer_states = pow(arity, subtree_height); - - if (!tree_config.are_inputs_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync( - d_leaves, leaves, bottom_layer_states * input_block_len * sizeof(L), cudaMemcpyHostToDevice, ctx.stream)); - } - - bottom_layer.hash_many( - tree_config.are_inputs_on_device ? leaves : d_leaves, digests, bottom_layer_states, input_block_len, - tree_config.digest_elements, hash_config); - - uint64_t number_of_states = bottom_layer_states / arity; - size_t segment_size = start_segment_size; - size_t segment_offset = start_segment_offset; - - if (!keep_rows || subtree_height < keep_rows) { - D* digests_with_offset = big_tree_digests + segment_offset + subtree_idx * bottom_layer_states; - CHK_IF_RETURN(cudaMemcpyAsync( - digests_with_offset, digests, bottom_layer_states * tree_config.digest_elements * sizeof(D), - cudaMemcpyDeviceToHost, ctx.stream)); - segment_offset += segment_size; - } - segment_size /= arity; - subtree_height--; - - D* prev_layer = digests; - D* next_layer = (D*)d_leaves; - - while (number_of_states > 0) { - CHK_IF_RETURN(compression.run_hash_many_kernel( - (L*)prev_layer, next_layer, number_of_states, tree_config.digest_elements * tree_config.arity, - tree_config.digest_elements, hash_config.ctx)); - - if (!keep_rows || subtree_height < keep_rows) { - D* digests_with_offset = - big_tree_digests + segment_offset + subtree_idx * number_of_states * tree_config.digest_elements; - CHK_IF_RETURN(cudaMemcpyAsync( - digests_with_offset, next_layer, number_of_states * tree_config.digest_elements * sizeof(D), - cudaMemcpyDeviceToHost, ctx.stream)); - segment_offset += segment_size; - } - swap(&prev_layer, &next_layer); - segment_size /= arity; - subtree_height--; - number_of_states /= arity; - } - - return CHK_LAST(); - } - - template - cudaError_t build_merkle_tree( - const L* leaves, - D* digests, - unsigned int height, - unsigned int input_block_len, - const Hasher& compression, - const Hasher& bottom_layer, - const TreeBuilderConfig& tree_config) - { - CHK_INIT_IF_RETURN(); - cudaStream_t& stream = tree_config.ctx.stream; - - if (compression.preimage_max_length < tree_config.arity * tree_config.digest_elements) - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, - "Hash max preimage length does not match merkle tree arity multiplied by digest elements"); - - uint64_t number_of_bottom_layer_states = pow(tree_config.arity, height); - - // This will determine how much splitting do we need to do - // `number_of_streams` subtrees should fit in the device - // This means each subtree should fit in `STREAM_CHUNK_SIZE` memory - uint64_t number_of_subtrees = 1; - uint64_t subtree_height = height; - uint64_t subtree_bottom_layer_states = number_of_bottom_layer_states; - uint64_t subtree_leaves_size = subtree_bottom_layer_states * input_block_len; - uint64_t subtree_digests_size = subtree_bottom_layer_states * tree_config.digest_elements; - - size_t subtree_d_leaves_memory = std::max( - tree_config.are_inputs_on_device ? 0 : (sizeof(L) * subtree_leaves_size), - subtree_digests_size * sizeof(D) / tree_config.arity); - size_t subtree_memory_required = sizeof(D) * subtree_digests_size + subtree_d_leaves_memory; - while (subtree_memory_required > STREAM_CHUNK_SIZE) { - number_of_subtrees *= tree_config.arity; - subtree_height--; - subtree_bottom_layer_states /= tree_config.arity; - subtree_digests_size /= tree_config.arity; - subtree_leaves_size /= tree_config.arity; - subtree_d_leaves_memory /= tree_config.arity; - subtree_memory_required = sizeof(D) * subtree_digests_size + subtree_d_leaves_memory; - } - int cap_height = height - subtree_height; - size_t caps_len = pow(tree_config.arity, cap_height) * tree_config.digest_elements; - - size_t available_memory, _total_memory; - CHK_IF_RETURN(cudaMemGetInfo(&available_memory, &_total_memory)); - available_memory -= GIGA / 8; // Leave 128 MB just in case - - // We can effectively parallelize memory copy with streams - // as long as they don't operate on more than `STREAM_CHUNK_SIZE` bytes - const size_t number_of_streams = std::min((uint64_t)(available_memory / STREAM_CHUNK_SIZE), number_of_subtrees); - cudaStream_t* streams = static_cast(malloc(sizeof(cudaStream_t) * number_of_streams)); - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamCreate(&streams[i])); - } - - bool caps_mode = tree_config.keep_rows && tree_config.keep_rows <= cap_height; - D* caps; - if (caps_mode) { caps = static_cast(malloc(caps_len * sizeof(D))); } - -#ifdef MERKLE_DEBUG - std::cout << "Available memory = " << available_memory / 1024 / 1024 << " MB" << std::endl; - std::cout << "Number of streams = " << number_of_streams << std::endl; - std::cout << "Number of subtrees = " << number_of_subtrees << std::endl; - std::cout << "Height of a subtree = " << subtree_height << std::endl; - std::cout << "Cutoff height = " << height - subtree_height << std::endl; - std::cout << "Number of leaves in a subtree = " << subtree_bottom_layer_states << std::endl; - std::cout << "Digest elements for a subtree = " << subtree_digests_size << std::endl; - std::cout << "Size of 1 subtree digests = " << subtree_digests_size * sizeof(D) / 1024 / 1024 << " MB" << std::endl; - std::cout << "Cap height = " << cap_height << std::endl; - std::cout << "Enabling caps mode? " << caps_mode << std::endl; - std::cout << "Allocated " << subtree_d_leaves_memory << " bytes for d_leaves" << std::endl; -#endif - - // Allocate memory for the leaves and digests - // These are shared by streams in a pool - L* d_leaves_ptr; - D* digests_ptr; - CHK_IF_RETURN(cudaMallocAsync(&d_leaves_ptr, subtree_d_leaves_memory * number_of_streams, stream)); - CHK_IF_RETURN(cudaMallocAsync(&digests_ptr, subtree_digests_size * number_of_streams * sizeof(D), stream)); - // Wait for these allocations to finish - CHK_IF_RETURN(cudaStreamSynchronize(stream)); - - // Build subtrees in parallel. This for loop invokes kernels that can run in a pool of size `number_of_streams` - for (size_t subtree_idx = 0; subtree_idx < number_of_subtrees; subtree_idx++) { - size_t stream_idx = subtree_idx % number_of_streams; - cudaStream_t subtree_stream = streams[stream_idx]; - - const L* subtree_leaves = leaves + subtree_idx * subtree_bottom_layer_states * input_block_len; - L* subtree_d_leaves = (L*)((unsigned char*)d_leaves_ptr + stream_idx * subtree_d_leaves_memory); - D* subtree_digests = digests_ptr + stream_idx * subtree_digests_size; - - int subtree_keep_rows = 0; - if (tree_config.keep_rows) { - int diff = tree_config.keep_rows - cap_height; - subtree_keep_rows = std::max(1, diff); - } - device_context::DeviceContext subtree_context{subtree_stream, tree_config.ctx.device_id, tree_config.ctx.mempool}; - - uint64_t start_segment_size = number_of_bottom_layer_states * tree_config.digest_elements; - cudaError_t subtree_result = build_merkle_subtree( - subtree_leaves, // leaves - subtree_d_leaves, // d_leves - subtree_digests, // digests - subtree_idx, // subtree_idx - subtree_height, // subtree_height - caps_mode ? caps : digests, // big_tree_digests - start_segment_size, // start_segment_size - 0, // start_segment_offset - subtree_keep_rows, // keep_rows - input_block_len, // input_block_len - bottom_layer, // bottom_layer - compression, // compression - tree_config, // tree_config - subtree_context // subtree_context - ); - CHK_IF_RETURN(subtree_result); - } - - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamSynchronize(streams[i])); - } - - // Finish the top-level tree if any - if (cap_height > 0) { - size_t start_segment_size = caps_len / tree_config.arity; - size_t start_segment_offset = 0; - if (!caps_mode) { // Calculate offset - size_t keep_rows = tree_config.keep_rows ? tree_config.keep_rows : height + 1; - size_t layer_size = pow(tree_config.arity, keep_rows - 1) * tree_config.digest_elements; - for (int i = 0; i < keep_rows - cap_height; i++) { - start_segment_offset += layer_size; - layer_size /= tree_config.arity; - } - } - CHK_IF_RETURN(cudaMemcpyAsync( - d_leaves_ptr, caps_mode ? caps : (digests + start_segment_offset - caps_len), caps_len * sizeof(D), - (caps_mode || !tree_config.are_outputs_on_device) ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice, stream)); - - uint64_t number_of_states = caps_len / tree_config.arity / tree_config.digest_elements; - - D* prev_layer = (D*)d_leaves_ptr; - D* next_layer = digests_ptr; - - size_t segment_size = start_segment_size; - size_t segment_offset = start_segment_offset; - while (number_of_states > 0) { - CHK_IF_RETURN(compression.run_hash_many_kernel( - (L*)prev_layer, next_layer, number_of_states, tree_config.digest_elements * tree_config.arity, - tree_config.digest_elements, tree_config.ctx)); - if (!tree_config.keep_rows || cap_height < tree_config.keep_rows + (int)caps_mode) { - D* digests_with_offset = digests + segment_offset; - CHK_IF_RETURN(cudaMemcpyAsync( - digests_with_offset, next_layer, number_of_states * tree_config.digest_elements * sizeof(D), - cudaMemcpyDeviceToHost, stream)); - segment_offset += segment_size; - } - - swap(&prev_layer, &next_layer); - - segment_size /= tree_config.arity; - cap_height--; - number_of_states /= tree_config.arity; - } - if (caps_mode) { free(caps); } - } - - CHK_IF_RETURN(cudaFreeAsync(d_leaves_ptr, stream)); - CHK_IF_RETURN(cudaFreeAsync(digests_ptr, stream)); - if (!tree_config.is_async) return CHK_STICKY(cudaStreamSynchronize(stream)); - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamSynchronize(streams[i])); - CHK_IF_RETURN(cudaStreamDestroy(streams[i])); - } - free(streams); - return CHK_LAST(); - } - -} // namespace merkle_tree \ No newline at end of file diff --git a/icicle/src/merkle-tree/mmcs.cu b/icicle/src/merkle-tree/mmcs.cu deleted file mode 100644 index f7acbb653..000000000 --- a/icicle/src/merkle-tree/mmcs.cu +++ /dev/null @@ -1,456 +0,0 @@ -#include "hash/hash.cuh" -#include "merkle-tree/merkle.cuh" -#include "matrix/matrix.cuh" -#include "vec_ops/vec_ops.cuh" - -#include - -using matrix::Matrix; - -namespace merkle_tree { - - template - cudaError_t hash_leaves( - const Matrix* leaves, - unsigned int number_of_inputs, - uint64_t number_of_rows, - D* digests, - unsigned int digest_elements, - const Hasher& hasher, - const device_context::DeviceContext& ctx) - { - HashConfig sponge_config = default_hash_config(ctx); - sponge_config.are_inputs_on_device = true; - sponge_config.are_outputs_on_device = true; - sponge_config.is_async = true; - - uint64_t number_of_rows_padded = next_pow_of_two(number_of_rows); - - CHK_IF_RETURN(hasher.hash_2d(leaves, digests, number_of_inputs, digest_elements, number_of_rows, ctx)); - - if (number_of_rows_padded - number_of_rows) { - // Pad with default digests - cudaMemsetAsync( - (void*)(digests + number_of_rows), 0, (number_of_rows_padded - number_of_rows) * digest_elements * sizeof(D), - ctx.stream); - } - - return CHK_LAST(); - } - - template - struct SubtreeParams { - unsigned int number_of_inputs; // Number of input matrices - unsigned int arity; // Arity of the tree - unsigned int digest_elements; // Number of output elements per hash - size_t number_of_rows; // Current number of input rows to operate on - size_t number_of_rows_padded; // next power of arity for number_of_rows - size_t subtree_idx; // The subtree id - size_t number_of_subtrees; // Total number of subtrees - uint64_t subtree_height; // Height of one subtree - - /// One segment corresponds to one layer of output digests - size_t segment_size; // The size of current segment. - size_t segment_offset; // An offset for the current segment - unsigned int leaves_offset; // An offset in the sorted list of input matrices - unsigned int number_of_leaves_to_inject; // Number of leaves to inject in current level - unsigned int keep_rows; // Number of rows to keep - bool are_inputs_on_device; - bool caps_mode; - const Hasher* hasher = nullptr; - const Hasher* compression = nullptr; - const device_context::DeviceContext* ctx = nullptr; - }; - - template - cudaError_t slice_and_copy_leaves( - const std::vector>& leaves, L* d_leaves, Matrix* d_leaves_info, SubtreeParams& params) - { - uint64_t target_height = params.number_of_rows_padded * params.number_of_subtrees; - params.number_of_leaves_to_inject = 0; - while (params.leaves_offset < params.number_of_inputs && - next_pow_of_two(leaves[params.leaves_offset].height) >= target_height) { - if (next_pow_of_two(leaves[params.leaves_offset].height) == target_height) params.number_of_leaves_to_inject++; - params.leaves_offset++; - } - - if (params.number_of_leaves_to_inject) { - size_t rows_offset = params.subtree_idx * params.number_of_rows_padded; - size_t actual_layer_rows = leaves[params.leaves_offset - params.number_of_leaves_to_inject].height; - params.number_of_rows = std::min(actual_layer_rows - rows_offset, params.number_of_rows_padded); - - Matrix* leaves_info = static_cast*>(malloc(params.number_of_leaves_to_inject * sizeof(Matrix))); - L* d_leaves_ptr = d_leaves; - for (auto i = 0; i < params.number_of_leaves_to_inject; i++) { - Matrix leaf = leaves[params.leaves_offset - params.number_of_leaves_to_inject + i]; - if (!params.are_inputs_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync( - d_leaves_ptr, leaf.values + rows_offset * leaf.width, params.number_of_rows * leaf.width * sizeof(L), - cudaMemcpyHostToDevice, params.ctx->stream)); - } else { - d_leaves_ptr = leaf.values + rows_offset * leaf.width; - } - - leaves_info[i] = {d_leaves_ptr, leaf.width, params.number_of_rows}; - d_leaves_ptr += params.number_of_rows * leaf.width; - } - CHK_IF_RETURN(cudaMemcpyAsync( - d_leaves_info, leaves_info, params.number_of_leaves_to_inject * sizeof(Matrix), cudaMemcpyHostToDevice, - params.ctx->stream)); - free(leaves_info); - } - - return CHK_LAST(); - } - - /// Checks if the current row needs to be copied out to the resulting digests array - /// Computes the needed offsets using segments model - template - cudaError_t maybe_copy_digests(D* digests, L* big_tree_digests, SubtreeParams& params) - { - if (!params.keep_rows || params.subtree_height < params.keep_rows + (int)params.caps_mode) { - D* digests_with_offset = big_tree_digests + params.segment_offset + - params.subtree_idx * params.number_of_rows_padded * params.digest_elements; - CHK_IF_RETURN(cudaMemcpyAsync( - digests_with_offset, digests, params.number_of_rows_padded * params.digest_elements * sizeof(D), - cudaMemcpyDeviceToHost, params.ctx->stream)); - params.segment_offset += params.segment_size; - } - return CHK_LAST(); - } - - template - cudaError_t fold_layer( - const std::vector>& leaves, - D* prev_layer, - D* next_layer, - L* aux_leaves_mem, - Matrix* d_leaves_info, - SubtreeParams& params) - { - CHK_IF_RETURN(slice_and_copy_leaves(leaves, aux_leaves_mem, d_leaves_info, params)); - - if (params.number_of_leaves_to_inject) { - CHK_IF_RETURN(params.compression->compress_and_inject( - d_leaves_info, params.number_of_leaves_to_inject, params.number_of_rows, prev_layer, next_layer, - params.digest_elements, *params.ctx)); - } else { - CHK_IF_RETURN(params.compression->run_hash_many_kernel( - prev_layer, next_layer, params.number_of_rows_padded, params.compression->width, params.digest_elements, - *params.ctx)); - } - - return CHK_LAST(); - } - - template - cudaError_t build_mmcs_subtree( - const std::vector>& leaves, - L* d_leaves, - D* states, - L* aux_leaves_mem, - L* big_tree_digests, - SubtreeParams& params) - { - // Leaves info - Matrix* d_leaves_info; - CHK_IF_RETURN(cudaMallocAsync(&d_leaves_info, params.number_of_inputs * sizeof(Matrix), params.ctx->stream)); - - CHK_IF_RETURN(slice_and_copy_leaves(leaves, d_leaves, d_leaves_info, params)); - - // Reuse leaves memory - D* digests = (D*)d_leaves; - - CHK_IF_RETURN(hash_leaves( - d_leaves_info, params.number_of_leaves_to_inject, params.number_of_rows, states, params.digest_elements, - *params.hasher, *params.ctx)); - - CHK_IF_RETURN(maybe_copy_digests(digests, big_tree_digests, params)); - - params.number_of_rows_padded /= params.arity; - params.segment_size /= params.arity; - params.subtree_height--; - - D* prev_layer = states; - D* next_layer = digests; - while (params.number_of_rows_padded > 0) { - CHK_IF_RETURN(fold_layer(leaves, prev_layer, next_layer, aux_leaves_mem, d_leaves_info, params)); - CHK_IF_RETURN(maybe_copy_digests(next_layer, big_tree_digests, params)); - swap(&prev_layer, &next_layer); - params.segment_size /= params.arity; - params.subtree_height--; - params.number_of_rows_padded /= params.arity; - } - return CHK_LAST(); - } - - template - cudaError_t mmcs_commit( - const Matrix* inputs, - const unsigned int number_of_inputs, - D* digests, - const Hasher& hasher, - const Hasher& compression, - const TreeBuilderConfig& tree_config) - { - CHK_INIT_IF_RETURN(); - cudaStream_t& stream = tree_config.ctx.stream; - - if (number_of_inputs == 0) THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "No matrices provided"); - - if (compression.preimage_max_length < tree_config.arity * tree_config.digest_elements) - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, - "Hash max preimage length does not match merkle tree arity multiplied by digest elements"); - - std::vector> sorted_inputs(number_of_inputs); - std::partial_sort_copy( - inputs, inputs + number_of_inputs, sorted_inputs.begin(), sorted_inputs.end(), - [](const Matrix& left, const Matrix& right) { return left.height > right.height; }); - - // Check that the height of any two given matrices either rounds up - // to the same next power of two or otherwise equal - for (unsigned int i = 0; i < number_of_inputs - 1; i++) { - unsigned int left = sorted_inputs[i].height; - unsigned int right = sorted_inputs[i + 1].height; - - if (next_pow_of_two(left) == next_pow_of_two(right) && left != right) - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "Matrix heights that round up to the same power of two must be equal"); - } - - uint64_t max_height = sorted_inputs[0].height; - - // Calculate maximum additional memory needed for injected matrices - uint64_t max_aux_total_elements = 0; - uint64_t current_aux_total_elements = 0; - uint64_t current_height = 0; - uint64_t bottom_layer_leaves_elements = 0; - if (!tree_config.are_inputs_on_device) { - for (auto it = sorted_inputs.begin(); it < sorted_inputs.end(); it++) { - if (it->height == max_height) { - bottom_layer_leaves_elements += it->height * it->width; - continue; - } - - if (it->height != current_height) { - current_height = it->height; - current_aux_total_elements = 0; - } - - current_aux_total_elements += it->width * it->height; - if (current_aux_total_elements > max_aux_total_elements) { - max_aux_total_elements = current_aux_total_elements; - } - } - } - - uint64_t number_of_bottom_layer_rows = next_pow_of_two(max_height); - size_t leaves_info_memory = number_of_inputs * sizeof(Matrix); - - unsigned int tree_height = get_height(number_of_bottom_layer_rows); - - // This will determine how much splitting do we need to do - // `number_of_streams` subtrees should fit in the device - // This means each subtree should fit in `STREAM_CHUNK_SIZE` memory - uint64_t number_of_subtrees = 1; - uint64_t subtree_height = tree_height; - uint64_t subtree_bottom_layer_rows = number_of_bottom_layer_rows; - uint64_t subtree_states_size = subtree_bottom_layer_rows * hasher.width; - uint64_t subtree_digests_size = subtree_bottom_layer_rows * tree_config.digest_elements; - uint64_t subtree_leaves_elements = 0; - for (int i = 0; i < number_of_inputs && sorted_inputs[i].height == max_height; i++) { - subtree_leaves_elements += sorted_inputs[i].width * sorted_inputs[i].height; - } - uint64_t subtree_aux_elements = max_aux_total_elements; - - size_t subtree_leaves_memory = std::max(subtree_leaves_elements * sizeof(L), subtree_digests_size * sizeof(D)); - size_t subtree_memory_required = - sizeof(D) * subtree_states_size + subtree_leaves_memory + subtree_aux_elements * sizeof(L) + leaves_info_memory; - while (subtree_memory_required > STREAM_CHUNK_SIZE) { - number_of_subtrees *= tree_config.arity; - subtree_height--; - subtree_bottom_layer_rows /= tree_config.arity; - subtree_states_size /= tree_config.arity; - subtree_digests_size /= tree_config.arity; - subtree_leaves_elements /= tree_config.arity; - subtree_aux_elements /= tree_config.arity; - subtree_leaves_memory = std::max(subtree_leaves_elements * sizeof(L), subtree_digests_size * sizeof(D)); - subtree_memory_required = - sizeof(D) * subtree_states_size + subtree_leaves_memory + subtree_aux_elements * sizeof(L) + leaves_info_memory; - } - unsigned int cap_height = tree_height - subtree_height; - size_t caps_len = pow(tree_config.arity, cap_height) * tree_config.digest_elements; - - size_t available_memory, _total_memory; - CHK_IF_RETURN(cudaMemGetInfo(&available_memory, &_total_memory)); - if (available_memory < (GIGA / 8 + STREAM_CHUNK_SIZE)) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, - "Not enough GPU memory to build a tree. At least 1.125 GB of GPU memory required"); - } - available_memory -= GIGA / 8; // Leave 128 MB just in case - - // We can effectively parallelize memory copy with streams - // as long as they don't operate on more than `STREAM_CHUNK_SIZE` bytes - const size_t number_of_streams = std::min((uint64_t)(available_memory / STREAM_CHUNK_SIZE), number_of_subtrees); - std::vector streams(number_of_streams); - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamCreate(&streams[i])); - } - - // If keep_rows is smaller then the remaining top-tree height - // we need to allocate additional memory to store the roots - // of subtrees, in order to proceed from there - bool caps_mode = tree_config.keep_rows && tree_config.keep_rows <= cap_height; - D* caps; - if (caps_mode) { caps = static_cast(malloc(caps_len * sizeof(D))); } - -#ifdef MERKLE_DEBUG - std::cout << "MMCS DEBUG" << std::endl; - std::cout << "====================================" << std::endl; - std::cout << "Available memory = " << available_memory / 1024 / 1024 << " MB" << std::endl; - std::cout << "Number of streams = " << number_of_streams << std::endl; - std::cout << "Number of subtrees = " << number_of_subtrees << std::endl; - std::cout << "Height of a subtree = " << subtree_height << std::endl; - std::cout << "Cutoff height = " << tree_height - subtree_height << std::endl; - std::cout << "Number of leaves in a subtree = " << subtree_bottom_layer_rows << std::endl; - std::cout << "State of a subtree = " << subtree_states_size << std::endl; - std::cout << "Digest elements for a subtree = " << subtree_digests_size << std::endl; - std::cout << "Size of 1 subtree states = " << subtree_states_size * sizeof(D) / 1024 / 1024 << " MB" << std::endl; - std::cout << "Size of 1 subtree digests = " << subtree_digests_size * sizeof(D) / 1024 / 1024 << " MB" << std::endl; - std::cout << "Cap height = " << cap_height << std::endl; - std::cout << "Enabling caps mode? " << caps_mode << std::endl; - - std::cout << "Allocating " << subtree_states_size * number_of_streams << " elements for states" << std::endl; - std::cout << "Allocating " << subtree_leaves_memory * number_of_streams << " bytes for leaves" << std::endl; - std::cout << "Allocating " << subtree_aux_elements * number_of_streams << " elements for aux leaves" << std::endl; - std::cout << std::endl; -#endif - - // Allocate memory for the states, injected leaves (aux) and digests - // These are shared by streams in a pool - D* states_ptr; - L *aux_ptr, *leaves_ptr; - CHK_IF_RETURN(cudaMallocAsync(&states_ptr, subtree_states_size * number_of_streams * sizeof(D), stream)); - CHK_IF_RETURN(cudaMemsetAsync(states_ptr, 0, subtree_states_size * number_of_streams * sizeof(D), stream)); - CHK_IF_RETURN(cudaMallocAsync(&leaves_ptr, subtree_leaves_memory * number_of_streams, stream)); - CHK_IF_RETURN(cudaMallocAsync(&aux_ptr, subtree_aux_elements * number_of_streams * sizeof(L), stream)); - // Wait for these allocations to finish - CHK_IF_RETURN(cudaStreamSynchronize(stream)); - - // Build subtrees in parallel. This for loop invokes kernels that can run in a pool of size `number_of_streams` - for (size_t subtree_idx = 0; subtree_idx < number_of_subtrees; subtree_idx++) { - size_t stream_idx = subtree_idx % number_of_streams; - cudaStream_t subtree_stream = streams[stream_idx]; - - D* subtree_state = states_ptr + stream_idx * subtree_states_size; - L* subtree_leaves = (L*)((unsigned char*)leaves_ptr + stream_idx * subtree_leaves_memory); - L* subtree_aux = aux_ptr + stream_idx * subtree_aux_elements; - - unsigned int subtree_keep_rows = 0; - if (tree_config.keep_rows) { - int diff = tree_config.keep_rows - cap_height; - subtree_keep_rows = std::max(1, diff); - } - device_context::DeviceContext subtree_context{subtree_stream, tree_config.ctx.device_id, tree_config.ctx.mempool}; - - SubtreeParams params = {}; - - params.number_of_inputs = number_of_inputs; - params.arity = tree_config.arity; - params.digest_elements = tree_config.digest_elements; - params.number_of_rows = subtree_bottom_layer_rows; - params.number_of_rows_padded = subtree_bottom_layer_rows; - - params.subtree_idx = subtree_idx; - params.subtree_height = subtree_height; - params.number_of_subtrees = number_of_subtrees; - - params.segment_size = number_of_bottom_layer_rows * tree_config.digest_elements; - params.keep_rows = subtree_keep_rows; - params.are_inputs_on_device = tree_config.are_inputs_on_device; - params.hasher = &hasher; - params.compression = &compression; - params.ctx = &subtree_context; - - cudaError_t subtree_result = build_mmcs_subtree( - sorted_inputs, - subtree_leaves, // d_leaves - subtree_state, // states - subtree_aux, // aux_leaves_mem - caps_mode ? caps : digests, // big_tree_digests - params // params - ); - CHK_IF_RETURN(subtree_result); - } - - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamSynchronize(streams[i])); - } - - // Finish the top-level tree if any - if (cap_height > 0) { - D* digests_ptr = (D*)leaves_ptr; - size_t start_segment_size = caps_len / tree_config.arity; - size_t start_segment_offset = 0; - if (!caps_mode) { // Calculate offset - size_t keep_rows = tree_config.keep_rows ? tree_config.keep_rows : tree_height + 1; - size_t layer_size = pow(tree_config.arity, keep_rows - 1) * tree_config.digest_elements; - for (int i = 0; i < keep_rows - cap_height; i++) { - start_segment_offset += layer_size; - layer_size /= tree_config.arity; - } - } - - CHK_IF_RETURN(cudaMemcpyAsync( - states_ptr, caps_mode ? caps : (digests + start_segment_offset - caps_len), caps_len * sizeof(D), - (caps_mode || !tree_config.are_outputs_on_device) ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice, stream)); - - uint64_t number_of_states = caps_len / tree_config.arity / tree_config.digest_elements; - Matrix* d_leaves_info; - CHK_IF_RETURN(cudaMallocAsync(&d_leaves_info, number_of_inputs * sizeof(Matrix), tree_config.ctx.stream)); - - SubtreeParams top_params = {}; - - top_params.number_of_inputs = number_of_inputs; - top_params.arity = tree_config.arity; - top_params.digest_elements = tree_config.digest_elements; - top_params.number_of_rows = number_of_states; - top_params.number_of_rows_padded = number_of_states; - - top_params.subtree_height = cap_height; - top_params.number_of_subtrees = 1; - - top_params.segment_offset = start_segment_offset; - top_params.segment_size = start_segment_size; - top_params.keep_rows = tree_config.keep_rows; - top_params.are_inputs_on_device = tree_config.are_inputs_on_device; - top_params.caps_mode = caps_mode; - top_params.hasher = &hasher; - top_params.compression = &compression; - top_params.ctx = &tree_config.ctx; - - D* prev_layer = states_ptr; - D* next_layer = digests_ptr; - while (top_params.number_of_rows_padded > 0) { - CHK_IF_RETURN(fold_layer(sorted_inputs, prev_layer, next_layer, aux_ptr, d_leaves_info, top_params)); - CHK_IF_RETURN(maybe_copy_digests(next_layer, digests, top_params)); - swap(&prev_layer, &next_layer); - top_params.segment_size /= top_params.arity; - top_params.subtree_height--; - top_params.number_of_rows_padded /= top_params.arity; - } - } - - if (caps_mode) { free(caps); } - CHK_IF_RETURN(cudaFreeAsync(states_ptr, stream)); - CHK_IF_RETURN(cudaFreeAsync(leaves_ptr, stream)); - for (size_t i = 0; i < number_of_streams; i++) { - CHK_IF_RETURN(cudaStreamDestroy(streams[i])); - } - if (!tree_config.is_async) return CHK_STICKY(cudaStreamSynchronize(stream)); - return CHK_LAST(); - } - -} // namespace merkle_tree diff --git a/icicle/src/merkle-tree/tests/merkle/.gitignore b/icicle/src/merkle-tree/tests/merkle/.gitignore deleted file mode 100644 index b3a0cf9c9..000000000 --- a/icicle/src/merkle-tree/tests/merkle/.gitignore +++ /dev/null @@ -1,7 +0,0 @@ -merkle.o -poseidon2.o -test_merkle_poseidon2 -merkle_bls.o -poseidon.o -test_merkle_poseidon -test_merkle \ No newline at end of file diff --git a/icicle/src/merkle-tree/tests/merkle/Makefile b/icicle/src/merkle-tree/tests/merkle/Makefile deleted file mode 100644 index 15b491f65..000000000 --- a/icicle/src/merkle-tree/tests/merkle/Makefile +++ /dev/null @@ -1,22 +0,0 @@ -test_merkle_poseidon: poseidon.o merkle_bls.o - nvcc -o test_merkle_poseidon -I../../../../include -DFIELD=bls12_381 -DFIELD_ID=2 -DCURVE=bls12_381 -DMERKLE_DEBUG poseidon.o merkle_bls.o test.cu - ./test_merkle_poseidon - -merkle_bls.o: ../../extern.cu ../../merkle.cu - nvcc -o merkle_bls.o -I../../../../include -DFIELD=bls12_381 -DFIELD_ID=2 -DCURVE=bls12_381 -DMERKLE_DEBUG -c ../../extern.cu - -poseidon.o: ../../../poseidon/extern.cu - nvcc -o poseidon.o -I../../../../include -DFIELD=bls12_381 -DFIELD_ID=2 -DCURVE=bls12_381 -c ../../../poseidon/extern.cu - -test_merkle: test_poseidon2.cu poseidon2.o merkle.o - nvcc -o test_merkle -I../../../../include -DFIELD=babybear -DFIELD_ID=1001 -DMERKLE_DEBUG poseidon2.o merkle.o test_poseidon2.cu - ./test_merkle - -merkle.o: ../../extern.cu ../../merkle.cu - nvcc -o merkle.o -I../../../../include -DFIELD=babybear -DFIELD_ID=1001 -DMERKLE_DEBUG -c ../../extern.cu - -poseidon2.o: ../../../poseidon2/extern.cu - nvcc -o poseidon2.o -I../../../../include -DFIELD=babybear -DFIELD_ID=1001 -c ../../../poseidon2/extern.cu - -clear: - rm -f poseidon2.o merkle.o test_merkle merkle_bls.o poseidon.o test_merkle \ No newline at end of file diff --git a/icicle/src/merkle-tree/tests/merkle/test.cu b/icicle/src/merkle-tree/tests/merkle/test.cu deleted file mode 100644 index 0fb348673..000000000 --- a/icicle/src/merkle-tree/tests/merkle/test.cu +++ /dev/null @@ -1,234 +0,0 @@ -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include -#include - -#define DEBUG -#include "merkle-tree/merkle.cuh" - -#include "poseidon/poseidon.cuh" - -#include "api/bls12_381.h" -using namespace bls12_381; - -// Arity -#define A 2 -#define T (A + 1) - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - // Load poseidon constants - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - poseidon::Poseidon poseidon(A, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - /// Tree of height N and arity A contains \sum{A^i} for i in 0..N-1 elements - uint32_t tree_height = argc > 1 ? atoi(argv[1]) : 26; - uint32_t number_of_leaves = pow(A, tree_height); - uint32_t total_number_of_leaves = number_of_leaves * A; - - /// Use keep_rows to specify how many rows do you want to store - int keep_rows = argc > 2 ? atoi(argv[2]) : 7; - size_t digests_len = merkle_tree::get_digests_len(keep_rows - 1, A, 1); - - /// Fill leaves with scalars [0, 1, ... 2^tree_height - 1] - START_TIMER(timer_allocation); - scalar_t input = scalar_t::zero(); - size_t leaves_mem = total_number_of_leaves * sizeof(scalar_t); - scalar_t* leaves = static_cast(malloc(leaves_mem)); - for (uint32_t i = 0; i < total_number_of_leaves; i++) { - leaves[i] = input; - input = input + scalar_t::one(); - } - END_TIMER(timer_allocation, "Allocated memory for leaves: "); - - /// Allocate memory for digests of {keep_rows} rows of a tree - START_TIMER(timer_digests); - size_t digests_mem = digests_len * sizeof(scalar_t); - scalar_t* digests = static_cast(malloc(digests_mem)); - END_TIMER(timer_digests, "Allocated memory for digests"); - - std::cout << "Memory for leaves = " << leaves_mem / 1024 / 1024 << " MB; " << leaves_mem / 1024 / 1024 / 1024 << " GB" - << std::endl; - std::cout << "Number of leaves = " << number_of_leaves << std::endl; - std::cout << "Total Number of leaves = " << total_number_of_leaves << std::endl; - std::cout << "Memory for digests = " << digests_mem / 1024 / 1024 << " MB; " << digests_mem / 1024 / 1024 / 1024 - << " GB" << std::endl; - std::cout << "Number of digest elements = " << digests_len << std::endl; - - std::cout << "Total RAM consumption = " << (digests_mem + leaves_mem) / 1024 / 1024 << " MB; " - << (digests_mem + leaves_mem) / 1024 / 1024 / 1024 << " GB" << std::endl; - - merkle_tree::TreeBuilderConfig tree_config = merkle_tree::default_merkle_config(); - tree_config.arity = 2; - tree_config.keep_rows = keep_rows; - START_TIMER(timer_merkle); - bls12_381_build_merkle_tree(leaves, digests, tree_height, A, &poseidon, &poseidon, tree_config); - END_TIMER(timer_merkle, "Merkle tree built: ") - - for (int i = 0; i < digests_len; i++) { - std::cout << digests[i] << std::endl; - } - - // Use this to generate test vectors - // for (int i = 0; i < digests_len; i++) { - // std::cout << "{"; - // for (int j = 0; j < 8; j++) { - // std::cout << ((uint32_t*)&digests[i].limbs_storage)[j]; - // if (j != 7) { std::cout << ", "; } - // } - // std::cout << "}," << std::endl; - // } - - /// These scalars are digests of top-7 rows of a Merkle tree. - /// Arity = 2, Tree height = 28, keep_rows = 7 - /// They are aligned in the following format: - /// L-7 L-6 L-5 L-4 L-3 L-2 L-1 - /// [0..63, 64..95, 96..111, 112..119, 120..123, 124..125, 126] - scalar_t expected[127] = { - {4014678573, 3239475319, 2314653449, 2957861895, 184634791, 3566699115, 1715234110, 445241356}, - {3505304837, 808365954, 2275962701, 1423516791, 1541423175, 1724600321, 226400287, 1785337258}, - {2768067495, 1388793239, 3997362411, 3464112194, 1973409933, 3681177968, 369038929, 472214073}, - {3654540770, 1469373240, 3282216438, 3185867716, 967978472, 1078033214, 78178652, 1830882032}, - {490654422, 2525841937, 626688402, 392783721, 58008344, 2145494226, 3242212931, 274501152}, - {3208477883, 3672804666, 239023996, 4035696816, 3265333325, 958951383, 181142437, 1239596452}, - {2969561146, 4232772549, 1556993315, 1662679755, 2909096842, 1439765953, 667526824, 31896728}, - {270711313, 12541629, 3347448212, 371567649, 1759208919, 1723057963, 674629473, 1013019629}, - {3017193939, 2312231485, 3737856857, 1313052851, 1196989094, 1495382607, 1023560618, 191925995}, - {293080341, 2290910595, 1543157595, 1127848979, 1815285163, 1706352673, 992727722, 591498292}, - {3549180392, 2881478531, 2660879925, 1033961139, 2665175480, 2168183735, 3470220482, 849297604}, - {2676239944, 3976356940, 2496525380, 1062384559, 686775848, 383379365, 3327679344, 1084443735}, - {1971496657, 3091116054, 3681796601, 1418643776, 3299382044, 3857762866, 3539539764, 1326628306}, - {630542774, 2408275928, 345917713, 687603493, 3754703591, 3550860419, 2083130565, 770159409}, - {870888363, 224377249, 3918057820, 3375742820, 2591003788, 2510070975, 3414099060, 340676877}, - {1875497580, 1000647891, 3811413293, 2001777831, 2253197473, 2328265571, 3906271342, 325320055}, - {1211415187, 488575334, 500057698, 3280169717, 3697121693, 1438922954, 931086076, 620808784}, - {1983293587, 2168562163, 3150824284, 3953229527, 436705204, 778341443, 3005081450, 615688285}, - {8576698, 3039838264, 671609957, 2809480667, 1620765047, 2117613498, 845941068, 455906025}, - {3906454348, 552283113, 645539850, 1316302434, 475485784, 2970197657, 274367582, 652809948}, - {1495892355, 1668646651, 14573046, 3197699206, 1489087180, 3696907100, 1481991676, 1713300937}, - {3908239800, 3651019608, 3657128408, 1619241101, 2332486430, 639066149, 2163145035, 1704519873}, - {601030174, 3024947211, 2825655316, 3863514365, 749861326, 2966866369, 3590896102, 1433089687}, - {2395125120, 2965783566, 2438173617, 591910928, 2454874264, 961201946, 3519059509, 1507770765}, - {4191182380, 3986691529, 626672198, 248199214, 2672873299, 1206537516, 4088647235, 643192311}, - {117373628, 2684708015, 1169533105, 4189512228, 39193015, 2348829360, 1044287150, 1200318263}, - {1641468310, 184141241, 1225984447, 3048243879, 3866424998, 3590741693, 2510098016, 274097253}, - {805243263, 2967911161, 448427804, 518401505, 404860884, 1593480904, 2843784918, 1217394583}, - {1068910868, 4126158631, 3428265735, 1108050638, 1809164812, 2970655045, 916421502, 271686161}, - {2733872014, 3489610640, 2253010322, 2061408208, 209418696, 317724116, 2610511126, 777106564}, - {922170442, 222695863, 4287857383, 1811357610, 3378048665, 339334666, 156256237, 1016584661}, - {2369411073, 3974740601, 3926942050, 1092836585, 2755312327, 492736530, 1777994166, 1608349307}, - {3791947957, 2178563469, 2708512205, 2104428978, 4281280169, 3158413195, 3714473637, 1932398193}, - {447850698, 872401916, 1547172058, 1242046677, 1896710579, 1137814221, 1602961798, 1571006781}, - {3469094617, 1395163063, 1061899259, 4059174800, 2083256644, 1034754354, 941298869, 416224305}, - {3468873946, 3282426941, 497353671, 953582605, 1587266512, 1740183981, 671088260, 1875939615}, - {3123987458, 2866544187, 2718390509, 359710965, 259964033, 4177846877, 740787103, 1674220305}, - {783470818, 2334330296, 116871553, 1683603268, 759950366, 3727285592, 3616407101, 525075992}, - {3899236298, 1976585972, 3822298095, 748204764, 2978121214, 200932224, 1340374715, 79506611}, - {4128139198, 1939423978, 3727937787, 1855257827, 630776586, 3470334826, 2498269262, 1495682436}, - {1348892970, 629870637, 3595702369, 770535689, 2256370736, 3444863052, 891128487, 33159341}, - {2967968932, 4261332726, 2399871138, 3313204386, 3991787238, 3531989490, 1624489585, 1792543190}, - {2179587180, 3737311899, 6357632, 3727566655, 3147613619, 2123011127, 3097377147, 992191510}, - {2182997143, 4101668140, 1996814613, 3581332946, 867002512, 1843201087, 4223363546, 1928077695}, - {3028933890, 3818130584, 3557387839, 2374442829, 911787393, 770676400, 337096879, 240174663}, - {3043672379, 1899171261, 504182702, 3054796849, 3828295843, 671705157, 3825173996, 1414902488}, - {376706115, 4194608423, 3594444823, 1625946449, 403768163, 2456846785, 44584848, 84202017}, - {3064390814, 2716996388, 3897127426, 2552999677, 3013912747, 1452854892, 3978189518, 1284784196}, - {3199468428, 2903114874, 72247584, 2410968068, 2003524493, 4123634338, 435261870, 701486964}, - {828746335, 3913770139, 1618218172, 3278121503, 2079247718, 1978636954, 910397289, 96552693}, - {2211749314, 3357848024, 3537465382, 3532697268, 2132182736, 1966921197, 1315441937, 1777448477}, - {2200248661, 391584476, 2234387592, 2850080717, 2653523993, 3210840164, 2903237762, 1794426612}, - {554961316, 3167917252, 4221707975, 1012517545, 736753739, 110159932, 145505695, 227111964}, - {3875411415, 1613987934, 1084962884, 3019340126, 2315514976, 2094187263, 3497819348, 291280352}, - {1739154213, 3075259556, 3948002791, 1367585114, 1701733509, 3623455345, 3493135301, 1923303618}, - {1827158133, 3185981038, 878567088, 2821327133, 3663194195, 4207640486, 3564098674, 1184657196}, - {3758205899, 815333136, 2869440132, 1818787811, 1081254131, 1686248084, 1041506138, 381361493}, - {3628751007, 777692999, 4180345157, 4240314964, 3494907162, 692170506, 2009935286, 1322780120}, - {887413708, 23343153, 3555916162, 3334619375, 2777783869, 2643155742, 1986000428, 630137915}, - {4096942349, 4145995447, 897723766, 2990805409, 4029167381, 2495258909, 4088391249, 609264602}, - {612790660, 2661534473, 247248263, 1136855857, 1161947150, 1315488261, 3680235769, 1229555413}, - {722881024, 192705355, 1754502243, 2697508301, 2945854931, 3504159885, 3445165630, 238076059}, - {222219657, 3437798818, 2744917536, 224256950, 1904282056, 105073736, 772693683, 292110801}, - {1796528306, 837471306, 1567726322, 1897641954, 3078024748, 3325524011, 3385455562, 951015556}, - {2407829502, 3164486936, 1487657, 4182947136, 3849960947, 3830817126, 1138850753, 130868390}, - {1678448871, 4289024418, 2650638643, 67119139, 3182458352, 3617797334, 628056031, 191722155}, - {3324568052, 2439834493, 375051932, 2170654759, 2348874396, 3713035811, 4199471222, 283037510}, - {773651958, 2125754199, 199104117, 213977251, 367104435, 3157051288, 2326732794, 275408701}, - {2460311739, 1791145889, 3915204508, 1272346229, 2834150762, 3900627326, 148310662, 1750293843}, - {2130280064, 2159064112, 1437026429, 2733029067, 294236910, 1031342011, 2050658606, 1599487766}, - {1105683188, 141496307, 1055256037, 646629220, 2520569043, 2475709708, 3396934601, 14464324}, - {2583712422, 1775337336, 2707695021, 1832388440, 4273340329, 2820244513, 2507193407, 1404928203}, - {1269867733, 2223696603, 1861500459, 2421626755, 3502911196, 2815315379, 549184699, 16975667}, - {426922700, 3601088398, 4218337357, 1790978582, 4136866168, 4035715442, 2326873078, 235501400}, - {3402079655, 566662668, 553974537, 3190505572, 1786065459, 1789792927, 1424842205, 1816440650}, - {3848752806, 4260216223, 570978505, 2129098465, 232557756, 7106551, 687416267, 1391264097}, - {670044537, 331678659, 344646513, 3012645076, 36483293, 2753421815, 1891477861, 132172653}, - {7972615, 3961288035, 486739371, 228490819, 1675833723, 2413801880, 3168288162, 1168358009}, - {1158898905, 554620687, 1047964985, 3481289834, 4201243048, 75670413, 821867979, 231202146}, - {520239323, 1484416551, 3247647509, 1403067553, 1541879850, 3785630405, 1117276684, 85930988}, - {1931565112, 3470042772, 3537431410, 2318191566, 3569616629, 726769301, 1627396145, 328822231}, - {1678673875, 3628799819, 1857961535, 915186097, 3107622699, 3356035428, 3569404983, 1026648178}, - {913100268, 3846074625, 3740179720, 3454441980, 4093733473, 1346792008, 1163703973, 57404821}, - {1519620766, 1446603960, 2393223713, 1459282094, 1023092791, 2199286418, 2096208360, 699051559}, - {2219754787, 2500265080, 191792189, 1542645732, 1161454113, 2969905865, 3687236724, 10987900}, - {1743254412, 82672506, 3135049363, 887561904, 928784698, 2657646376, 288207897, 435585205}, - {3465517945, 3661905420, 2283191968, 3144869509, 3579046583, 2037447473, 1533911765, 211714799}, - {2496160147, 1418604384, 1802507016, 1336309157, 1184402515, 206535898, 128178254, 1130299949}, - {1961044967, 3019089825, 2997612316, 265811573, 3263223461, 3365214238, 1135984798, 1595605265}, - {1483975027, 2808544081, 229106190, 450319819, 1309534945, 3830732771, 1124125013, 281790638}, - {3747100452, 107125028, 607240855, 1725178756, 3597437256, 3348335091, 2059188672, 1306485825}, - {79235387, 3022164429, 1311047830, 3155028639, 916354506, 2279445426, 83551349, 1018957398}, - {2095857719, 3787514231, 1479065116, 482814220, 2228714165, 221954960, 4265971691, 1649347513}, - {3188516880, 3597244272, 108997607, 353456206, 3223447432, 446895619, 2424498673, 1113641309}, - {57226169, 310268999, 3047208456, 3667872449, 1342874028, 1962131723, 2203612281, 717247378}, - {2523858162, 3604519783, 975513559, 4197094359, 2071686613, 2849799672, 321527978, 1319949504}, - {1913310732, 564832490, 732308813, 2586688178, 4054037287, 2720587232, 909207782, 1001626679}, - {4229590794, 3110228758, 4161959585, 4143451607, 1274301898, 499710233, 2801297171, 1696629637}, - {4283094591, 2041587860, 4272909686, 3032518657, 1275782421, 638738833, 562322176, 1764251529}, - {2166709661, 2507730084, 2167347676, 730353949, 4164042092, 702353215, 1692356174, 630374766}, - {4139857027, 2019739214, 1365461556, 190724658, 3746881101, 1551936264, 2542716346, 641014892}, - {3705816562, 1108147156, 1905372056, 2828808730, 1103318054, 1094456706, 1691237212, 559553895}, - {4190357473, 2752760058, 3475907929, 138551448, 2308123641, 1481331661, 4250021059, 794197070}, - {2204086692, 2614018999, 4066699829, 2915251952, 1331110005, 2166000553, 3772671276, 708029528}, - {3657760721, 2239870589, 436271478, 2779795192, 24689995, 1297075264, 1671988556, 523339184}, - {884023061, 652621983, 62069329, 1533423970, 279751135, 1384150035, 2787913666, 1184536494}, - {3832683369, 3220024363, 3898646445, 3616156349, 1816361908, 58265014, 2258645744, 1322185408}, - {2186663972, 992823504, 580939809, 4134674086, 3099356555, 1833300007, 689911450, 1656492952}, - {702913923, 1119558248, 3418804320, 2560686829, 3844756959, 2394009955, 1587731805, 1401231027}, - {3358386685, 971450419, 3942097807, 1751903613, 2010516544, 1867306049, 3690714201, 1210488397}, - {3401698520, 536404836, 794977212, 3865401272, 293002110, 51547193, 2792668565, 794288943}, - {2535122179, 3301198404, 1831226988, 939065116, 2928600716, 482930544, 1250716156, 1762491790}, - {754984898, 38001923, 2689392564, 1026928647, 489115662, 455738937, 2410612830, 1307198941}, - {172844518, 1699952276, 1485123703, 1156671318, 3179094813, 1188798431, 112551259, 1847940720}, - {3545208113, 2289833538, 2649086240, 2023989097, 3349947852, 696893462, 687717267, 1856396125}, - {3910459449, 3705304614, 3532442943, 3481251226, 895301896, 2986084489, 3299258047, 1761424967}, - {2725674291, 116532222, 3565763434, 1451215632, 1092995942, 2035526239, 1045737445, 1613646521}, - {4130593726, 3024687204, 1021306812, 2469430501, 497194315, 2974581792, 1255017536, 597255305}, - {523876618, 761620533, 3490119046, 1051900320, 1477617871, 2040477122, 3099679769, 1824205879}, - {4238414613, 3935907273, 2701284094, 1435298493, 2842895400, 3094051424, 2033163260, 1083623692}, - {642361789, 1604941380, 2400216156, 566375932, 30286136, 1212333003, 4008803597, 1648114656}, - {4152681515, 2343918434, 2094367525, 1848475261, 1968024081, 2889245354, 3005464900, 1786065752}, - {1081950398, 3116162259, 3148939719, 2734634826, 499009055, 1696427234, 1357324515, 801013715}, - {998077513, 4143316008, 3160753512, 1619454701, 647700665, 2041647364, 4250743448, 876294208}, - {42525978, 2569996457, 3426184151, 3354583607, 4034764830, 1228943658, 894711986, 618735436}, - {885919544, 1214434554, 3367715898, 2051767156, 3953912206, 4149513394, 37475677, 727749078}, - {4011510622, 1671330619, 385068934, 1150628512, 1425291267, 724496539, 1547726122, 746328500}, - }; - - for (int i = 0; i < digests_len; i++) { - scalar_t root = digests[i]; - assert(root == expected[i]); - } - free(digests); - free(leaves); -} - -#endif \ No newline at end of file diff --git a/icicle/src/merkle-tree/tests/merkle/test_poseidon2.cu b/icicle/src/merkle-tree/tests/merkle/test_poseidon2.cu deleted file mode 100644 index 7bd35c92a..000000000 --- a/icicle/src/merkle-tree/tests/merkle/test_poseidon2.cu +++ /dev/null @@ -1,109 +0,0 @@ -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include -#include - -#include "merkle-tree/merkle.cuh" - -#include "poseidon2/poseidon2.cuh" - -#include "api/babybear.h" -using namespace babybear; - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - /// Tree of height N and arity A contains \sum{A^i} for i in 0..N elements - uint32_t tree_arity = 2; - uint32_t width = 16; - uint32_t input_block_len = 8; - uint32_t digest_elements = 8; - uint64_t tree_height = argc > 1 ? atoi(argv[1]) : 23; - uint64_t number_of_leaves = pow(tree_arity, tree_height); - uint64_t total_number_of_leaves = number_of_leaves * input_block_len; - - // Load poseidon constants - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - poseidon2::Poseidon2 poseidon( - width, input_block_len, poseidon2::MdsType::DEFAULT_MDS, poseidon2::DiffusionStrategy::DEFAULT_DIFFUSION, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - /// Use keep_rows to specify how many rows do you want to store - int keep_rows = argc > 2 ? atoi(argv[2]) : 3; - size_t digests_len = merkle_tree::get_digests_len(keep_rows - 1, tree_arity, digest_elements); - - /// Fill leaves with scalars [0, 1, ... 2^tree_height - 1] - START_TIMER(timer_allocation); - scalar_t input = scalar_t::zero(); - size_t leaves_mem = total_number_of_leaves * sizeof(scalar_t); - scalar_t* leaves = static_cast(malloc(leaves_mem)); - for (uint64_t i = 0; i < total_number_of_leaves; i++) { - leaves[i] = input; - input = input + scalar_t::one(); - } - END_TIMER(timer_allocation, "Allocated memory for leaves: "); - - /// Allocate memory for digests of {keep_rows} rows of a tree - START_TIMER(timer_digests); - size_t digests_mem = digests_len * sizeof(scalar_t); - scalar_t* digests = static_cast(malloc(digests_mem)); - END_TIMER(timer_digests, "Allocated memory for digests"); - - std::cout << "Memory for leaves = " << leaves_mem / 1024 / 1024 << " MB; " << leaves_mem / 1024 / 1024 / 1024 << " GB" - << std::endl; - std::cout << "Number of leaves = " << number_of_leaves << std::endl; - std::cout << "Total Number of leaves = " << total_number_of_leaves << std::endl; - std::cout << "Memory for digests = " << digests_mem / 1024 / 1024 << " MB; " << digests_mem / 1024 / 1024 / 1024 - << " GB" << std::endl; - std::cout << "Number of digest elements = " << digests_len << std::endl; - - std::cout << "Total RAM consumption = " << (digests_mem + leaves_mem) / 1024 / 1024 << " MB; " - << (digests_mem + leaves_mem) / 1024 / 1024 / 1024 << " GB" << std::endl; - - merkle_tree::TreeBuilderConfig tree_config = merkle_tree::default_merkle_config(); - tree_config.arity = tree_arity; - tree_config.keep_rows = keep_rows; - tree_config.digest_elements = digest_elements; - START_TIMER(timer_merkle); - babybear_build_merkle_tree(leaves, digests, tree_height, input_block_len, &poseidon, &poseidon, tree_config); - END_TIMER(timer_merkle, "Merkle tree built: ") - - for (int i = 0; i < digests_len; i++) { - // std::cout << digests[i] << std::endl; - } - - // Use this to generate test vectors - // for (int i = 0; i < digests_len; i++) { - // std::cout << "{"; - // for (int j = 0; j < 1; j++) { - // std::cout << ((uint32_t*)&digests[i].limbs_storage)[j]; - // } - // std::cout << "}," << std::endl; - // } - - scalar_t expected[64] = { - {876845485}, {1982055884}, {1232961929}, {1502814326}, {1731913687}, {351564698}, {449044700}, {656218013}, - {1616800877}, {1324365320}, {651075613}, {1679193452}, {218302636}, {283697394}, {1141456517}, {253630808}, - {936036237}, {1020969125}, {597252945}, {32839064}, {957901845}, {1137914369}, {155933167}, {986924657}, - {1553746264}, {1007314324}, {1208763331}, {110389244}, {118704360}, {607471513}, {834479233}, {914998571}, - {1086906039}, {1673233108}, {431115765}, {233068973}, {1974449092}, {1296268875}, {538093590}, {104288129}, - {1011605567}, {53314351}, {1461404090}, {870754513}, {1212389386}, {1363519118}, {799527383}, {1258384762}, - {678820782}, {1940801563}, {887764924}, {1006362075}, {2003940909}, {1213396717}, {1332793191}, {440259232}}; - - for (int i = 0; i < digests_len; i++) { - scalar_t root = digests[i]; - // std::cout << root << std::endl; - assert(root == expected[i]); - } - free(digests); - free(leaves); -} - -#endif \ No newline at end of file diff --git a/icicle/src/merkle-tree/tests/mmcs/.gitignore b/icicle/src/merkle-tree/tests/mmcs/.gitignore deleted file mode 100644 index ae6419c96..000000000 --- a/icicle/src/merkle-tree/tests/mmcs/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -mmcs.o -poseidon2.o -test_mmcs_poseidon2 -vec_ops.o \ No newline at end of file diff --git a/icicle/src/merkle-tree/tests/mmcs/Makefile b/icicle/src/merkle-tree/tests/mmcs/Makefile deleted file mode 100644 index 42db32337..000000000 --- a/icicle/src/merkle-tree/tests/mmcs/Makefile +++ /dev/null @@ -1,15 +0,0 @@ -test_merkle: poseidon2.o mmcs.o vec_ops.o - nvcc -o test_mmcs_poseidon2 -lineinfo -I../../../../include -DFIELD=babybear -DFIELD_ID=1001 -DMERKLE_DEBUG poseidon2.o vec_ops.o mmcs.o test_poseidon2.cu - ./test_mmcs_poseidon2 - -mmcs.o: ../../extern_mmcs.cu ../../mmcs.cu - nvcc -o mmcs.o -I../../../../include -lineinfo -DFIELD=babybear -DFIELD_ID=1001 -DMERKLE_DEBUG -c ../../extern_mmcs.cu - -poseidon2.o: ../../../poseidon2/extern.cu - nvcc -o poseidon2.o -I../../../../include -lineinfo -DFIELD=babybear -DFIELD_ID=1001 -c ../../../poseidon2/extern.cu - -vec_ops.o: - nvcc -o vec_ops.o -I../../../../include -lineinfo -DFIELD=babybear -DFIELD_ID=1001 -c ../../../vec_ops/extern.cu - -clear: - rm -f poseidon2.o mmcs.o vec_ops.o test_mmcs_poseidon2 diff --git a/icicle/src/merkle-tree/tests/mmcs/test_poseidon2.cu b/icicle/src/merkle-tree/tests/mmcs/test_poseidon2.cu deleted file mode 100644 index 9de240dbc..000000000 --- a/icicle/src/merkle-tree/tests/mmcs/test_poseidon2.cu +++ /dev/null @@ -1,139 +0,0 @@ -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include -#include - -#include "merkle-tree/merkle.cuh" - -#include "poseidon2/poseidon2.cuh" - -#include "api/babybear.h" -using namespace babybear; - -using FpMilliseconds = std::chrono::duration; -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - /// Tree of height N and arity A contains \sum{A^i} for i in 0..N elements - uint32_t tree_arity = 2; - uint32_t width = 16; - uint32_t input_block_len = 600; - uint32_t rate = 8; - uint32_t digest_elements = 8; - uint32_t copied_matrices = 1; - uint64_t tree_height = argc > 1 ? atoi(argv[1]) : 3; - uint64_t number_of_leaves = pow(tree_arity, tree_height); - uint64_t total_number_of_leaves = number_of_leaves * input_block_len; - - bool are_inputs_on_device = true; - - // Load poseidon constants - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - poseidon2::Poseidon2 poseidon( - width, rate, poseidon2::MdsType::PLONKY, poseidon2::DiffusionStrategy::MONTGOMERY, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - /// Use keep_rows to specify how many rows do you want to store - int keep_rows = argc > 2 ? atoi(argv[2]) : 3; - size_t digests_len = merkle_tree::get_digests_len(keep_rows - 1, tree_arity, digest_elements); - - /// Fill leaves with scalars [0, 1, ... 2^tree_height - 1] - START_TIMER(timer_allocation); - scalar_t input = scalar_t::zero(); - - // unsigned int number_of_inputs = tree_height * copied_matrices; - unsigned int number_of_inputs = 1; - Matrix* leaves = static_cast*>(malloc(number_of_inputs * sizeof(Matrix))); - uint64_t current_matrix_rows = number_of_leaves; - for (int i = 0; i < number_of_inputs; i++) { - uint64_t current_matrix_size = current_matrix_rows * input_block_len; - for (int j = 0; j < copied_matrices; j++) { - scalar_t* matrix = static_cast(malloc(current_matrix_size * sizeof(scalar_t))); - - for (uint64_t k = 0; k < current_matrix_size; k++) { - matrix[k] = input; - input = input + scalar_t::one(); - } - - scalar_t* d_matrix; - if (are_inputs_on_device) { - cudaMalloc(&d_matrix, current_matrix_size * sizeof(scalar_t)); - cudaMemcpy(d_matrix, matrix, current_matrix_size * sizeof(scalar_t), cudaMemcpyHostToDevice); - } - - leaves[i * copied_matrices + j] = { - are_inputs_on_device ? d_matrix : matrix, - input_block_len, - current_matrix_rows, - }; - } - - current_matrix_rows /= tree_arity; - } - - END_TIMER(timer_allocation, "Allocated memory for leaves: "); - - /// Allocate memory for digests of {keep_rows} rows of a tree - START_TIMER(timer_digests); - size_t digests_mem = digests_len * sizeof(scalar_t); - scalar_t* digests = static_cast(malloc(digests_mem)); - END_TIMER(timer_digests, "Allocated memory for digests"); - - // std::cout << "Memory for leaves = " << total_number_of_leaves * sizeof(scalar_t) / 1024 / 1024 << " MB; " << - // leaves_mem / 1024 / 1024 / 1024 << " GB" - // << std::endl; - std::cout << "Number of leaves = " << number_of_leaves << std::endl; - std::cout << "Total Number of leaves = " << total_number_of_leaves << std::endl; - std::cout << "Memory for digests = " << digests_mem / 1024 / 1024 << " MB; " << digests_mem / 1024 / 1024 / 1024 - << " GB" << std::endl; - std::cout << "Number of digest elements = " << digests_len << std::endl; - std::cout << std::endl; - - // std::cout << "Total RAM consumption = " << (digests_mem + leaves_mem) / 1024 / 1024 << " MB; " - // << (digests_mem + leaves_mem) / 1024 / 1024 / 1024 << " GB" << std::endl; - - merkle_tree::TreeBuilderConfig tree_config = merkle_tree::default_merkle_config(); - tree_config.are_inputs_on_device = are_inputs_on_device; - tree_config.arity = tree_arity; - tree_config.keep_rows = keep_rows; - tree_config.digest_elements = digest_elements; - START_TIMER(timer_merkle); - babybear_mmcs_commit_cuda(leaves, number_of_inputs, digests, &poseidon, &poseidon, tree_config); - END_TIMER(timer_merkle, "Merkle tree built: ") - - for (int i = 0; i < 10; i++) { - std::cout << digests[digests_len - i - 1] << std::endl; - } - - // Use this to generate test vectors - // for (int i = 0; i < digests_len; i++) { - // std::cout << "{"; - // for (int j = 0; j < 8; j++) { - // std::cout << ((uint64_t*)&digests[i].limbs_storage)[j]; - // if (j != 7) { std::cout << ", "; } - // } - // std::cout << "}," << std::endl; - // } - - /// These scalars are digests of top-7 rows of a Merkle tree. - /// Arity = 2, Tree height = 28, keep_rows = 7 - /// They are aligned in the following format: - /// L-7 L-6 L-5 L-4 L-3 L-2 L-1 - /// [0..63, 64..95, 96..111, 112..119, 120..123, 124..125, 126] - scalar_t expected[0] = {}; - - for (int i = 0; i < digests_len; i++) { - scalar_t root = digests[i]; - // assert(root == expected[i]); - } - free(digests); - free(leaves); -} - -#endif \ No newline at end of file diff --git a/icicle/src/msm.cpp b/icicle/src/msm.cpp new file mode 100644 index 000000000..e75667e64 --- /dev/null +++ b/icicle/src/msm.cpp @@ -0,0 +1,75 @@ +#include "icicle/backend/msm_backend.h" +#include "icicle/dispatcher.h" +#include "icicle/curves/curve_config.h" + +using namespace curve_config; + +namespace icicle { + + /*************************** MSM ***************************/ + ICICLE_DISPATCHER_INST(MsmDispatcher, msm, MsmImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, msm)( + const scalar_t* scalars, const affine_t* bases, int msm_size, const MSMConfig* config, projective_t* results) + { + return MsmDispatcher::execute(scalars, bases, msm_size, *config, results); + } + + template <> + eIcicleError + msm(const scalar_t* scalars, const affine_t* bases, int msm_size, const MSMConfig& config, projective_t* results) + { + return CONCAT_EXPAND(CURVE, msm)(scalars, bases, msm_size, &config, results); + } + +#ifdef G2 + ICICLE_DISPATCHER_INST(MsmG2Dispatcher, g2_msm, MsmG2Impl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, g2_msm)( + const scalar_t* scalars, const g2_affine_t* bases, int msm_size, const MSMConfig* config, g2_projective_t* results) + { + return MsmG2Dispatcher::execute(scalars, bases, msm_size, *config, results); + } + + template <> + eIcicleError msm( + const scalar_t* scalars, const g2_affine_t* bases, int msm_size, const MSMConfig& config, g2_projective_t* results) + { + return CONCAT_EXPAND(CURVE, g2_msm)(scalars, bases, msm_size, &config, results); + } +#endif // G2 + + /*************************** MSM PRECOMPUTE BASES ***************************/ + ICICLE_DISPATCHER_INST(MsmPreComputeDispatcher, msm_precompute_bases, MsmPreComputeImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, msm_precompute_bases)( + const affine_t* input_bases, int nof_bases, const MSMConfig* config, affine_t* output_bases) + { + return MsmPreComputeDispatcher::execute(input_bases, nof_bases, *config, output_bases); + } + + template <> + eIcicleError + msm_precompute_bases(const affine_t* input_bases, int nof_bases, const MSMConfig& config, affine_t* output_bases) + { + return CONCAT_EXPAND(CURVE, msm_precompute_bases)(input_bases, nof_bases, &config, output_bases); + } + +#ifdef G2 + ICICLE_DISPATCHER_INST(MsmG2PreComputeDispatcher, g2_msm_precompute_bases, MsmG2PreComputeImpl); + + extern "C" eIcicleError CONCAT_EXPAND(CURVE, g2_msm_precompute_bases)( + const g2_affine_t* input_bases, int nof_bases, const MSMConfig* config, g2_affine_t* output_bases) + { + return MsmG2PreComputeDispatcher::execute(input_bases, nof_bases, *config, output_bases); + } + + template <> + eIcicleError msm_precompute_bases( + const g2_affine_t* input_bases, int nof_bases, const MSMConfig& config, g2_affine_t* output_bases) + { + return CONCAT_EXPAND(CURVE, g2_msm_precompute_bases)(input_bases, nof_bases, &config, output_bases); + } +#endif // G2 + +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/msm/Makefile b/icicle/src/msm/Makefile deleted file mode 100644 index b3cda9ca0..000000000 --- a/icicle/src/msm/Makefile +++ /dev/null @@ -1,8 +0,0 @@ -build_msm: - mkdir -p work - nvcc -o work/test_msm -std=c++17 -arch=sm_80 -I. -I../../include tests/msm_test.cu - -test_msm: - mkdir -p work - nvcc -o work/test_msm -std=c++17 -arch=sm_80 -I. -I../../include tests/msm_test.cu - work/test_msm \ No newline at end of file diff --git a/icicle/src/msm/extern.cu b/icicle/src/msm/extern.cu deleted file mode 100644 index 42bc31dba..000000000 --- a/icicle/src/msm/extern.cu +++ /dev/null @@ -1,54 +0,0 @@ -#include "curves/curve_config.cuh" -#include "fields/field_config.cuh" - -using namespace curve_config; -using namespace field_config; - -#include "msm.cu" -#include "utils/utils.h" - -namespace msm { - /** - * Extern "C" version of [precompute_msm_points](@ref precompute_msm_points) function with the following values of - * template parameters (where the curve is given by `-DCURVE` env variable during build): - * - `A` is the [affine representation](@ref affine_t) of curve points; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, precompute_msm_points_cuda)( - affine_t* points, int msm_size, MSMConfig& config, affine_t* output_points) - { - return precompute_msm_points(points, msm_size, config, output_points); - } - /** - * Extern "C" version of [precompute_msm_bases](@ref precompute_msm_bases) function with the following values of - * template parameters (where the curve is given by `-DCURVE` env variable during build): - * - `A` is the [affine representation](@ref affine_t) of curve points; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, precompute_msm_bases_cuda)( - affine_t* bases, - int bases_size, - int precompute_factor, - int _c, - bool are_bases_on_device, - device_context::DeviceContext& ctx, - affine_t* output_bases) - { - return precompute_msm_bases( - bases, bases_size, precompute_factor, _c, are_bases_on_device, ctx, output_bases); - } - - /** - * Extern "C" version of [msm](@ref msm) function with the following values of template parameters - * (where the curve is given by `-DCURVE` env variable during build): - * - `S` is the [scalar field](@ref scalar_t) of the curve; - * - `A` is the [affine representation](@ref affine_t) of curve points; - * - `P` is the [projective representation](@ref projective_t) of curve points. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, msm_cuda)( - const scalar_t* scalars, const affine_t* points, int msm_size, MSMConfig& config, projective_t* out) - { - return msm(scalars, points, msm_size, config, out); - } -} // namespace msm \ No newline at end of file diff --git a/icicle/src/msm/extern_g2.cu b/icicle/src/msm/extern_g2.cu deleted file mode 100644 index 1a84c66e3..000000000 --- a/icicle/src/msm/extern_g2.cu +++ /dev/null @@ -1,53 +0,0 @@ -#include "curves/curve_config.cuh" -#include "fields/field_config.cuh" - -using namespace curve_config; -using namespace field_config; - -#include "msm.cu" -#include "utils/utils.h" - -namespace msm { - /** - * Extern "C" version of [precompute_msm_points](@ref precompute_msm_points) function with the following values of - * template parameters (where the curve is given by `-DCURVE` env variable during build): - * - `A` is the [affine representation](@ref g2_affine_t) of G2 curve points; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_precompute_msm_points_cuda)( - g2_affine_t* points, int msm_size, MSMConfig& config, g2_affine_t* output_points) - { - return precompute_msm_points(points, msm_size, config, output_points); - } - /** - * Extern "C" version of [precompute_msm_bases](@ref precompute_msm_bases) function with the following values of - * template parameters (where the curve is given by `-DCURVE` env variable during build): - * - `A` is the [affine representation](@ref g2_affine_t) of G2 curve points; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_precompute_msm_bases_cuda)( - g2_affine_t* bases, - int bases_size, - int precompute_factor, - int _c, - bool are_bases_on_device, - device_context::DeviceContext& ctx, - g2_affine_t* output_bases) - { - return precompute_msm_bases( - bases, bases_size, precompute_factor, _c, are_bases_on_device, ctx, output_bases); - } - /** - * Extern "C" version of [msm](@ref msm) function with the following values of template parameters - * (where the curve is given by `-DCURVE` env variable during build): - * - `S` is the [scalar field](@ref scalar_t) of the curve; - * - `A` is the [affine representation](@ref g2_affine_t) of G2 curve points; - * - `P` is the [projective representation](@ref g2_projective_t) of G2 curve points. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_msm_cuda)( - const scalar_t* scalars, const g2_affine_t* points, int msm_size, MSMConfig& config, g2_projective_t* out) - { - return msm(scalars, points, msm_size, config, out); - } -} // namespace msm \ No newline at end of file diff --git a/icicle/src/msm/msm.cu b/icicle/src/msm/msm.cu deleted file mode 100644 index 7b6f88513..000000000 --- a/icicle/src/msm/msm.cu +++ /dev/null @@ -1,947 +0,0 @@ -#include "msm/msm.cuh" - -#include -#include -#include -#include -#include - -#include -#include -#include - -#include "curves/affine.cuh" -#include "curves/projective.cuh" -#include "fields/field.cuh" -#include "gpu-utils/error_handler.cuh" -#include "utils/mont.cuh" - -namespace msm { - - namespace { - -#define MAX_TH 256 - - // #define SSM_SUM //WIP - - template - __global__ void left_shift_kernel(A* points, const unsigned shift, const unsigned count, A* points_out) - { - const unsigned tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= count) return; - P point = P::from_affine(points[tid]); - for (unsigned i = 0; i < shift; i++) - point = P::dbl(point); - points_out[tid] = P::to_affine(point); - } - - unsigned get_optimal_c(int bitsize) { return (unsigned)max(ceil(std::log2(bitsize)) - 4.0, 1.0); } - - template - __global__ void normalize_kernel(E* inout, E factor, int n) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) inout[tid] = (inout[tid] + factor - 1) / factor; - } - - // a kernel that writes to bucket_indices which enables large bucket accumulation to happen afterwards. - // specifically we map thread indices to buckets which said threads will handle in accumulation. - template - __global__ void initialize_large_bucket_indices( - unsigned* sorted_bucket_sizes_sum, - unsigned nof_pts_per_thread, - unsigned nof_large_buckets, - // log_nof_buckets_to_compute should be equal to ceil(log(nof_buckets_to_compute)) - unsigned log_nof_large_buckets, - unsigned* bucket_indices) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= nof_large_buckets) { return; } - unsigned start = (sorted_bucket_sizes_sum[tid] + nof_pts_per_thread - 1) / nof_pts_per_thread + tid; - unsigned end = (sorted_bucket_sizes_sum[tid + 1] + nof_pts_per_thread - 1) / nof_pts_per_thread + tid + 1; - for (unsigned i = start; i < end; i++) { - // this just concatenates two pieces of data - large bucket index and (i - start) - bucket_indices[i] = tid | ((i - start) << log_nof_large_buckets); - } - } - - // this function provides a single step of reduction across buckets sizes of - // which are given by large_bucket_sizes pointer - template - __global__ void sum_reduction_variable_size_kernel( - P* v, - unsigned* bucket_sizes_sum, - unsigned* bucket_sizes, - unsigned* large_bucket_thread_indices, - unsigned nof_threads) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= nof_threads) { return; } - - unsigned large_bucket_tid = large_bucket_thread_indices[tid]; - unsigned segment_ind = tid - bucket_sizes_sum[large_bucket_tid] - large_bucket_tid; - unsigned large_bucket_size = bucket_sizes[large_bucket_tid]; - if (segment_ind < (large_bucket_size >> 1)) { v[tid] = v[tid] + v[tid + ((large_bucket_size + 1) >> 1)]; } - } - - template - __global__ void single_stage_multi_reduction_kernel( - const P* v, - P* v_r, - unsigned orig_block_size, - unsigned block_size, - unsigned write_stride, - unsigned buckets_per_bm, - unsigned write_phase, - unsigned step, - unsigned nof_threads) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= nof_threads) return; - - // we need shifted tid because we don't want to be reducing into zero buckets, this allows to skip them. - // for write_phase==1, the read pattern is different so we don't skip over anything. - const int shifted_tid = write_phase ? tid : tid + (tid + step) / step; - const int jump = block_size / 2; - const int block_id = shifted_tid / jump; - // here the reason for shifting is the same as for shifted_tid but we skip over entire blocks which happens - // only for write_phase=1 because of its read pattern. - const int shifted_block_id = write_phase ? block_id + (block_id + step) / step : block_id; - const int block_tid = shifted_tid % jump; - const unsigned read_ind = orig_block_size * shifted_block_id + block_tid; - const unsigned write_ind = jump * shifted_block_id + block_tid; - const unsigned v_r_key = - write_stride ? ((write_ind / buckets_per_bm) * 2 + write_phase) * write_stride + write_ind % buckets_per_bm - : read_ind; - v_r[v_r_key] = v[read_ind] + v[read_ind + jump]; - } - - // this kernel performs single scalar multiplication - // each thread multiplies a single scalar and point - template - __global__ void ssm_kernel(const S* scalars, const P* points, P* results, unsigned N) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid < N) results[tid] = scalars[tid] * points[tid]; - } - - // this kernel sums all the elements in a given vector using multiple threads - template - __global__ void sum_reduction_kernel(P* v, P* v_r) - { - unsigned tid = blockIdx.x * blockDim.x + threadIdx.x; - - // Start at 1/2 block stride and divide by two each iteration - for (unsigned s = blockDim.x / 2; s > 0; s >>= 1) { - // Each thread does work unless it is further than the stride - if (threadIdx.x < s) { v[tid] = v[tid] + v[tid + s]; } - __syncthreads(); - } - - // Let the thread 0 for this block write the final result - if (threadIdx.x == 0) { v_r[blockIdx.x] = v[tid]; } - } - - // this kernel initializes the buckets with zero points - // each thread initializes a different bucket - template - __global__ void initialize_buckets_kernel(P* buckets, unsigned N) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid < N) buckets[tid] = P::zero(); // zero point - } - - // this kernel splits the scalars into digits of size c - // each thread splits a single scalar into nof_bms digits - template - __global__ void split_scalars_kernel( - unsigned* buckets_indices, - unsigned* point_indices, - const S* scalars, - unsigned nof_scalars, - unsigned points_size, - unsigned msm_size, - unsigned nof_bms, - unsigned bm_bitsize, - unsigned c, - unsigned precomputed_bms_stride) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_scalars) return; - - unsigned bucket_index; - unsigned current_index; - unsigned msm_index = tid / msm_size; - const S& scalar = scalars[tid]; - for (unsigned bm = 0; bm < nof_bms; bm++) { - const unsigned precomputed_index = bm / precomputed_bms_stride; - const unsigned target_bm = bm % precomputed_bms_stride; - - bucket_index = scalar.get_scalar_digit(bm, c); - current_index = bm * nof_scalars + tid; - - if (bucket_index != 0) { - buckets_indices[current_index] = - (msm_index << (c + bm_bitsize)) | (target_bm << c) | - bucket_index; // the bucket module number and the msm number are appended at the msbs - } else { - buckets_indices[current_index] = 0; // will be skipped - } - point_indices[current_index] = - tid % points_size + points_size * precomputed_index; // the point index is saved for later - } - } - - template - __global__ void - find_cutoff_kernel(unsigned* v, unsigned size, unsigned cutoff, unsigned run_length, unsigned* result) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - const unsigned nof_threads = (size + run_length - 1) / run_length; - if (tid >= nof_threads) { return; } - const unsigned start_index = tid * run_length; - for (int i = start_index; i < min(start_index + run_length, size - 1); i++) { - if (v[i] > cutoff && v[i + 1] <= cutoff) { - result[0] = i + 1; - return; - } - } - if (tid == 0 && v[size - 1] > cutoff) { result[0] = size; } - } - - // this kernel adds up the points in each bucket - template - __global__ void accumulate_buckets_kernel( - P* __restrict__ buckets, - unsigned* __restrict__ bucket_offsets, - unsigned* __restrict__ bucket_sizes, - unsigned* __restrict__ single_bucket_indices, - const unsigned* __restrict__ point_indices, - A* __restrict__ points, - const unsigned nof_buckets, - const unsigned nof_buckets_to_compute, - const unsigned msm_idx_shift, - const unsigned c) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_buckets_to_compute) return; - unsigned msm_index = single_bucket_indices[tid] >> msm_idx_shift; - const unsigned single_bucket_index = (single_bucket_indices[tid] & ((1 << msm_idx_shift) - 1)); - unsigned bucket_index = msm_index * nof_buckets + single_bucket_index; - const unsigned bucket_offset = bucket_offsets[tid]; - const unsigned bucket_size = bucket_sizes[tid]; - - P bucket; // get rid of init buckets? no.. because what about buckets with no points - for (unsigned i = 0; i < bucket_size; - i++) { // add the relevant points starting from the relevant offset up to the bucket size - unsigned point_ind = point_indices[bucket_offset + i]; - A point = points[point_ind]; - bucket = - i ? (point == A::zero() ? bucket : bucket + point) : (point == A::zero() ? P::zero() : P::from_affine(point)); - } - buckets[bucket_index] = bucket; - } - - template - __global__ void accumulate_large_buckets_kernel( - P* __restrict__ buckets, - unsigned* __restrict__ bucket_offsets, - unsigned* __restrict__ bucket_sizes, - unsigned* __restrict__ large_bucket_thread_indices, - unsigned* __restrict__ point_indices, - A* __restrict__ points, - const unsigned nof_buckets_to_compute, - const unsigned c, - const int points_per_thread, - // log_nof_buckets_to_compute should be equal to ceil(log(nof_buckets_to_compute)) - const unsigned log_nof_buckets_to_compute, - const unsigned nof_threads) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_threads) return; - int bucket_segment_index = large_bucket_thread_indices[tid] >> log_nof_buckets_to_compute; - large_bucket_thread_indices[tid] &= ((1 << log_nof_buckets_to_compute) - 1); - int bucket_ind = large_bucket_thread_indices[tid]; - const unsigned bucket_offset = bucket_offsets[bucket_ind] + bucket_segment_index * points_per_thread; - const unsigned bucket_size = max(0, (int)bucket_sizes[bucket_ind] - bucket_segment_index * points_per_thread); - P bucket; - unsigned run_length = min(bucket_size, points_per_thread); - for (unsigned i = 0; i < run_length; - i++) { // add the relevant points starting from the relevant offset up to the bucket size - unsigned point_ind = point_indices[bucket_offset + i]; - A point = points[point_ind]; - bucket = - i ? (point == A::zero() ? bucket : bucket + point) : (point == A::zero() ? P::zero() : P::from_affine(point)); - } - buckets[tid] = run_length ? bucket : P::zero(); - } - - template - __global__ void distribute_large_buckets_kernel( - const P* large_buckets, - P* buckets, - const unsigned* sorted_bucket_sizes_sum, - const unsigned* single_bucket_indices, - const unsigned size, - const unsigned nof_buckets, - const unsigned msm_idx_shift) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= size) { return; } - - unsigned msm_index = single_bucket_indices[tid] >> msm_idx_shift; - unsigned bucket_index = msm_index * nof_buckets + (single_bucket_indices[tid] & ((1 << msm_idx_shift) - 1)); - unsigned large_bucket_index = sorted_bucket_sizes_sum[tid] + tid; - buckets[bucket_index] = large_buckets[large_bucket_index]; - } - - // this kernel sums the entire bucket module - // each thread deals with a single bucket module - template - __global__ void big_triangle_sum_kernel(const P* buckets, P* final_sums, unsigned nof_bms, unsigned c) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_bms) return; - unsigned buckets_in_bm = (1 << c); - P line_sum = buckets[(tid + 1) * buckets_in_bm - 1]; - final_sums[tid] = line_sum; - for (unsigned i = buckets_in_bm - 2; i > 0; i--) { - line_sum = line_sum + buckets[tid * buckets_in_bm + i]; // using the running sum method - final_sums[tid] = final_sums[tid] + line_sum; - } - } - - // this kernel uses single scalar multiplication to multiply each bucket by its index - // each thread deals with a single bucket - template - __global__ void ssm_buckets_kernel(P* buckets, unsigned* single_bucket_indices, unsigned nof_buckets, unsigned c) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_buckets) return; - unsigned bucket_index = single_bucket_indices[tid]; - S scalar_bucket_multiplier; - scalar_bucket_multiplier = { - bucket_index & ((1 << c) - 1), 0, 0, 0, 0, 0, 0, 0}; // the index without the bucket module index - buckets[bucket_index] = scalar_bucket_multiplier * buckets[bucket_index]; - } - - template - __global__ void last_pass_kernel( - const P* final_buckets, - P* final_sums, - unsigned nof_sums_per_batch, - unsigned batch_size, - unsigned nof_bms_per_batch, - unsigned orig_c) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_sums_per_batch * batch_size) return; - unsigned batch_index = tid / nof_sums_per_batch; - unsigned batch_tid = tid % nof_sums_per_batch; - unsigned bm_index = batch_tid / orig_c; - unsigned bm_tid = batch_tid % orig_c; - for (unsigned c = orig_c; c > 1;) { - c = (c + 1) >> 1; - bm_index <<= 1; - if (bm_tid >= c) { - bm_index++; - bm_tid -= c; - } - } - final_sums[tid] = final_buckets[2 * (batch_index * nof_bms_per_batch + bm_index) + 1]; - } - - // this kernel computes the final result using the double and add algorithm - // it is done by a single thread - template - __global__ void final_accumulation_kernel( - const P* final_sums, P* final_results, unsigned nof_msms, unsigned nof_results, unsigned c) - { - unsigned tid = (blockIdx.x * blockDim.x) + threadIdx.x; - if (tid >= nof_msms) return; - P final_result = P::zero(); - // Note: in some cases accumulation of bm is implemented such that some bms are known to be empty. Therefore - // skipping them. - for (unsigned i = nof_results; i > 1; i--) { - final_result = final_result + final_sums[i - 1 + tid * nof_results]; // add - for (unsigned j = 0; j < c; j++) // double - { - final_result = final_result + final_result; - } - } - final_results[tid] = final_result + final_sums[tid * nof_results]; - } - - // this function computes msm using the bucket method - template - cudaError_t bucket_method_msm( - unsigned bitsize, - unsigned c, - const S* scalars, - const A* points, - unsigned batch_size, // number of MSMs to compute - unsigned single_msm_size, // number of elements per MSM (a.k.a N) - unsigned nof_points, // number of EC points in 'points' array. Must be either (1) single_msm_size if MSMs are - // sharing points or (2) single_msm_size*batch_size otherwise - P* final_result, - bool are_scalars_on_device, - bool are_scalars_montgomery_form, - bool are_points_on_device, - bool are_points_montgomery_form, - bool are_results_on_device, - bool is_big_triangle, - int large_bucket_factor, - int precompute_factor, - bool is_async, - cudaStream_t stream) - { - CHK_INIT_IF_RETURN(); - - const unsigned nof_scalars = batch_size * single_msm_size; // assuming scalars not shared between batch elements - const bool is_nof_points_valid = ((single_msm_size * batch_size) % nof_points == 0); - if (!is_nof_points_valid) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "bucket_method_msm: #points must be divisible by single_msm_size*batch_size"); - } - - const S* d_scalars; - S* d_allocated_scalars = nullptr; - if (!are_scalars_on_device) { - // copy scalars to gpu - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_scalars, sizeof(S) * nof_scalars, stream)); - CHK_IF_RETURN( - cudaMemcpyAsync(d_allocated_scalars, scalars, sizeof(S) * nof_scalars, cudaMemcpyHostToDevice, stream)); - - if (are_scalars_montgomery_form) { - CHK_IF_RETURN(mont::from_montgomery(d_allocated_scalars, nof_scalars, stream, d_allocated_scalars)); - } - d_scalars = d_allocated_scalars; - } else { // already on device - if (are_scalars_montgomery_form) { - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_scalars, sizeof(S) * nof_scalars, stream)); - CHK_IF_RETURN(mont::from_montgomery(scalars, nof_scalars, stream, d_allocated_scalars)); - d_scalars = d_allocated_scalars; - } else { - d_scalars = scalars; - } - } - - unsigned total_bms_per_msm = (bitsize + c - 1) / c; - unsigned nof_bms_per_msm = (total_bms_per_msm - 1) / precompute_factor + 1; - unsigned input_indexes_count = nof_scalars * total_bms_per_msm; - - unsigned bm_bitsize = (unsigned)ceil(std::log2(nof_bms_per_msm)); - - unsigned* bucket_indices; - unsigned* point_indices; - unsigned* sorted_bucket_indices; - unsigned* sorted_point_indices; - CHK_IF_RETURN(cudaMallocAsync(&bucket_indices, sizeof(unsigned) * input_indexes_count, stream)); - CHK_IF_RETURN(cudaMallocAsync(&point_indices, sizeof(unsigned) * input_indexes_count, stream)); - CHK_IF_RETURN(cudaMallocAsync(&sorted_bucket_indices, sizeof(unsigned) * input_indexes_count, stream)); - CHK_IF_RETURN(cudaMallocAsync(&sorted_point_indices, sizeof(unsigned) * input_indexes_count, stream)); - - // split scalars into digits - unsigned NUM_THREADS = 1 << 10; - unsigned NUM_BLOCKS = (nof_scalars + NUM_THREADS - 1) / NUM_THREADS; - - split_scalars_kernel<<>>( - bucket_indices, point_indices, d_scalars, nof_scalars, nof_points, single_msm_size, total_bms_per_msm, - bm_bitsize, c, nof_bms_per_msm); - nof_points *= precompute_factor; - - // ------------------------------ Sorting routines for scalars start here ---------------------------------- - // sort indices - the indices are sorted from smallest to largest in order to group together the points that - // belong to each bucket - unsigned* sort_indices_temp_storage{}; - size_t sort_indices_temp_storage_bytes; - // The second to last parameter is the default value supplied explicitly to allow passing the stream - // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for - // more info - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairs( - sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices, sorted_bucket_indices, - point_indices, sorted_point_indices, input_indexes_count, 0, sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaMallocAsync(&sort_indices_temp_storage, sort_indices_temp_storage_bytes, stream)); - // The second to last parameter is the default value supplied explicitly to allow passing the stream - // See https://nvlabs.github.io/cub/structcub_1_1_device_radix_sort.html#a65e82152de448c6373ed9563aaf8af7e for - // more info - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairs( - sort_indices_temp_storage, sort_indices_temp_storage_bytes, bucket_indices, sorted_bucket_indices, - point_indices, sorted_point_indices, input_indexes_count, 0, sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaFreeAsync(sort_indices_temp_storage, stream)); - CHK_IF_RETURN(cudaFreeAsync(bucket_indices, stream)); - CHK_IF_RETURN(cudaFreeAsync(point_indices, stream)); - - // compute number of bucket modules and number of buckets in each module - unsigned nof_bms_in_batch = nof_bms_per_msm * batch_size; - // minus nof_bms_per_msm because zero bucket is not included in each bucket module - const unsigned nof_buckets = (nof_bms_per_msm << c) - nof_bms_per_msm; - const unsigned total_nof_buckets = nof_buckets * batch_size; - - // find bucket_sizes - unsigned* single_bucket_indices; - unsigned* bucket_sizes; - unsigned* nof_buckets_to_compute; - // +1 here and in other places because there still is zero index corresponding to zero bucket at this point - CHK_IF_RETURN(cudaMallocAsync(&single_bucket_indices, sizeof(unsigned) * (total_nof_buckets + 1), stream)); - CHK_IF_RETURN(cudaMallocAsync(&bucket_sizes, sizeof(unsigned) * (total_nof_buckets + 1), stream)); - CHK_IF_RETURN(cudaMallocAsync(&nof_buckets_to_compute, sizeof(unsigned), stream)); - unsigned* encode_temp_storage{}; - size_t encode_temp_storage_bytes = 0; - CHK_IF_RETURN(cub::DeviceRunLengthEncode::Encode( - encode_temp_storage, encode_temp_storage_bytes, sorted_bucket_indices, single_bucket_indices, bucket_sizes, - nof_buckets_to_compute, input_indexes_count, stream)); - CHK_IF_RETURN(cudaMallocAsync(&encode_temp_storage, encode_temp_storage_bytes, stream)); - CHK_IF_RETURN(cub::DeviceRunLengthEncode::Encode( - encode_temp_storage, encode_temp_storage_bytes, sorted_bucket_indices, single_bucket_indices, bucket_sizes, - nof_buckets_to_compute, input_indexes_count, stream)); - CHK_IF_RETURN(cudaFreeAsync(encode_temp_storage, stream)); - CHK_IF_RETURN(cudaFreeAsync(sorted_bucket_indices, stream)); - - // get offsets - where does each new bucket begin - unsigned* bucket_offsets; - CHK_IF_RETURN(cudaMallocAsync(&bucket_offsets, sizeof(unsigned) * (total_nof_buckets + 1), stream)); - unsigned* offsets_temp_storage{}; - size_t offsets_temp_storage_bytes = 0; - CHK_IF_RETURN(cub::DeviceScan::ExclusiveSum( - offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, total_nof_buckets + 1, stream)); - CHK_IF_RETURN(cudaMallocAsync(&offsets_temp_storage, offsets_temp_storage_bytes, stream)); - CHK_IF_RETURN(cub::DeviceScan::ExclusiveSum( - offsets_temp_storage, offsets_temp_storage_bytes, bucket_sizes, bucket_offsets, total_nof_buckets + 1, stream)); - CHK_IF_RETURN(cudaFreeAsync(offsets_temp_storage, stream)); - - // ----------- Starting to upload points (if they were on host) in parallel to scalar sorting ---------------- - const A* d_points; - A* d_allocated_points = nullptr; - cudaStream_t stream_points = nullptr; - if (!are_points_on_device || are_points_montgomery_form) CHK_IF_RETURN(cudaStreamCreate(&stream_points)); - if (!are_points_on_device) { - // copy points to gpu - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_points, sizeof(A) * nof_points, stream_points)); - CHK_IF_RETURN( - cudaMemcpyAsync(d_allocated_points, points, sizeof(A) * nof_points, cudaMemcpyHostToDevice, stream_points)); - - if (are_points_montgomery_form) { - CHK_IF_RETURN(mont::from_montgomery(d_allocated_points, nof_points, stream_points, d_allocated_points)); - } - d_points = d_allocated_points; - } else { // already on device - if (are_points_montgomery_form) { - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_points, sizeof(A) * nof_points, stream_points)); - CHK_IF_RETURN(mont::from_montgomery(points, nof_points, stream_points, d_allocated_points)); - d_points = d_allocated_points; - } else { - d_points = points; - } - } - - cudaEvent_t event_points_uploaded; - if (stream_points) { - CHK_IF_RETURN(cudaEventCreateWithFlags(&event_points_uploaded, cudaEventDisableTiming)); - CHK_IF_RETURN(cudaEventRecord(event_points_uploaded, stream_points)); - } - - P* buckets; - CHK_IF_RETURN(cudaMallocAsync(&buckets, sizeof(P) * (total_nof_buckets + nof_bms_in_batch), stream)); - - // launch the bucket initialization kernel with maximum threads - NUM_THREADS = 1 << 10; - NUM_BLOCKS = (total_nof_buckets + nof_bms_in_batch + NUM_THREADS - 1) / NUM_THREADS; - initialize_buckets_kernel<<>>(buckets, total_nof_buckets + nof_bms_in_batch); - - // removing zero bucket, if it exists - unsigned smallest_bucket_index; - CHK_IF_RETURN(cudaMemcpyAsync( - &smallest_bucket_index, single_bucket_indices, sizeof(unsigned), cudaMemcpyDeviceToHost, stream)); - // maybe zero bucket is empty after all? in this case zero_bucket_offset is set to 0 - unsigned zero_bucket_offset = (smallest_bucket_index == 0) ? 1 : 0; - - // sort by bucket sizes - unsigned h_nof_buckets_to_compute; - CHK_IF_RETURN(cudaMemcpyAsync( - &h_nof_buckets_to_compute, nof_buckets_to_compute, sizeof(unsigned), cudaMemcpyDeviceToHost, stream)); - CHK_IF_RETURN(cudaFreeAsync(nof_buckets_to_compute, stream)); - h_nof_buckets_to_compute -= zero_bucket_offset; - - unsigned* sorted_bucket_sizes; - CHK_IF_RETURN(cudaMallocAsync(&sorted_bucket_sizes, sizeof(unsigned) * h_nof_buckets_to_compute, stream)); - unsigned* sorted_bucket_offsets; - CHK_IF_RETURN(cudaMallocAsync(&sorted_bucket_offsets, sizeof(unsigned) * h_nof_buckets_to_compute, stream)); - unsigned* sort_offsets_temp_storage{}; - size_t sort_offsets_temp_storage_bytes = 0; - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairsDescending( - sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, bucket_sizes + zero_bucket_offset, - sorted_bucket_sizes, bucket_offsets + zero_bucket_offset, sorted_bucket_offsets, h_nof_buckets_to_compute, 0, - sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaMallocAsync(&sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, stream)); - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairsDescending( - sort_offsets_temp_storage, sort_offsets_temp_storage_bytes, bucket_sizes + zero_bucket_offset, - sorted_bucket_sizes, bucket_offsets + zero_bucket_offset, sorted_bucket_offsets, h_nof_buckets_to_compute, 0, - sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaFreeAsync(sort_offsets_temp_storage, stream)); - CHK_IF_RETURN(cudaFreeAsync(bucket_offsets, stream)); - - unsigned* sorted_single_bucket_indices; - CHK_IF_RETURN( - cudaMallocAsync(&sorted_single_bucket_indices, sizeof(unsigned) * h_nof_buckets_to_compute, stream)); - unsigned* sort_single_temp_storage{}; - size_t sort_single_temp_storage_bytes = 0; - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairsDescending( - sort_single_temp_storage, sort_single_temp_storage_bytes, bucket_sizes + zero_bucket_offset, - sorted_bucket_sizes, single_bucket_indices + zero_bucket_offset, sorted_single_bucket_indices, - h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaMallocAsync(&sort_single_temp_storage, sort_single_temp_storage_bytes, stream)); - CHK_IF_RETURN(cub::DeviceRadixSort::SortPairsDescending( - sort_single_temp_storage, sort_single_temp_storage_bytes, bucket_sizes + zero_bucket_offset, - sorted_bucket_sizes, single_bucket_indices + zero_bucket_offset, sorted_single_bucket_indices, - h_nof_buckets_to_compute, 0, sizeof(unsigned) * 8, stream)); - CHK_IF_RETURN(cudaFreeAsync(sort_single_temp_storage, stream)); - CHK_IF_RETURN(cudaFreeAsync(bucket_sizes, stream)); - CHK_IF_RETURN(cudaFreeAsync(single_bucket_indices, stream)); - - // find large buckets - unsigned average_bucket_size = (single_msm_size / (1 << c)) * precompute_factor; - // how large a bucket must be to qualify as a "large bucket" - unsigned bucket_th = large_bucket_factor * average_bucket_size; - unsigned* nof_large_buckets; - CHK_IF_RETURN(cudaMallocAsync(&nof_large_buckets, sizeof(unsigned), stream)); - CHK_IF_RETURN(cudaMemset(nof_large_buckets, 0, sizeof(unsigned))); - - unsigned TOTAL_THREADS = 129000; // TODO: device dependent - unsigned cutoff_run_length = max(2, h_nof_buckets_to_compute / TOTAL_THREADS); - unsigned cutoff_nof_runs = (h_nof_buckets_to_compute + cutoff_run_length - 1) / cutoff_run_length; - NUM_THREADS = 1 << 5; - NUM_BLOCKS = (cutoff_nof_runs + NUM_THREADS - 1) / NUM_THREADS; - if (h_nof_buckets_to_compute > 0 && bucket_th > 0) - find_cutoff_kernel<<>>( - sorted_bucket_sizes, h_nof_buckets_to_compute, bucket_th, cutoff_run_length, nof_large_buckets); - unsigned h_nof_large_buckets; - CHK_IF_RETURN( - cudaMemcpyAsync(&h_nof_large_buckets, nof_large_buckets, sizeof(unsigned), cudaMemcpyDeviceToHost, stream)); - CHK_IF_RETURN(cudaFreeAsync(nof_large_buckets, stream)); - - if (stream_points) { - // by this point, points need to be already uploaded and un-Montgomeried - CHK_IF_RETURN(cudaStreamWaitEvent(stream, event_points_uploaded)); - CHK_IF_RETURN(cudaEventDestroy(event_points_uploaded)); - CHK_IF_RETURN(cudaStreamDestroy(stream_points)); - } - - cudaStream_t stream_large_buckets; - cudaEvent_t event_large_buckets_accumulated; - // ---------------- This is where handling of large buckets happens (if there are any) ------------- - if (h_nof_large_buckets > 0 && bucket_th > 0) { - CHK_IF_RETURN(cudaStreamCreate(&stream_large_buckets)); - CHK_IF_RETURN(cudaEventCreateWithFlags(&event_large_buckets_accumulated, cudaEventDisableTiming)); - - unsigned* sorted_bucket_sizes_sum; - CHK_IF_RETURN(cudaMallocAsync( - &sorted_bucket_sizes_sum, sizeof(unsigned) * (h_nof_large_buckets + 1), stream_large_buckets)); - CHK_IF_RETURN(cudaMemsetAsync(sorted_bucket_sizes_sum, 0, sizeof(unsigned), stream_large_buckets)); - unsigned* large_bucket_temp_storage{}; - size_t large_bucket_temp_storage_bytes = 0; - CHK_IF_RETURN(cub::DeviceScan::InclusiveSum( - large_bucket_temp_storage, large_bucket_temp_storage_bytes, sorted_bucket_sizes, sorted_bucket_sizes_sum + 1, - h_nof_large_buckets, stream_large_buckets)); - CHK_IF_RETURN( - cudaMallocAsync(&large_bucket_temp_storage, large_bucket_temp_storage_bytes, stream_large_buckets)); - CHK_IF_RETURN(cub::DeviceScan::InclusiveSum( - large_bucket_temp_storage, large_bucket_temp_storage_bytes, sorted_bucket_sizes, sorted_bucket_sizes_sum + 1, - h_nof_large_buckets, stream_large_buckets)); - CHK_IF_RETURN(cudaFreeAsync(large_bucket_temp_storage, stream_large_buckets)); - unsigned h_nof_pts_in_large_buckets; - CHK_IF_RETURN(cudaMemcpyAsync( - &h_nof_pts_in_large_buckets, sorted_bucket_sizes_sum + h_nof_large_buckets, sizeof(unsigned), - cudaMemcpyDeviceToHost, stream_large_buckets)); - unsigned h_largest_bucket; - CHK_IF_RETURN(cudaMemcpyAsync( - &h_largest_bucket, sorted_bucket_sizes, sizeof(unsigned), cudaMemcpyDeviceToHost, stream_large_buckets)); - - // the number of threads for large buckets has an extra h_nof_large_buckets term to account for bucket sizes - // unevenly divisible by average_bucket_size. there are similar corrections elsewhere when accessing large - // buckets - unsigned large_buckets_nof_threads = - (h_nof_pts_in_large_buckets + average_bucket_size - 1) / average_bucket_size + h_nof_large_buckets; - unsigned log_nof_large_buckets = (unsigned)ceil(std::log2(h_nof_large_buckets)); - unsigned* large_bucket_indices; - CHK_IF_RETURN(cudaMallocAsync(&large_bucket_indices, sizeof(unsigned) * large_buckets_nof_threads, stream)); - NUM_THREADS = max(1, min(1 << 8, h_nof_large_buckets)); - NUM_BLOCKS = (h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS; - initialize_large_bucket_indices

<<>>( - sorted_bucket_sizes_sum, average_bucket_size, h_nof_large_buckets, log_nof_large_buckets, - large_bucket_indices); - - P* large_buckets; - CHK_IF_RETURN(cudaMallocAsync(&large_buckets, sizeof(P) * large_buckets_nof_threads, stream_large_buckets)); - - NUM_THREADS = max(1, min(1 << 8, large_buckets_nof_threads)); - NUM_BLOCKS = (large_buckets_nof_threads + NUM_THREADS - 1) / NUM_THREADS; - accumulate_large_buckets_kernel<<>>( - large_buckets, sorted_bucket_offsets, sorted_bucket_sizes, large_bucket_indices, sorted_point_indices, - d_points, h_nof_large_buckets, c, average_bucket_size, log_nof_large_buckets, large_buckets_nof_threads); - - NUM_THREADS = max(1, min(MAX_TH, h_nof_large_buckets)); - NUM_BLOCKS = (h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS; - // normalization is needed to update buckets sizes and offsets due to reduction that already took place - normalize_kernel<<>>( - sorted_bucket_sizes_sum, average_bucket_size, h_nof_large_buckets); - // reduce - for (int s = h_largest_bucket; s > 1; s = ((s + 1) >> 1)) { - NUM_THREADS = max(1, min(MAX_TH, h_nof_large_buckets)); - NUM_BLOCKS = (h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS; - normalize_kernel<<>>( - sorted_bucket_sizes, s == h_largest_bucket ? average_bucket_size : 2, h_nof_large_buckets); - NUM_THREADS = max(1, min(MAX_TH, large_buckets_nof_threads)); - NUM_BLOCKS = (large_buckets_nof_threads + NUM_THREADS - 1) / NUM_THREADS; - sum_reduction_variable_size_kernel<<>>( - large_buckets, sorted_bucket_sizes_sum, sorted_bucket_sizes, large_bucket_indices, - large_buckets_nof_threads); - } - CHK_IF_RETURN(cudaFreeAsync(large_bucket_indices, stream_large_buckets)); - - // distribute - NUM_THREADS = max(1, min(MAX_TH, h_nof_large_buckets)); - NUM_BLOCKS = (h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS; - distribute_large_buckets_kernel<<>>( - large_buckets, buckets, sorted_bucket_sizes_sum, sorted_single_bucket_indices, h_nof_large_buckets, - nof_buckets + nof_bms_per_msm, c + bm_bitsize); - CHK_IF_RETURN(cudaFreeAsync(large_buckets, stream_large_buckets)); - CHK_IF_RETURN(cudaFreeAsync(sorted_bucket_sizes_sum, stream_large_buckets)); - - CHK_IF_RETURN(cudaEventRecord(event_large_buckets_accumulated, stream_large_buckets)); - } - - // ------------------------- Accumulation of (non-large) buckets --------------------------------- - if (h_nof_buckets_to_compute > h_nof_large_buckets) { - NUM_THREADS = 1 << 8; - NUM_BLOCKS = (h_nof_buckets_to_compute - h_nof_large_buckets + NUM_THREADS - 1) / NUM_THREADS; - // launch the accumulation kernel with maximum threads - accumulate_buckets_kernel<<>>( - buckets, sorted_bucket_offsets + h_nof_large_buckets, sorted_bucket_sizes + h_nof_large_buckets, - sorted_single_bucket_indices + h_nof_large_buckets, sorted_point_indices, d_points, - nof_buckets + nof_bms_per_msm, h_nof_buckets_to_compute - h_nof_large_buckets, c + bm_bitsize, c); - } - CHK_IF_RETURN(cudaFreeAsync(sorted_point_indices, stream)); - CHK_IF_RETURN(cudaFreeAsync(sorted_bucket_sizes, stream)); - CHK_IF_RETURN(cudaFreeAsync(sorted_bucket_offsets, stream)); - CHK_IF_RETURN(cudaFreeAsync(sorted_single_bucket_indices, stream)); - if (h_nof_large_buckets > 0 && bucket_th > 0) { - // all the large buckets need to be accumulated before the final summation - CHK_IF_RETURN(cudaStreamWaitEvent(stream, event_large_buckets_accumulated)); - CHK_IF_RETURN(cudaStreamDestroy(stream_large_buckets)); - } - - P* d_allocated_final_result = nullptr; - if (!are_results_on_device) - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_final_result, sizeof(P) * batch_size, stream)); - - // --- Reduction of buckets happens here, after this we'll get a single sum for each bucket module/window --- - unsigned nof_final_results_per_msm = - nof_bms_per_msm; // for big-triangle accumluation this is the number of bucket modules - P* final_results; - if (is_big_triangle || c == 1) { - CHK_IF_RETURN(cudaMallocAsync(&final_results, sizeof(P) * nof_bms_in_batch, stream)); - // launch the bucket module sum kernel - a thread for each bucket module - NUM_THREADS = 32; - NUM_BLOCKS = (nof_bms_in_batch + NUM_THREADS - 1) / NUM_THREADS; - big_triangle_sum_kernel<<>>(buckets, final_results, nof_bms_in_batch, c); - } else { - // the iterative reduction algorithm works with 2 types of reduction that can run on parallel streams - cudaStream_t stream_reduction; - cudaEvent_t event_finished_reduction; - CHK_IF_RETURN(cudaStreamCreate(&stream_reduction)); - CHK_IF_RETURN(cudaEventCreateWithFlags(&event_finished_reduction, cudaEventDisableTiming)); - - unsigned source_bits_count = c; - unsigned source_windows_count = nof_bms_per_msm; - unsigned source_buckets_count = nof_buckets + nof_bms_per_msm; // nof buckets per msm including zero buckets - unsigned target_windows_count; - P* source_buckets = buckets; - buckets = nullptr; - P* target_buckets; - P* temp_buckets1; - P* temp_buckets2; - for (unsigned i = 0;; i++) { - const unsigned target_bits_count = (source_bits_count + 1) >> 1; // half the bits rounded up - target_windows_count = source_windows_count << 1; // twice the number of bms - const unsigned target_buckets_count = target_windows_count << target_bits_count; // new_bms*2^new_c - CHK_IF_RETURN(cudaMallocAsync(&target_buckets, sizeof(P) * target_buckets_count * batch_size, stream)); - CHK_IF_RETURN(cudaMallocAsync( - &temp_buckets1, sizeof(P) * source_buckets_count * batch_size, - stream)); // for type1 reduction (strided, bottom window - evens) - CHK_IF_RETURN(cudaMallocAsync( - &temp_buckets2, sizeof(P) * source_buckets_count * batch_size, - stream)); // for type2 reduction (serial, top window - odds) - initialize_buckets_kernel<<<(target_buckets_count * batch_size + 255) / 256, 256>>>( - target_buckets, target_buckets_count * batch_size); // initialization is needed for the odd c case - - for (unsigned j = 0; j < target_bits_count; j++) { - const bool is_first_iter = (j == 0); - const bool is_second_iter = (j == 1); - const bool is_last_iter = (j == target_bits_count - 1); - const bool is_odd_c = source_bits_count & 1; - unsigned nof_threads = - (((source_windows_count << target_bits_count) - source_windows_count) << (target_bits_count - 1 - j)) * - batch_size; // nof sections to reduce (minus the section that goes to zero buckets) shifted by nof threads - // per section - NUM_THREADS = max(1, min(MAX_TH, nof_threads)); - NUM_BLOCKS = (nof_threads + NUM_THREADS - 1) / NUM_THREADS; - if (!is_odd_c || !is_first_iter) { // skip if c is odd and it's the first iteration - single_stage_multi_reduction_kernel<<>>( - is_first_iter || (is_second_iter && is_odd_c) ? source_buckets : temp_buckets1, - is_last_iter ? target_buckets : temp_buckets1, 1 << source_bits_count, - 1 << (source_bits_count - j + (is_odd_c ? 1 : 0)), is_last_iter ? 1 << target_bits_count : 0, - 1 << target_bits_count, 0 /*=write_phase*/, (1 << target_bits_count) - 1, nof_threads); - } - - nof_threads = (((source_windows_count << (source_bits_count - target_bits_count)) - source_windows_count) - << (target_bits_count - 1 - j)) * - batch_size; // nof sections to reduce (minus the section that goes to zero buckets) shifted by - // nof threads per section - NUM_THREADS = max(1, min(MAX_TH, nof_threads)); - NUM_BLOCKS = (nof_threads + NUM_THREADS - 1) / NUM_THREADS; - single_stage_multi_reduction_kernel<<>>( - is_first_iter ? source_buckets : temp_buckets2, is_last_iter ? target_buckets : temp_buckets2, - 1 << target_bits_count, 1 << (target_bits_count - j), is_last_iter ? 1 << target_bits_count : 0, - 1 << (target_bits_count - (is_odd_c ? 1 : 0)), 1 /*=write_phase*/, - (1 << (target_bits_count - (is_odd_c ? 1 : 0))) - 1, nof_threads); - } - CHK_IF_RETURN(cudaEventRecord(event_finished_reduction, stream_reduction)); - CHK_IF_RETURN( - cudaStreamWaitEvent(stream, event_finished_reduction)); // sync streams after every write to target_buckets - if (target_bits_count == 1) { - // Note: the reduction ends up with 'target_windows_count' windows per batch element. Some are guaranteed - // to be empty when target_windows_count>bitsize. for example consider bitsize=253 and c=2. The reduction - // ends with 254 bms but the most significant one is guaranteed to be zero since the scalars are 253b. - // precomputation and odd c can cause additional empty windows. - nof_final_results_per_msm = min(c * nof_bms_per_msm, bitsize); - nof_bms_per_msm = target_windows_count; - unsigned total_nof_final_results = nof_final_results_per_msm * batch_size; - - CHK_IF_RETURN(cudaMallocAsync(&final_results, sizeof(P) * total_nof_final_results, stream)); - - NUM_THREADS = 32; - NUM_BLOCKS = (total_nof_final_results + NUM_THREADS - 1) / NUM_THREADS; - last_pass_kernel<<>>( - target_buckets, final_results, nof_final_results_per_msm, batch_size, nof_bms_per_msm, c); - c = 1; - CHK_IF_RETURN(cudaFreeAsync(source_buckets, stream)); - CHK_IF_RETURN(cudaFreeAsync(target_buckets, stream)); - CHK_IF_RETURN(cudaFreeAsync(temp_buckets1, stream)); - CHK_IF_RETURN(cudaFreeAsync(temp_buckets2, stream)); - CHK_IF_RETURN(cudaStreamDestroy(stream_reduction)); - break; - } - CHK_IF_RETURN(cudaFreeAsync(source_buckets, stream)); - CHK_IF_RETURN(cudaFreeAsync(temp_buckets1, stream)); - CHK_IF_RETURN(cudaFreeAsync(temp_buckets2, stream)); - source_buckets = target_buckets; - target_buckets = nullptr; - temp_buckets1 = nullptr; - temp_buckets2 = nullptr; - source_bits_count = target_bits_count; - source_windows_count = target_windows_count; - source_buckets_count = target_buckets_count; - } - } - - // ------- This is the final stage where bucket modules/window sums get added up with appropriate weights - // ------- - NUM_THREADS = 32; - NUM_BLOCKS = (batch_size + NUM_THREADS - 1) / NUM_THREADS; - // launch the double and add kernel, a single thread per batch element - final_accumulation_kernel<<>>( - final_results, are_results_on_device ? final_result : d_allocated_final_result, batch_size, - nof_final_results_per_msm, c); - CHK_IF_RETURN(cudaFreeAsync(final_results, stream)); - - if (!are_results_on_device) - CHK_IF_RETURN(cudaMemcpyAsync( - final_result, d_allocated_final_result, sizeof(P) * batch_size, cudaMemcpyDeviceToHost, stream)); - - // free memory - if (d_allocated_scalars) CHK_IF_RETURN(cudaFreeAsync(d_allocated_scalars, stream)); - if (d_allocated_points) CHK_IF_RETURN(cudaFreeAsync(d_allocated_points, stream)); - if (d_allocated_final_result) CHK_IF_RETURN(cudaFreeAsync(d_allocated_final_result, stream)); - CHK_IF_RETURN(cudaFreeAsync(buckets, stream)); - - if (!is_async) CHK_IF_RETURN(cudaStreamSynchronize(stream)); - - return CHK_LAST(); - } - } // namespace - - template - cudaError_t msm(const S* scalars, const A* points, int msm_size, MSMConfig& config, P* results) - { - const int bitsize = (config.bitsize == 0) ? S::NBITS : config.bitsize; - cudaStream_t& stream = config.ctx.stream; - - unsigned c = (config.c == 0) ? get_optimal_c(msm_size) : config.c; - - return CHK_STICKY(bucket_method_msm( - bitsize, c, scalars, points, config.batch_size, msm_size, - (config.points_size == 0) ? msm_size : config.points_size, results, config.are_scalars_on_device, - config.are_scalars_montgomery_form, config.are_points_on_device, config.are_points_montgomery_form, - config.are_results_on_device, config.is_big_triangle, config.large_bucket_factor, config.precompute_factor, - config.is_async, stream)); - } - - template - cudaError_t precompute_msm_points(A* points, int msm_size, MSMConfig& config, A* output_points) - { - CHK_INIT_IF_RETURN(); - - cudaStream_t& stream = config.ctx.stream; - unsigned c = (config.c == 0) ? get_optimal_c(msm_size) : config.c; - - CHK_IF_RETURN(cudaMemcpyAsync( - output_points, points, sizeof(A) * config.points_size, - config.are_points_on_device ? cudaMemcpyDeviceToDevice : cudaMemcpyHostToDevice, stream)); - - unsigned total_nof_bms = (P::SCALAR_FF_NBITS - 1) / c + 1; - unsigned shift = c * ((total_nof_bms - 1) / config.precompute_factor + 1); - - unsigned NUM_THREADS = 1 << 8; - unsigned NUM_BLOCKS = (config.points_size + NUM_THREADS - 1) / NUM_THREADS; - for (int i = 1; i < config.precompute_factor; i++) { - left_shift_kernel<<>>( - &output_points[(i - 1) * config.points_size], shift, config.points_size, - &output_points[i * config.points_size]); - } - - return CHK_LAST(); - } - - template - [[deprecated("Use precompute_msm_points instead.")]] cudaError_t precompute_msm_bases( - A* bases, - int bases_size, - int precompute_factor, - int _c, - bool are_bases_on_device, - device_context::DeviceContext& ctx, - A* output_bases) - { - CHK_INIT_IF_RETURN(); - - cudaStream_t& stream = ctx.stream; - - CHK_IF_RETURN(cudaMemcpyAsync( - output_bases, bases, sizeof(A) * bases_size, - are_bases_on_device ? cudaMemcpyDeviceToDevice : cudaMemcpyHostToDevice, stream)); - - unsigned c = 16; - unsigned total_nof_bms = (P::SCALAR_FF_NBITS - 1) / c + 1; - unsigned shift = c * ((total_nof_bms - 1) / precompute_factor + 1); - - unsigned NUM_THREADS = 1 << 8; - unsigned NUM_BLOCKS = (bases_size + NUM_THREADS - 1) / NUM_THREADS; - for (int i = 1; i < precompute_factor; i++) { - left_shift_kernel<<>>( - &output_bases[(i - 1) * bases_size], shift, bases_size, &output_bases[i * bases_size]); - } - - return CHK_LAST(); - } -} // namespace msm \ No newline at end of file diff --git a/icicle/src/msm/tests/msm_test.cu b/icicle/src/msm/tests/msm_test.cu deleted file mode 100644 index 005a00c52..000000000 --- a/icicle/src/msm/tests/msm_test.cu +++ /dev/null @@ -1,295 +0,0 @@ -#include "fields/id.h" -// #define FIELD_ID 2 -#define CURVE_ID 3 -#include "curves/curve_config.cuh" -// #include "fields/field_config.cuh" - -#include "msm.cu" - -#include -#include -#include - -#include "curves/params/bn254.cuh" -#include "fields/field.cuh" -#include "curves/projective.cuh" -#include "gpu-utils/device_context.cuh" - -// using namespace bn254; - -class Dummy_Scalar -{ -public: - static constexpr unsigned NBITS = 32; - - unsigned x; - unsigned p = 10; - // unsigned p = 1<<30; - - static HOST_DEVICE_INLINE Dummy_Scalar zero() { return {0}; } - - static HOST_DEVICE_INLINE Dummy_Scalar one() { return {1}; } - - friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Dummy_Scalar& scalar) - { - os << scalar.x; - return os; - } - - HOST_DEVICE_INLINE unsigned get_scalar_digit(unsigned digit_num, unsigned digit_width) const - { - return (x >> (digit_num * digit_width)) & ((1 << digit_width) - 1); - } - - friend HOST_DEVICE_INLINE Dummy_Scalar operator+(Dummy_Scalar p1, const Dummy_Scalar& p2) - { - return {(p1.x + p2.x) % p1.p}; - } - - friend HOST_DEVICE_INLINE bool operator==(const Dummy_Scalar& p1, const Dummy_Scalar& p2) { return (p1.x == p2.x); } - - friend HOST_DEVICE_INLINE bool operator==(const Dummy_Scalar& p1, const unsigned p2) { return (p1.x == p2); } - - static HOST_DEVICE_INLINE Dummy_Scalar neg(const Dummy_Scalar& scalar) { return {scalar.p - scalar.x}; } - static HOST_INLINE Dummy_Scalar rand_host() - { - return {(unsigned)rand() % 10}; - // return {(unsigned)rand()}; - } -}; - -class Dummy_Projective -{ -public: - Dummy_Scalar x; - - static HOST_DEVICE_INLINE Dummy_Projective zero() { return {0}; } - - static HOST_DEVICE_INLINE Dummy_Projective one() { return {1}; } - - static HOST_DEVICE_INLINE Dummy_Projective to_affine(const Dummy_Projective& point) { return {point.x}; } - - static HOST_DEVICE_INLINE Dummy_Projective from_affine(const Dummy_Projective& point) { return {point.x}; } - - static HOST_DEVICE_INLINE Dummy_Projective neg(const Dummy_Projective& point) { return {Dummy_Scalar::neg(point.x)}; } - - friend HOST_DEVICE_INLINE Dummy_Projective operator+(Dummy_Projective p1, const Dummy_Projective& p2) - { - return {p1.x + p2.x}; - } - - // friend HOST_DEVICE_INLINE Dummy_Projective operator-(Dummy_Projective p1, const Dummy_Projective& p2) { - // return p1 + neg(p2); - // } - - friend HOST_INLINE std::ostream& operator<<(std::ostream& os, const Dummy_Projective& point) - { - os << point.x; - return os; - } - - friend HOST_DEVICE_INLINE Dummy_Projective operator*(Dummy_Scalar scalar, const Dummy_Projective& point) - { - Dummy_Projective res = zero(); -#ifdef CUDA_ARCH - UNROLL -#endif - for (int i = 0; i < Dummy_Scalar::NBITS; i++) { - if (i > 0) { res = res + res; } - if (scalar.get_scalar_digit(Dummy_Scalar::NBITS - i - 1, 1)) { res = res + point; } - } - return res; - } - - friend HOST_DEVICE_INLINE bool operator==(const Dummy_Projective& p1, const Dummy_Projective& p2) - { - return (p1.x == p2.x); - } - - static HOST_DEVICE_INLINE bool is_zero(const Dummy_Projective& point) { return point.x == 0; } - - static HOST_INLINE Dummy_Projective rand_host() - { - return {(unsigned)rand() % 10}; - // return {(unsigned)rand()}; - } -}; - -// switch between dummy and real: - -// typedef scalar_t test_scalar; -// typedef projective_t test_projective; -// typedef affine_t test_affine; - -typedef curve_config::scalar_t test_scalar; -typedef curve_config::projective_t test_projective; -typedef curve_config::affine_t test_affine; - -// typedef Dummy_Scalar test_scalar; -// typedef Dummy_Projective test_projective; -// typedef Dummy_Projective test_affine; - -int main(int argc, char** argv) -{ - cudaEvent_t start, stop; - float msm_time; - - int msm_log_size = (argc > 1) ? atoi(argv[1]) : 17; - int msm_size = 1 << msm_log_size; - int batch_size = (argc > 2) ? atoi(argv[2]) : 4; - // unsigned msm_size = 1<<21; - int N = batch_size * msm_size; - int precomp_factor = (argc > 3) ? atoi(argv[3]) : 1; - int user_c = (argc > 4) ? atoi(argv[4]) : 15; - - printf( - "running msm curve=%d, 2^%d, batch_size=%d, precomp_factor=%d, c=%d\n", CURVE_ID, msm_log_size, batch_size, - precomp_factor, user_c); - - test_scalar* scalars = new test_scalar[N]; - test_affine* points = new test_affine[N]; - - test_scalar::rand_host_many(scalars, N); - test_projective::rand_host_many_affine(points, N); - - std::cout << "finished generating" << std::endl; - - // projective_t *short_res = (projective_t*)malloc(sizeof(projective_t)); - // test_projective *large_res = (test_projective*)malloc(sizeof(test_projective)); - test_projective res[batch_size]; - test_projective ref[batch_size]; - // test_projective batched_large_res[batch_size]; - // fake_point *large_res = (fake_point*)malloc(sizeof(fake_point)); - // fake_point batched_large_res[256]; - - // short_msm(scalars, points, N, short_res); - // for (unsigned i=0;i(scalars+msm_size*i, points+msm_size*i, msm_size, large_res+i, - // false); std::cout<<"final result large"<(scalars, points, batch_size, msm_size, - // batched_large_res, false); - cudaStream_t stream; - cudaStreamCreate(&stream); - - device_context::DeviceContext ctx = { - stream, // stream - 0, // device_id - 0, // mempool - }; - msm::MSMConfig config = { - ctx, // DeviceContext - N, // points_size - precomp_factor, // precompute_factor - user_c, // c - 0, // bitsize - 10, // large_bucket_factor - batch_size, // batch_size - false, // are_scalars_on_device - false, // are_scalars_montgomery_form - true, // are_points_on_device - false, // are_points_montgomery_form - true, // are_results_on_device - false, // is_big_triangle - true, // is_async - // false, // segments_reduction - }; - - cudaEventCreate(&start); - cudaEventCreate(&stop); - - if (precomp_factor > 1) - msm::precompute_msm_points(points_d, msm_size, config, precomp_points_d); - - // warm up - msm::msm( - scalars, precomp_factor > 1 ? precomp_points_d : points_d, msm_size, config, res_d); - cudaDeviceSynchronize(); - - // auto begin1 = std::chrono::high_resolution_clock::now(); - cudaEventRecord(start, stream); - msm::msm( - scalars, precomp_factor > 1 ? precomp_points_d : points_d, msm_size, config, res_d); - cudaEventRecord(stop, stream); - cudaStreamSynchronize(stream); - cudaEventElapsedTime(&msm_time, start, stop); - // cudaEvent_t msm_end_event; - // cudaEventCreate(&msm_end_event); - // auto end1 = std::chrono::high_resolution_clock::now(); - // auto elapsed1 = std::chrono::duration_cast(end1 - begin1); - printf("msm time : %.3f ms.\n", msm_time); - - // reference - config.c = 16; - config.precompute_factor = 1; - config.is_big_triangle = true; - config.batch_size = 1; - config.points_size = msm_size; - // config.segments_reduction = false; - for (int i = 0; i < batch_size; i++) { - msm::msm( - scalars + i * msm_size, points_d + i * msm_size, msm_size, config, ref_d + i); - } - - // config.are_results_on_device = false; - // std::cout << test_projective::to_affine(large_res[0]) << std::endl; - // auto begin = std::chrono::high_resolution_clock::now(); - // msm::MSM(scalars_d, points_d, msm_size, config, large_res); - // test_reduce_triangle(scalars); - // test_reduce_rectangle(scalars); - // test_reduce_single(scalars); - // test_reduce_var(scalars); - // auto end = std::chrono::high_resolution_clock::now(); - // auto elapsed = std::chrono::duration_cast(end - begin); - // printf("Big Triangle: %.3f seconds.\n", elapsed.count() * 1e-9); - cudaStreamSynchronize(stream); - cudaStreamDestroy(stream); - - // std::cout << test_projective::to_affine(large_res[0]) << std::endl; - - cudaMemcpy(res, res_d, sizeof(test_projective) * batch_size, cudaMemcpyDeviceToHost); - cudaMemcpy(ref, ref_d, sizeof(test_projective) * batch_size, cudaMemcpyDeviceToHost); - - // reference_msm(scalars, points, msm_size); - - // std::cout<<"final results batched large"<* config, scalar_t* output) + { + return NttDispatcher::execute(input, size, dir, *config, output); + } + + template <> + eIcicleError ntt(const scalar_t* input, int size, NTTDir dir, const NTTConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, ntt)(input, size, dir, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(NttExtFieldDispatcher, extension_ntt, NttExtFieldImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_ntt)( + const extension_t* input, int size, NTTDir dir, const NTTConfig* config, extension_t* output) + { + return NttExtFieldDispatcher::execute(input, size, dir, *config, output); + } + + template <> + eIcicleError + ntt(const extension_t* input, int size, NTTDir dir, const NTTConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_ntt)(input, size, dir, &config, output); + } +#endif // EXT_FIELD + + /*************************** INIT DOMAIN ***************************/ + ICICLE_DISPATCHER_INST(NttInitDomainDispatcher, ntt_init_domain, NttInitDomainImpl); + + extern "C" eIcicleError + CONCAT_EXPAND(FIELD, ntt_init_domain)(const scalar_t* primitive_root, const NTTInitDomainConfig* config) + { + return NttInitDomainDispatcher::execute(*primitive_root, *config); + } + + template <> + eIcicleError ntt_init_domain(const scalar_t& primitive_root, const NTTInitDomainConfig& config) + { + return CONCAT_EXPAND(FIELD, ntt_init_domain)(&primitive_root, &config); + } + + /*************************** RELEASE DOMAIN ***************************/ + ICICLE_DISPATCHER_INST(NttReleaseDomainDispatcher, ntt_release_domain, NttReleaseDomainImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, ntt_release_domain)() + { + // Note: passing zero is a workaround for the function required per field but need to differentiate by type when + // calling + return NttReleaseDomainDispatcher::execute(scalar_t::zero()); + } + + template <> + eIcicleError ntt_release_domain() + { + return CONCAT_EXPAND(FIELD, ntt_release_domain)(); + } + + /*************************** GET ROOT OF UNITY ***************************/ + extern "C" eIcicleError CONCAT_EXPAND(FIELD, get_root_of_unity)(uint64_t max_size, scalar_t* rou) + { + const auto log_max_size = static_cast(std::ceil(std::log2(max_size))); + if (scalar_t::get_omegas_count() < log_max_size) { + ICICLE_LOG_ERROR << "no root-of-unity of order " << log_max_size << " in field " << typeid(scalar_t).name(); + return eIcicleError::INVALID_ARGUMENT; + } + *rou = scalar_t::omega(log_max_size); + return eIcicleError::SUCCESS; + } + + template <> + eIcicleError get_root_of_unity(uint64_t max_size, scalar_t* rou) + { + return CONCAT_EXPAND(FIELD, get_root_of_unity)(max_size, rou); + } + + /*************************** GET ROOT OF UNITY FROM DOMAIN ***************************/ + ICICLE_DISPATCHER_INST(NttRouFromDomainDispatcher, ntt_get_rou_from_domain, NttGetRouFromDomainImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, get_root_of_unity_from_domain)(uint64_t logn, scalar_t* rou) + { + return NttRouFromDomainDispatcher::execute(logn, rou); + } + + template <> + eIcicleError get_root_of_unity_from_domain(uint64_t logn, scalar_t* rou) + { + return CONCAT_EXPAND(FIELD, get_root_of_unity_from_domain)(logn, rou); + } + +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/ntt/Makefile b/icicle/src/ntt/Makefile deleted file mode 100644 index 4d31429b0..000000000 --- a/icicle/src/ntt/Makefile +++ /dev/null @@ -1,13 +0,0 @@ -build_verification: - mkdir -p work - nvcc -o work/test_verification -I. -I../../include tests/verification.cu -std=c++17 - -test_verification: build_verification - work/test_verification - -build_verification_ecntt: - mkdir -p work - nvcc -o work/test_verification_ecntt -I. -I../../include tests/verification.cu -std=c++17 -DECNTT - -test_verification_ecntt: build_verification_ecntt - work/test_verification_ecntt diff --git a/icicle/src/ntt/extern.cu b/icicle/src/ntt/extern.cu deleted file mode 100644 index e64da6e30..000000000 --- a/icicle/src/ntt/extern.cu +++ /dev/null @@ -1,58 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "ntt.cu" - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" - -namespace ntt { - /** - * Extern "C" version of [init_domain](@ref init_domain) function with the following - * value of template parameter (where the field is given by `-DFIELD` env variable during build): - * - `S` is the [field](@ref scalar_t) - either a scalar field of the elliptic curve or a - * stand-alone "STARK field"; - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, initialize_domain)( - scalar_t* primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode) - { - return init_domain(*primitive_root, ctx, fast_twiddles_mode); - } - - /** - * Extern "C" version of [ntt](@ref ntt) function with the following values of template parameters - * (where the field is given by `-DFIELD` env variable during build): - * - `S` is the [field](@ref scalar_t) - either a scalar field of the elliptic curve or a - * stand-alone "STARK field"; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, ntt_cuda)( - const scalar_t* input, int size, NTTDir dir, NTTConfig& config, scalar_t* output) - { - return ntt(input, size, dir, config, output); - } - - /** - * Extern "C" version of [release_domain](@ref release_domain) function with the following values of template - * parameters (where the field is given by `-DFIELD` env variable during build): - * - `S` is the [field](@ref scalar_t) - either a scalar field of the elliptic curve or a - * stand-alone "STARK field"; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, release_domain)(device_context::DeviceContext& ctx) - { - return release_domain(ctx); - } - - /** - * Extern "C" version of [get_root_of_unity](@ref get_root_of_unity) function with the following - * value of template parameter (where the field is given by `-DFIELD` env variable during build): - * - `S` is the [field](@ref scalar_t) - either a scalar field of the elliptic curve or a - * stand-alone "STARK field"; - */ - extern "C" scalar_t CONCAT_EXPAND(FIELD, get_root_of_unity)(uint32_t logn) - { - return get_root_of_unity(logn); - } -} // namespace ntt \ No newline at end of file diff --git a/icicle/src/ntt/extern_ecntt.cu b/icicle/src/ntt/extern_ecntt.cu deleted file mode 100644 index 72a4e42bb..000000000 --- a/icicle/src/ntt/extern_ecntt.cu +++ /dev/null @@ -1,25 +0,0 @@ -#include "curves/curve_config.cuh" -#include "fields/field_config.cuh" - -using namespace curve_config; -using namespace field_config; - -#include "ntt.cu" - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" - -namespace ntt { - /** - * Extern "C" version of [ntt](@ref ntt) function with the following values of template parameters - * (where the curve is given by `-DCURVE` env variable during build): - * - `S` is the [projective representation](@ref projective_t) of the curve (i.e. EC NTT is computed); - * - `E` is the [scalar field](@ref scalar_t) of the curve; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(CURVE, ecntt_cuda)( - const projective_t* input, int size, NTTDir dir, NTTConfig& config, projective_t* output) - { - return ntt(input, size, dir, config, output); - } -} // namespace ntt diff --git a/icicle/src/ntt/extern_extension.cu b/icicle/src/ntt/extern_extension.cu deleted file mode 100644 index 5def1ddd1..000000000 --- a/icicle/src/ntt/extern_extension.cu +++ /dev/null @@ -1,23 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "ntt.cu" - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" - -namespace ntt { - /** - * Extern "C" version of [ntt](@ref ntt) function with the following values of template parameters - * (where the field is given by `-DFIELD` env variable during build): - * - `E` is the [field](@ref scalar_t); - * - `S` is the [extension](@ref extension_t) of `E` of appropriate degree; - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_ntt_cuda)( - const extension_t* input, int size, NTTDir dir, NTTConfig& config, extension_t* output) - { - return ntt(input, size, dir, config, output); - } -} // namespace ntt diff --git a/icicle/src/ntt/kernel_ntt.cu b/icicle/src/ntt/kernel_ntt.cu deleted file mode 100644 index 46e97243f..000000000 --- a/icicle/src/ntt/kernel_ntt.cu +++ /dev/null @@ -1,1094 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "thread_ntt.cu" -#include "gpu-utils/sharedmem.cuh" -#include "ntt/ntt.cuh" // for ntt::Ordering - -namespace mxntt { - - static inline __device__ uint32_t dig_rev(uint32_t num, uint32_t log_size, bool dit, bool fast_tw) - { - uint32_t rev_num = 0, temp, dig_len; - if (dit) { - for (int i = 4; i >= 0; i--) { - dig_len = fast_tw ? STAGE_SIZES_DEVICE_FT[log_size][i] : STAGE_SIZES_DEVICE[log_size][i]; - temp = num & ((1 << dig_len) - 1); - num = num >> dig_len; - rev_num = rev_num << dig_len; - rev_num = rev_num | temp; - } - } else { - for (int i = 0; i < 5; i++) { - dig_len = fast_tw ? STAGE_SIZES_DEVICE_FT[log_size][i] : STAGE_SIZES_DEVICE[log_size][i]; - temp = num & ((1 << dig_len) - 1); - num = num >> dig_len; - rev_num = rev_num << dig_len; - rev_num = rev_num | temp; - } - } - return rev_num; - } - - static inline __device__ uint32_t bit_rev(uint32_t num, uint32_t log_size) { return __brev(num) >> (32 - log_size); } - - enum eRevType { None, RevToMixedRev, MixedRevToRev, NaturalToMixedRev, NaturalToRev, MixedRevToNatural }; - - static __device__ uint32_t generalized_rev(uint32_t num, uint32_t log_size, bool dit, bool fast_tw, eRevType rev_type) - { - switch (rev_type) { - case eRevType::RevToMixedRev: - // R -> N -> MR - return dig_rev(bit_rev(num, log_size), log_size, dit, fast_tw); - case eRevType::MixedRevToRev: - // MR -> N -> R - return bit_rev(dig_rev(num, log_size, dit, fast_tw), log_size); - case eRevType::NaturalToMixedRev: - case eRevType::MixedRevToNatural: - return dig_rev(num, log_size, dit, fast_tw); - case eRevType::NaturalToRev: - return bit_rev(num, log_size); - default: - return num; - } - return num; - } - - // Note: the following reorder kernels are fused with normalization for INTT - template - static __global__ void reorder_digits_inplace_and_normalize_kernel( - E* arr, - uint32_t log_size, - bool columns_batch, - uint32_t batch_size, - bool dit, - bool fast_tw, - eRevType rev_type, - bool is_normalize, - S inverse_N) - { - // launch N threads (per batch element) - // each thread starts from one index and calculates the corresponding group - // if its index is the smallest number in the group -> do the memory transformation - // else --> do nothing - - const uint64_t size = 1UL << log_size; - const uint64_t tid = uint64_t(blockDim.x) * blockIdx.x + threadIdx.x; - const uint64_t idx = columns_batch ? tid / batch_size : tid % size; - const uint64_t batch_idx = columns_batch ? tid % batch_size : tid / size; - if (tid >= uint64_t(size) * batch_size) return; - - uint64_t next_element = idx; - uint64_t group[MAX_GROUP_SIZE]; - group[0] = columns_batch ? next_element * batch_size + batch_idx : next_element + size * batch_idx; - - uint32_t i = 1; - for (; i < MAX_GROUP_SIZE;) { - next_element = generalized_rev(next_element, log_size, dit, fast_tw, rev_type); - if (next_element < idx) return; // not handling this group - if (next_element == idx) break; // calculated whole group - group[i++] = columns_batch ? next_element * batch_size + batch_idx : next_element + size * batch_idx; - } - - --i; - // reaching here means I am handling this group - const E last_element_in_group = arr[group[i]]; - for (; i > 0; --i) { - arr[group[i]] = is_normalize ? (arr[group[i - 1]] * inverse_N) : arr[group[i - 1]]; - } - arr[group[0]] = is_normalize ? (last_element_in_group * inverse_N) : last_element_in_group; - } - - template - __launch_bounds__(64) __global__ void reorder_digits_and_normalize_kernel( - const E* arr, - E* arr_reordered, - uint32_t log_size, - bool columns_batch, - uint32_t batch_size, - uint32_t columns_batch_size, - bool dit, - bool fast_tw, - eRevType rev_type, - bool is_normalize, - S inverse_N) - { - const uint64_t size = 1UL << log_size; - const uint64_t tid = uint64_t(blockDim.x) * blockIdx.x + threadIdx.x; - if (tid >= uint64_t(size) * batch_size) return; - - uint64_t rd = tid; - uint64_t wr = (columns_batch ? 0 : ((tid >> log_size) << log_size)) + - generalized_rev((tid / columns_batch_size) & (size - 1), log_size, dit, fast_tw, rev_type); - arr_reordered[wr * columns_batch_size + (tid % columns_batch_size)] = is_normalize ? arr[rd] * inverse_N : arr[rd]; - } - - template - static __global__ void batch_elementwise_mul_with_reorder_kernel( - const E* in_vec, - uint32_t size, - bool columns_batch, - uint32_t batch_size, - uint32_t columns_batch_size, - S* scalar_vec, - int step, - uint32_t n_scalars, - uint32_t log_size, - eRevType rev_type, - bool fast_tw, - E* out_vec) - { - uint64_t tid = uint64_t(blockDim.x) * blockIdx.x + threadIdx.x; - if (tid >= uint64_t(size) * batch_size) return; - int64_t scalar_id = (tid / columns_batch_size) % size; - if (rev_type != eRevType::None) { - // Note: when we multiply an in_vec that is mixed (by DIF (I)NTT), we want to shuffle the - // scalars the same way (then multiply element-wise). This would be a DIT-digit-reverse shuffle. (this is - // confusing but) BUT to avoid shuffling the scalars, we instead want to ask which element in the non-shuffled - // vec is now placed at index tid, which is the opposite of a DIT-digit-reverse --> this is the DIF-digit-reverse. - // Therefore we use the DIF-digit-reverse to know which element moved to index tid and use it to access the - // corresponding element in scalars vec. - const bool dif = rev_type == eRevType::NaturalToMixedRev; - scalar_id = generalized_rev((tid / columns_batch_size) & (size - 1), log_size, !dif, fast_tw, rev_type); - } - out_vec[tid] = *(scalar_vec + ((scalar_id * step) % n_scalars)) * in_vec[tid]; - } - - template - __launch_bounds__(64) __global__ void ntt64( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t columns_batch_size, - uint32_t nof_ntt_blocks, - uint32_t data_stride, - uint32_t log_data_stride, - uint32_t twiddle_stride, - bool strided, - uint32_t stage_num, - bool inv, - bool dit, - bool fast_tw) - { - NTTEngine engine; - stage_metadata s_meta; - SharedMemory smem; - E* shmem = smem.getPointer(); - - s_meta.th_stride = 8; - s_meta.ntt_block_size = 64; - s_meta.ntt_block_id = columns_batch_size ? blockIdx.x / ((columns_batch_size + 7) / 8) - : (blockIdx.x << 3) + (strided ? (threadIdx.x & 0x7) : (threadIdx.x >> 3)); - s_meta.ntt_inp_id = strided ? (threadIdx.x >> 3) : (threadIdx.x & 0x7); - - s_meta.batch_id = - columns_batch_size ? (threadIdx.x & 0x7) + ((blockIdx.x % ((columns_batch_size + 7) / 8)) << 3) : 0; - if (s_meta.ntt_block_id >= nof_ntt_blocks || (columns_batch_size > 0 && s_meta.batch_id >= columns_batch_size)) - return; - - if (fast_tw) - engine.loadBasicTwiddles(basic_twiddles); - else - engine.loadBasicTwiddlesGeneric(basic_twiddles, inv); - if (columns_batch_size) - engine.loadGlobalDataColumnBatch(in, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.loadGlobalData(in, data_stride, log_data_stride, strided, s_meta); - - if (twiddle_stride && dit) { - if (fast_tw) - engine.loadExternalTwiddles64(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric64( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (fast_tw) - engine.loadInternalTwiddles64(internal_twiddles, strided); - else - engine.loadInternalTwiddlesGeneric64(internal_twiddles, strided, inv); - -#pragma unroll 1 - for (uint32_t phase = 0; phase < 2; phase++) { - engine.ntt8win(); - if (phase == 0) { - engine.SharedData64Columns8(shmem, true, false, strided); // store - __syncthreads(); - engine.SharedData64Rows8(shmem, false, false, strided); // load - engine.twiddlesInternal(); - } - } - - if (twiddle_stride && !dit) { - if (fast_tw) - engine.loadExternalTwiddles64(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric64( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (columns_batch_size) - engine.storeGlobalDataColumnBatch(out, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.storeGlobalData(out, data_stride, log_data_stride, strided, s_meta); - } - - template - __launch_bounds__(64) __global__ void ntt32( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t columns_batch_size, - uint32_t nof_ntt_blocks, - uint32_t data_stride, - uint32_t log_data_stride, - uint32_t twiddle_stride, - bool strided, - uint32_t stage_num, - bool inv, - bool dit, - bool fast_tw) - { - NTTEngine engine; - stage_metadata s_meta; - - SharedMemory smem; - E* shmem = smem.getPointer(); - - s_meta.th_stride = 4; - s_meta.ntt_block_size = 32; - s_meta.ntt_block_id = columns_batch_size ? blockIdx.x / ((columns_batch_size + 15) / 16) - : (blockIdx.x << 4) + (strided ? (threadIdx.x & 0xf) : (threadIdx.x >> 2)); - s_meta.ntt_inp_id = strided ? (threadIdx.x >> 4) : (threadIdx.x & 0x3); - - s_meta.batch_id = - columns_batch_size ? (threadIdx.x & 0xf) + ((blockIdx.x % ((columns_batch_size + 15) / 16)) << 4) : 0; - if (s_meta.ntt_block_id >= nof_ntt_blocks || (columns_batch_size > 0 && s_meta.batch_id >= columns_batch_size)) - return; - - if (fast_tw) - engine.loadBasicTwiddles(basic_twiddles); - else - engine.loadBasicTwiddlesGeneric(basic_twiddles, inv); - - if (columns_batch_size) - engine.loadGlobalDataColumnBatch(in, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.loadGlobalData(in, data_stride, log_data_stride, strided, s_meta); - - if (fast_tw) - engine.loadInternalTwiddles32(internal_twiddles, strided); - else - engine.loadInternalTwiddlesGeneric32(internal_twiddles, strided, inv); - engine.ntt8win(); - engine.twiddlesInternal(); - engine.SharedData32Columns8(shmem, true, false, strided); // store - __syncthreads(); - engine.SharedData32Rows4_2(shmem, false, false, strided); // load - engine.ntt4_2(); - if (twiddle_stride) { - if (fast_tw) - engine.loadExternalTwiddles32(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric32( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (columns_batch_size) - engine.storeGlobalData32ColumnBatch(out, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.storeGlobalData32(out, data_stride, log_data_stride, strided, s_meta); - } - - template - __launch_bounds__(64) __global__ void ntt32dit( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t columns_batch_size, - uint32_t nof_ntt_blocks, - uint32_t data_stride, - uint32_t log_data_stride, - uint32_t twiddle_stride, - bool strided, - uint32_t stage_num, - bool inv, - bool dit, - bool fast_tw) - { - NTTEngine engine; - stage_metadata s_meta; - - SharedMemory smem; - E* shmem = smem.getPointer(); - - s_meta.th_stride = 4; - s_meta.ntt_block_size = 32; - s_meta.ntt_block_id = columns_batch_size ? blockIdx.x / ((columns_batch_size + 15) / 16) - : (blockIdx.x << 4) + (strided ? (threadIdx.x & 0xf) : (threadIdx.x >> 2)); - s_meta.ntt_inp_id = strided ? (threadIdx.x >> 4) : (threadIdx.x & 0x3); - - s_meta.batch_id = - columns_batch_size ? (threadIdx.x & 0xf) + ((blockIdx.x % ((columns_batch_size + 15) / 16)) << 4) : 0; - if (s_meta.ntt_block_id >= nof_ntt_blocks || (columns_batch_size > 0 && s_meta.batch_id >= columns_batch_size)) - return; - - if (fast_tw) - engine.loadBasicTwiddles(basic_twiddles); - else - engine.loadBasicTwiddlesGeneric(basic_twiddles, inv); - - if (columns_batch_size) - engine.loadGlobalData32ColumnBatch(in, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.loadGlobalData32(in, data_stride, log_data_stride, strided, s_meta); - if (twiddle_stride) { - if (fast_tw) - engine.loadExternalTwiddles32(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric32( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (fast_tw) - engine.loadInternalTwiddles32(internal_twiddles, strided); - else - engine.loadInternalTwiddlesGeneric32(internal_twiddles, strided, inv); - engine.ntt4_2(); - engine.SharedData32Columns4_2(shmem, true, false, strided); // store - __syncthreads(); - engine.SharedData32Rows8(shmem, false, false, strided); // load - engine.twiddlesInternal(); - engine.ntt8win(); - if (columns_batch_size) - engine.storeGlobalDataColumnBatch(out, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.storeGlobalData(out, data_stride, log_data_stride, strided, s_meta); - } - - template - __launch_bounds__(64) __global__ void ntt16( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t columns_batch_size, - uint32_t nof_ntt_blocks, - uint32_t data_stride, - uint32_t log_data_stride, - uint32_t twiddle_stride, - bool strided, - uint32_t stage_num, - bool inv, - bool dit, - bool fast_tw) - { - NTTEngine engine; - stage_metadata s_meta; - - SharedMemory smem; - E* shmem = smem.getPointer(); - - s_meta.th_stride = 2; - s_meta.ntt_block_size = 16; - s_meta.ntt_block_id = columns_batch_size - ? blockIdx.x / ((columns_batch_size + 31) / 32) - : (blockIdx.x << 5) + (strided ? (threadIdx.x & 0x1f) : (threadIdx.x >> 1)); - s_meta.ntt_inp_id = strided ? (threadIdx.x >> 5) : (threadIdx.x & 0x1); - - s_meta.batch_id = - columns_batch_size ? (threadIdx.x & 0x1f) + ((blockIdx.x % ((columns_batch_size + 31) / 32)) << 5) : 0; - if (s_meta.ntt_block_id >= nof_ntt_blocks || (columns_batch_size > 0 && s_meta.batch_id >= columns_batch_size)) - return; - - if (fast_tw) - engine.loadBasicTwiddles(basic_twiddles); - else - engine.loadBasicTwiddlesGeneric(basic_twiddles, inv); - - if (columns_batch_size) - engine.loadGlobalDataColumnBatch(in, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.loadGlobalData(in, data_stride, log_data_stride, strided, s_meta); - - if (fast_tw) - engine.loadInternalTwiddles16(internal_twiddles, strided); - else - engine.loadInternalTwiddlesGeneric16(internal_twiddles, strided, inv); - engine.ntt8win(); - engine.twiddlesInternal(); - engine.SharedData16Columns8(shmem, true, false, strided); // store - __syncthreads(); - engine.SharedData16Rows2_4(shmem, false, false, strided); // load - engine.ntt2_4(); - if (twiddle_stride) { - if (fast_tw) - engine.loadExternalTwiddles16(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric16( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (columns_batch_size) - engine.storeGlobalData16ColumnBatch(out, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.storeGlobalData16(out, data_stride, log_data_stride, strided, s_meta); - } - - template - __launch_bounds__(64) __global__ void ntt16dit( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t columns_batch_size, - uint32_t nof_ntt_blocks, - uint32_t data_stride, - uint32_t log_data_stride, - uint32_t twiddle_stride, - bool strided, - uint32_t stage_num, - bool inv, - bool dit, - bool fast_tw) - { - NTTEngine engine; - stage_metadata s_meta; - - SharedMemory smem; - E* shmem = smem.getPointer(); - - s_meta.th_stride = 2; - s_meta.ntt_block_size = 16; - s_meta.ntt_block_id = columns_batch_size - ? blockIdx.x / ((columns_batch_size + 31) / 32) - : (blockIdx.x << 5) + (strided ? (threadIdx.x & 0x1f) : (threadIdx.x >> 1)); - s_meta.ntt_inp_id = strided ? (threadIdx.x >> 5) : (threadIdx.x & 0x1); - - s_meta.batch_id = - columns_batch_size ? (threadIdx.x & 0x1f) + ((blockIdx.x % ((columns_batch_size + 31) / 32)) << 5) : 0; - if (s_meta.ntt_block_id >= nof_ntt_blocks || (columns_batch_size > 0 && s_meta.batch_id >= columns_batch_size)) - return; - - if (fast_tw) - engine.loadBasicTwiddles(basic_twiddles); - else - engine.loadBasicTwiddlesGeneric(basic_twiddles, inv); - - if (columns_batch_size) - engine.loadGlobalData16ColumnBatch(in, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.loadGlobalData16(in, data_stride, log_data_stride, strided, s_meta); - - if (twiddle_stride) { - if (fast_tw) - engine.loadExternalTwiddles16(external_twiddles, twiddle_stride, log_data_stride, s_meta); - else - engine.loadExternalTwiddlesGeneric16( - external_twiddles, twiddle_stride, log_data_stride, s_meta, tw_log_size, inv); - engine.twiddlesExternal(); - } - if (fast_tw) - engine.loadInternalTwiddles16(internal_twiddles, strided); - else - engine.loadInternalTwiddlesGeneric16(internal_twiddles, strided, inv); - engine.ntt2_4(); - engine.SharedData16Columns2_4(shmem, true, false, strided); // store - __syncthreads(); - engine.SharedData16Rows8(shmem, false, false, strided); // load - engine.twiddlesInternal(); - engine.ntt8win(); - if (columns_batch_size) - engine.storeGlobalDataColumnBatch(out, data_stride, log_data_stride, s_meta, columns_batch_size); - else - engine.storeGlobalData(out, data_stride, log_data_stride, strided, s_meta); - } - - template - __global__ void normalize_kernel(E* data, S norm_factor, uint64_t size) - { - uint64_t tid = uint64_t(blockIdx.x) * blockDim.x + threadIdx.x; - if (tid >= size) return; - data[tid] = data[tid] * norm_factor; - } - - template - __global__ void generate_base_table(S basic_root, S* base_table, uint32_t skip) - { - S w = basic_root; - S t = S::one(); - for (int i = 0; i < 64; i += skip) { - base_table[i] = t; - t = t * w; - } - } - - // Generic twiddles: 1N twiddles for forward and inverse NTT - template - __global__ void generate_basic_twiddles_generic(S basic_root, S* w6_table, S* basic_twiddles) - { - S w0 = basic_root * basic_root; - S w1 = (basic_root + w0 * basic_root) * S::inv_log_size(1); - S w2 = (basic_root - w0 * basic_root) * S::inv_log_size(1); - basic_twiddles[0] = w0; - basic_twiddles[1] = w1; - basic_twiddles[2] = w2; - S basic_inv = w6_table[64 - 8]; - w0 = basic_inv * basic_inv; - w1 = (basic_inv + w0 * basic_inv) * S::inv_log_size(1); - w2 = (basic_inv - w0 * basic_inv) * S::inv_log_size(1); - basic_twiddles[3] = w0; - basic_twiddles[4] = w1; - basic_twiddles[5] = w2; - } - - template - __global__ void generate_twiddle_combinations_generic( - S* w6_table, S* w12_table, S* w18_table, S* w24_table, S* w30_table, S* external_twiddles, uint32_t log_size) - { - uint32_t tid = blockIdx.x * blockDim.x + threadIdx.x; - uint32_t exp = tid << (30 - log_size); - S w6, w12, w18, w24, w30; - w6 = w6_table[exp >> 24]; - w12 = w12_table[((exp >> 18) & 0x3f)]; - w18 = w18_table[((exp >> 12) & 0x3f)]; - w24 = w24_table[((exp >> 6) & 0x3f)]; - w30 = w30_table[(exp & 0x3f)]; - S t = w6 * w12 * w18 * w24 * w30; - external_twiddles[tid] = t; - } - - template - __global__ void set_value(S* arr, int idx, S val) - { - arr[idx] = val; - } - - template - cudaError_t generate_external_twiddles_generic( - const S& basic_root, - S* external_twiddles, - S*& internal_twiddles, - S*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream) - { - CHK_INIT_IF_RETURN(); - - const int n = pow(2, log_size); - CHK_IF_RETURN(cudaMallocAsync(&basic_twiddles, 6 * sizeof(S), stream)); - - S* w6_table; - S* w12_table; - S* w18_table; - S* w24_table; - S* w30_table; - CHK_IF_RETURN(cudaMallocAsync(&w6_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w12_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w18_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w24_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w30_table, sizeof(S) * 64, stream)); - - // Note: for compatibility with radix-2 INTT, need ONE in last element (in addition to first element) - set_value<<<1, 1, 0, stream>>>(external_twiddles, n /*last element idx*/, S::one()); - - cudaStreamSynchronize(stream); - - S temp_root = basic_root; - generate_base_table<<<1, 1, 0, stream>>>(basic_root, w30_table, 1 << (30 - log_size)); - - if (log_size > 24) - for (int i = 0; i < 6 - (30 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w24_table, 1 << (log_size > 24 ? 0 : 24 - log_size)); - - if (log_size > 18) - for (int i = 0; i < 6 - (log_size > 24 ? 0 : 24 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w18_table, 1 << (log_size > 18 ? 0 : 18 - log_size)); - - if (log_size > 12) - for (int i = 0; i < 6 - (log_size > 18 ? 0 : 18 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w12_table, 1 << (log_size > 12 ? 0 : 12 - log_size)); - - if (log_size > 6) - for (int i = 0; i < 6 - (log_size > 12 ? 0 : 12 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w6_table, 1 << (log_size > 6 ? 0 : 6 - log_size)); - - if (log_size > 2) - for (int i = 0; i < 3 - (log_size > 6 ? 0 : 6 - log_size); i++) - temp_root = temp_root * temp_root; - generate_basic_twiddles_generic<<<1, 1, 0, stream>>>(temp_root, w6_table, basic_twiddles); - - const int NOF_BLOCKS = (log_size >= 8) ? (1 << (log_size - 8)) : 1; - const int NOF_THREADS = (log_size >= 8) ? 256 : (1 << log_size); - generate_twiddle_combinations_generic<<>>( - w6_table, w12_table, w18_table, w24_table, w30_table, external_twiddles, log_size); - - internal_twiddles = w6_table; - - CHK_IF_RETURN(cudaFreeAsync(w12_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w18_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w24_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w30_table, stream)); - - return CHK_LAST(); - } - - // Fast-twiddles: 2N twiddles for forward, 2N for inverse - template - __global__ void generate_basic_twiddles_fast_twiddles_mode(S basic_root, S* basic_twiddles) - { - S w0 = basic_root * basic_root; - S w1 = (basic_root + w0 * basic_root) * S::inv_log_size(1); - S w2 = (basic_root - w0 * basic_root) * S::inv_log_size(1); - basic_twiddles[0] = w0; - basic_twiddles[1] = w1; - basic_twiddles[2] = w2; - } - - template - __global__ void generate_twiddle_combinations_fast_twiddles_mode( - S* w6_table, - S* w12_table, - S* w18_table, - S* w24_table, - S* w30_table, - S* external_twiddles, - uint32_t log_size, - uint32_t prev_log_size) - { - uint32_t tid = blockIdx.x * blockDim.x + threadIdx.x; - uint32_t exp = ((tid & ((1 << prev_log_size) - 1)) * (tid >> prev_log_size)) << (30 - log_size); - S w6, w12, w18, w24, w30; - w6 = w6_table[exp >> 24]; - w12 = w12_table[((exp >> 18) & 0x3f)]; - w18 = w18_table[((exp >> 12) & 0x3f)]; - w24 = w24_table[((exp >> 6) & 0x3f)]; - w30 = w30_table[(exp & 0x3f)]; - S t = w6 * w12 * w18 * w24 * w30; - external_twiddles[tid + (1 << log_size) - 1] = t; - } - - template - cudaError_t generate_external_twiddles_fast_twiddles_mode( - const S& basic_root, - S* external_twiddles, - S*& internal_twiddles, - S*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream) - { - CHK_INIT_IF_RETURN(); - - S* w6_table; - S* w12_table; - S* w18_table; - S* w24_table; - S* w30_table; - CHK_IF_RETURN(cudaMallocAsync(&w6_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w12_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w18_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w24_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&w30_table, sizeof(S) * 64, stream)); - CHK_IF_RETURN(cudaMallocAsync(&basic_twiddles, 3 * sizeof(S), stream)); - - S temp_root = basic_root; - generate_base_table<<<1, 1, 0, stream>>>(basic_root, w30_table, 1 << (30 - log_size)); - if (log_size > 24) - for (int i = 0; i < 6 - (30 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w24_table, 1 << (log_size > 24 ? 0 : 24 - log_size)); - if (log_size > 18) - for (int i = 0; i < 6 - (log_size > 24 ? 0 : 24 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w18_table, 1 << (log_size > 18 ? 0 : 18 - log_size)); - if (log_size > 12) - for (int i = 0; i < 6 - (log_size > 18 ? 0 : 18 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w12_table, 1 << (log_size > 12 ? 0 : 12 - log_size)); - if (log_size > 6) - for (int i = 0; i < 6 - (log_size > 12 ? 0 : 12 - log_size); i++) - temp_root = temp_root * temp_root; - generate_base_table<<<1, 1, 0, stream>>>(temp_root, w6_table, 1 << (log_size > 6 ? 0 : 6 - log_size)); - for (int i = 0; i < 3 - (log_size > 6 ? 0 : 6 - log_size); i++) - temp_root = temp_root * temp_root; - generate_basic_twiddles_fast_twiddles_mode<<<1, 1, 0, stream>>>(temp_root, basic_twiddles); - - for (int i = 8; i < log_size + 1; i++) { - generate_twiddle_combinations_fast_twiddles_mode<<<1 << (i - 8), 256, 0, stream>>>( - w6_table, w12_table, w18_table, w24_table, w30_table, external_twiddles, i, STAGE_PREV_SIZES[i]); - } - internal_twiddles = w6_table; - - CHK_IF_RETURN(cudaFreeAsync(w12_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w18_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w24_table, stream)); - CHK_IF_RETURN(cudaFreeAsync(w30_table, stream)); - - return CHK_LAST(); - } - - template - cudaError_t large_ntt( - const E* in, - E* out, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - uint32_t log_size, - uint32_t tw_log_size, - uint32_t batch_size, - bool columns_batch, - bool inv, - bool normalize, - bool dit, - bool fast_tw, - cudaStream_t cuda_stream) - { - CHK_INIT_IF_RETURN(); - - if (log_size == 1 || log_size == 2 || log_size == 3 || log_size == 7) { - throw IcicleError(IcicleError_t::InvalidArgument, "size not implemented for mixed-radix-NTT"); - } - - if (log_size == 4) { - const int NOF_THREADS = columns_batch ? 64 : min(64, 2 * batch_size); - const int NOF_BLOCKS = - columns_batch ? ((batch_size + 31) / 32) : (2 * batch_size + NOF_THREADS - 1) / NOF_THREADS; - if (dit) { - ntt16dit<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, columns_batch ? 1 : batch_size, 1, 0, 0, columns_batch, 0, inv, dit, fast_tw); - } else { // dif - ntt16<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, columns_batch ? 1 : batch_size, 1, 0, 0, columns_batch, 0, inv, dit, fast_tw); - } - if (normalize) - normalize_kernel<<>>(out, S::inv_log_size(4), (1UL << log_size) * batch_size); - return CHK_LAST(); - } - - if (log_size == 5) { - const int NOF_THREADS = columns_batch ? 64 : min(64, 4 * batch_size); - const int NOF_BLOCKS = - columns_batch ? ((batch_size + 15) / 16) : (4 * batch_size + NOF_THREADS - 1) / NOF_THREADS; - if (dit) { - ntt32dit<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, columns_batch ? 1 : batch_size, 1, 0, 0, columns_batch, 0, inv, dit, fast_tw); - } else { // dif - ntt32<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, columns_batch ? 1 : batch_size, 1, 0, 0, columns_batch, 0, inv, dit, fast_tw); - } - if (normalize) - normalize_kernel<<>>(out, S::inv_log_size(5), (1UL << log_size) * batch_size); - return CHK_LAST(); - } - - if (log_size == 6) { - const int NOF_THREADS = columns_batch ? 64 : min(64, 8 * batch_size); - const int NOF_BLOCKS = - columns_batch ? ((batch_size + 7) / 8) : ((8 * batch_size + NOF_THREADS - 1) / NOF_THREADS); - ntt64<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, columns_batch ? 1 : batch_size, 1, 0, 0, columns_batch, 0, inv, dit, fast_tw); - if (normalize) - normalize_kernel<<>>(out, S::inv_log_size(6), (1UL << log_size) * batch_size); - return CHK_LAST(); - } - - if (log_size == 8) { - const int NOF_THREADS = 64; - const int NOF_BLOCKS = - columns_batch ? ((batch_size + 31) / 32 * 16) : ((32 * batch_size + NOF_THREADS - 1) / NOF_THREADS); - if (dit) { - ntt16dit<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 1, 0, 0, - columns_batch, 0, inv, dit, fast_tw); - ntt16dit<<>>( - out, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 16, 4, 16, true, 1, - inv, dit, fast_tw); - } else { // dif - ntt16<<>>( - in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 16, 4, 16, true, 1, - inv, dit, fast_tw); - ntt16<<>>( - out, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 1, 0, 0, - columns_batch, 0, inv, dit, fast_tw); - } - if (normalize) - normalize_kernel<<>>(out, S::inv_log_size(8), (1UL << log_size) * batch_size); - return CHK_LAST(); - } - - // general case: - uint32_t nof_blocks = (1UL << (log_size - 9)) * (columns_batch ? ((batch_size + 31) / 32) * 32 : batch_size); - if (dit) { - for (int i = 0; i < 5; i++) { - uint32_t stage_size = fast_tw ? STAGE_SIZES_HOST_FT[log_size][i] : STAGE_SIZES_HOST[log_size][i]; - uint32_t stride_log = 0; - for (int j = 0; j < i; j++) - stride_log += fast_tw ? STAGE_SIZES_HOST_FT[log_size][j] : STAGE_SIZES_HOST[log_size][j]; - if (stage_size == 6) - ntt64<<>>( - i ? out : in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 6) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - else if (stage_size == 5) - ntt32dit<<>>( - i ? out : in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 5) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - else if (stage_size == 4) - ntt16dit<<>>( - i ? out : in, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - } - } else { // dif - bool first_run = false, prev_stage = false; - for (int i = 4; i >= 0; i--) { - uint32_t stage_size = fast_tw ? STAGE_SIZES_HOST_FT[log_size][i] : STAGE_SIZES_HOST[log_size][i]; - uint32_t stride_log = 0; - for (int j = 0; j < i; j++) - stride_log += fast_tw ? STAGE_SIZES_HOST_FT[log_size][j] : STAGE_SIZES_HOST[log_size][j]; - first_run = stage_size && !prev_stage; - if (stage_size == 6) - ntt64<<>>( - first_run ? in : out, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 6) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - else if (stage_size == 5) - ntt32<<>>( - first_run ? in : out, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 5) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - else if (stage_size == 4) - ntt16<<>>( - first_run ? in : out, out, external_twiddles, internal_twiddles, basic_twiddles, log_size, tw_log_size, - columns_batch ? batch_size : 0, (1 << log_size - 4) * (columns_batch ? 1 : batch_size), 1 << stride_log, - stride_log, i ? (1 << stride_log) : 0, i || columns_batch, i, inv, dit, fast_tw); - prev_stage = stage_size; - } - } - if (normalize) - normalize_kernel<<<(1 << (log_size - 8)) * batch_size, 256, 0, cuda_stream>>>( - out, S::inv_log_size(log_size), (1UL << log_size) * batch_size); - - return CHK_LAST(); - } - - template - cudaError_t mixed_radix_ntt( - const E* d_input, - E* d_output, - S* external_twiddles, - S* internal_twiddles, - S* basic_twiddles, - S* linear_twiddle, // twiddles organized as [1,w,w^2,...] for coset-eval in fast-tw mode - int ntt_size, - int max_logn, - int batch_size, - bool columns_batch, - bool is_inverse, - bool fast_tw, - ntt::Ordering ordering, - S* arbitrary_coset, - int coset_gen_index, - cudaStream_t cuda_stream) - { - CHK_INIT_IF_RETURN(); - - const uint64_t total_nof_elements = uint64_t(ntt_size) * batch_size; - const uint64_t logn = uint64_t(log2(ntt_size)); - const uint64_t NOF_BLOCKS_64b = (total_nof_elements + 64 - 1) / 64; - const uint32_t NOF_THREADS = total_nof_elements < 64 ? total_nof_elements : 64; - // CUDA grid is 32b fields. Assert that I don't need a larger grid. - const uint32_t NOF_BLOCKS = NOF_BLOCKS_64b; - if (NOF_BLOCKS != NOF_BLOCKS_64b) { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "NTT dimensions (ntt_size, batch) are too large. Unsupported!"); - } - - bool is_normalize = is_inverse; - const bool is_on_coset = (coset_gen_index != 0) || arbitrary_coset; - const uint32_t n_twiddles = 1U << max_logn; - // Note: for evaluation on coset, need to reorder the coset too to match the data for element-wise multiplication - eRevType reverse_input = None, reverse_output = None, reverse_coset = None; - bool dit = false; - switch (ordering) { - case ntt::Ordering::kNN: - reverse_input = eRevType::NaturalToMixedRev; - dit = true; - break; - case ntt::Ordering::kRN: - reverse_input = eRevType::RevToMixedRev; - dit = true; - reverse_coset = is_inverse ? eRevType::None : eRevType::NaturalToRev; - break; - case ntt::Ordering::kNR: - reverse_output = eRevType::MixedRevToRev; - reverse_coset = is_inverse ? eRevType::NaturalToRev : eRevType::None; - break; - case ntt::Ordering::kRR: - reverse_input = eRevType::RevToMixedRev; - dit = true; - reverse_output = eRevType::NaturalToRev; - reverse_coset = eRevType::NaturalToRev; - break; - case ntt::Ordering::kMN: - dit = true; - reverse_coset = is_inverse ? None : eRevType::NaturalToMixedRev; - break; - case ntt::Ordering::kNM: - reverse_coset = is_inverse ? eRevType::NaturalToMixedRev : eRevType::None; - break; - } - - if (is_on_coset && !is_inverse) { - batch_elementwise_mul_with_reorder_kernel<<>>( - d_input, ntt_size, columns_batch, batch_size, columns_batch ? batch_size : 1, - arbitrary_coset ? arbitrary_coset : linear_twiddle, arbitrary_coset ? 1 : coset_gen_index, n_twiddles, logn, - reverse_coset, fast_tw, d_output); - - d_input = d_output; - } - - if (reverse_input != eRevType::None) { - const bool is_reverse_in_place = (d_input == d_output); - if (is_reverse_in_place) { - reorder_digits_inplace_and_normalize_kernel<<>>( - d_output, logn, columns_batch, batch_size, dit, fast_tw, reverse_input, is_normalize, S::inv_log_size(logn)); - } else { - reorder_digits_and_normalize_kernel<<>>( - d_input, d_output, logn, columns_batch, batch_size, columns_batch ? batch_size : 1, dit, fast_tw, - reverse_input, is_normalize, S::inv_log_size(logn)); - } - is_normalize = false; - d_input = d_output; - } - - // inplace ntt - CHK_IF_RETURN(large_ntt( - d_input, d_output, external_twiddles, internal_twiddles, basic_twiddles, logn, max_logn, batch_size, - columns_batch, is_inverse, (is_normalize && reverse_output == eRevType::None), dit, fast_tw, cuda_stream)); - - if (reverse_output != eRevType::None) { - reorder_digits_inplace_and_normalize_kernel<<>>( - d_output, logn, columns_batch, batch_size, dit, fast_tw, reverse_output, is_normalize, S::inv_log_size(logn)); - } - - if (is_on_coset && is_inverse) { - batch_elementwise_mul_with_reorder_kernel<<>>( - d_output, ntt_size, columns_batch, batch_size, columns_batch ? batch_size : 1, - arbitrary_coset ? arbitrary_coset : linear_twiddle + n_twiddles, arbitrary_coset ? 1 : -coset_gen_index, - n_twiddles, logn, reverse_coset, fast_tw, d_output); - } - - return CHK_LAST(); - } - - // Explicit instantiation for scalar type - template cudaError_t generate_external_twiddles_generic( - const scalar_t& basic_root, - scalar_t* external_twiddles, - scalar_t*& internal_twiddles, - scalar_t*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream); - - template cudaError_t generate_external_twiddles_fast_twiddles_mode( - const scalar_t& basic_root, - scalar_t* external_twiddles, - scalar_t*& internal_twiddles, - scalar_t*& basic_twiddles, - uint32_t log_size, - cudaStream_t& stream); - - template cudaError_t mixed_radix_ntt( - const scalar_t* d_input, - scalar_t* d_output, - scalar_t* external_twiddles, - scalar_t* internal_twiddles, - scalar_t* basic_twiddles, - scalar_t* linear_twiddles, - - int ntt_size, - int max_logn, - int batch_size, - bool columns_batch, - bool is_inverse, - bool fast_tw, - ntt::Ordering ordering, - scalar_t* arbitrary_coset, - int coset_gen_index, - cudaStream_t cuda_stream); - -#if defined(EXT_FIELD) - template cudaError_t mixed_radix_ntt( - const extension_t* d_input, - extension_t* d_output, - scalar_t* external_twiddles, - scalar_t* internal_twiddles, - scalar_t* basic_twiddles, - scalar_t* linear_twiddles, - - int ntt_size, - int max_logn, - int batch_size, - bool columns_batch, - bool is_inverse, - bool fast_tw, - ntt::Ordering ordering, - scalar_t* arbitrary_coset, - int coset_gen_index, - cudaStream_t cuda_stream); -#endif - - // TODO: we may reintroduce mixed-radix ECNTT based on upcoming benching PR - // #if defined(ECNTT) - // template cudaError_t mixed_radix_ntt( - // projective_t* d_input, - // projective_t* d_output, - // scalar_t* external_twiddles, - // scalar_t* internal_twiddles, - // scalar_t* basic_twiddles, - // int ntt_size, - // int max_logn, - // int batch_size, - // bool columns_batch, - // bool is_inverse, - // bool fast_tw, - // ntt::Ordering ordering, - // scalar_t* arbitrary_coset, - // int coset_gen_index, - // cudaStream_t cuda_stream); - // #endif // ECNTT -} // namespace mxntt diff --git a/icicle/src/ntt/ntt.cu b/icicle/src/ntt/ntt.cu deleted file mode 100644 index 0afe4a296..000000000 --- a/icicle/src/ntt/ntt.cu +++ /dev/null @@ -1,768 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "ntt/ntt.cuh" - -#include -#include -#include - -#include "gpu-utils/sharedmem.cuh" -#include "utils/utils_kernels.cuh" -#include "utils/utils.h" -#include "ntt/ntt_impl.cuh" - -#include - -#ifdef CURVE_ID -#include "curves/curve_config.cuh" -using namespace curve_config; -#define IS_ECNTT std::is_same_v -#else -#define IS_ECNTT false -#endif - -namespace ntt { - - namespace { - // TODO: Set MAX THREADS based on GPU arch - const uint32_t MAX_NUM_THREADS = 512; // TODO: hotfix - should be 1024, currently limits shared memory size - const uint32_t MAX_THREADS_BATCH = 512; - const uint32_t MAX_THREADS_BATCH_ECNTT = - 128; // TODO: hardcoded - allows (2^18 x 64) ECNTT for sm86, decrease this to allow larger ecntt length, batch - // size limited by on-device memory - const uint32_t MAX_SHARED_MEM_ELEMENT_SIZE = 32; // TODO: occupancy calculator, hardcoded for sm_86..sm_89 - const uint32_t MAX_SHARED_MEM = MAX_SHARED_MEM_ELEMENT_SIZE * MAX_NUM_THREADS; - - template - __global__ void reverse_order_kernel(const E* arr, E* arr_reversed, uint32_t n, uint32_t logn, uint32_t batch_size) - { - int threadId = (blockIdx.x * blockDim.x) + threadIdx.x; - if (threadId < n * batch_size) { - int idx = threadId % n; - int batch_idx = threadId / n; - int idx_reversed = __brev(idx) >> (32 - logn); - - if (arr == arr_reversed) { // for in-place (when pointers arr==arr_reversed) - if (idx < idx_reversed) { - E val = arr[batch_idx * n + idx]; - arr_reversed[batch_idx * n + idx] = arr[batch_idx * n + idx_reversed]; - arr_reversed[batch_idx * n + idx_reversed] = val; - } - } else - arr_reversed[batch_idx * n + idx_reversed] = arr[batch_idx * n + idx]; - } - } - - /** - * Bit-reverses a batch of input arrays out-of-place inside GPU. - * for example: on input array ([a[0],a[1],a[2],a[3]], 4, 2) it returns - * [a[0],a[3],a[2],a[1]] (elements at indices 3 and 1 swhich places). - * @param arr_in batch of arrays of some object of type T. Should be on GPU. - * @param n length of `arr`. - * @param logn log(n). - * @param batch_size the size of the batch. - * @param arr_out buffer of the same size as `arr_in` on the GPU to write the bit-permuted array into. - */ - template - void reverse_order_batch( - const E* arr_in, uint32_t n, uint32_t logn, uint32_t batch_size, cudaStream_t stream, E* arr_out) - { - int number_of_threads = MAX_THREADS_BATCH; - int number_of_blocks = (n * batch_size + number_of_threads - 1) / number_of_threads; - reverse_order_kernel<<>>(arr_in, arr_out, n, logn, batch_size); - } - - /** - * Bit-reverses an input array out-of-place inside GPU. - * for example: on array ([a[0],a[1],a[2],a[3]], 4, 2) it returns - * [a[0],a[3],a[2],a[1]] (elements at indices 3 and 1 swhich places). - * @param arr_in array of some object of type T of size which is a power of 2. Should be on GPU. - * @param n length of `arr`. - * @param logn log(n). - * @param arr_out buffer of the same size as `arr_in` on the GPU to write the bit-permuted array into. - */ - template - void reverse_order(const E* arr_in, uint32_t n, uint32_t logn, cudaStream_t stream, E* arr_out) - { - reverse_order_batch(arr_in, n, logn, 1, stream, arr_out); - } - - /** - * Cooley-Tuckey NTT. - * NOTE! this function assumes that d_twiddles are located in the device memory. - * @param arr_in input array of type E (elements). - * @param n length of d_arr. - * @param twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2). - * @param n_twiddles length of twiddles, should be negative for intt. - * @param max_task max count of parallel tasks. - * @param s log2(n) loop index. - * @param arr_out buffer for the output. - */ - template - __global__ void ntt_template_kernel_shared_rev( - const E* __restrict__ arr_in, - int n, - const S* __restrict__ r_twiddles, - int n_twiddles, - int max_task, - int ss, - int logn, - E* __restrict__ arr_out) - { - SharedMemory smem; - E* arr = smem.getPointer(); - - uint32_t task = blockIdx.x; - uint32_t loop_limit = blockDim.x; - uint32_t chunks = n / (loop_limit * 2); - uint32_t offset = (task / chunks) * n; - if (task < max_task) { - // flattened loop allows parallel processing - uint32_t l = threadIdx.x; - - if (l < loop_limit) { - UNROLL - for (; ss < logn; ss++) { - int s = logn - ss - 1; - bool is_beginning = ss == 0; - bool is_end = ss == (logn - 1); - - uint32_t ntw_i = task % chunks; - - uint32_t n_twiddles_div = n_twiddles >> (s + 1); - - uint32_t shift_s = 1 << s; - uint32_t shift2_s = 1 << (s + 1); - - l = ntw_i * loop_limit + l; // to l from chunks to full - - uint32_t j = l & (shift_s - 1); // Equivalent to: l % (1 << s) - uint32_t i = ((l >> s) * shift2_s) & (n - 1); // (..) % n (assuming n is power of 2) - uint32_t oij = i + j; - uint32_t k = oij + shift_s; - - S tw = *(r_twiddles + (int)(j * n_twiddles_div)); - - E u = is_beginning ? arr_in[offset + oij] : arr[oij]; - E v = is_beginning ? arr_in[offset + k] : arr[k]; - if (is_end) { - arr_out[offset + oij] = u + v; - arr_out[offset + k] = tw * (u - v); - } else { - arr[oij] = u + v; - arr[k] = tw * (u - v); - } - - __syncthreads(); - } - } - } - } - - /** - * Cooley-Tuckey NTT. - * NOTE! this function assumes that d_twiddles are located in the device memory. - * @param arr_in input array of type E (elements). - * @param n length of d_arr. - * @param twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2). - * @param n_twiddles length of twiddles, should be negative for intt. - * @param max_task max count of parallel tasks. - * @param s log2(n) loop index. - * @param arr_out buffer for the output. - */ - template - __global__ void ntt_template_kernel_shared( - const E* __restrict__ arr_in, - int n, - const S* __restrict__ r_twiddles, - int n_twiddles, - int max_task, - int s, - int logn, - E* __restrict__ arr_out) - { - SharedMemory smem; - E* arr = smem.getPointer(); - - uint32_t task = blockIdx.x; - uint32_t loop_limit = blockDim.x; - uint32_t chunks = n / (loop_limit * 2); - uint32_t offset = (task / chunks) * n; - if (task < max_task) { - // flattened loop allows parallel processing - uint32_t l = threadIdx.x; - - if (l < loop_limit) { - UNROLL - for (; s < logn; s++) // TODO: this loop also can be unrolled - { - uint32_t ntw_i = task % chunks; - - uint32_t n_twiddles_div = n_twiddles >> (s + 1); - - uint32_t shift_s = 1 << s; - uint32_t shift2_s = 1 << (s + 1); - - l = ntw_i * loop_limit + l; // to l from chunks to full - - uint32_t j = l & (shift_s - 1); // Equivalent to: l % (1 << s) - uint32_t i = ((l >> s) * shift2_s) & (n - 1); // (..) % n (assuming n is power of 2) - uint32_t oij = i + j; - uint32_t k = oij + shift_s; - S tw = *(r_twiddles + (int)(j * n_twiddles_div)); - - E u = s == 0 ? arr_in[offset + oij] : arr[oij]; - E v = s == 0 ? arr_in[offset + k] : arr[k]; - v = tw * v; - if (s == (logn - 1)) { - arr_out[offset + oij] = u + v; - arr_out[offset + k] = u - v; - } else { - arr[oij] = u + v; - arr[k] = u - v; - } - - __syncthreads(); - } - } - } - } - - /** - * Cooley-Tukey NTT. - * NOTE! this function assumes that d_twiddles are located in the device memory. - * @param arr input array of type E (elements). - * @param n length of d_arr. - * @param twiddles twiddle factors of type S (scalars) array allocated on the device memory (must be a power of 2). - * @param n_twiddles length of twiddles, should be negative for intt. - * @param max_task max count of parallel tasks. - * @param s log2(n) loop index. - */ - template - __global__ void - ntt_template_kernel(const E* arr_in, int n, S* twiddles, int n_twiddles, int max_task, int s, bool rev, E* arr_out) - { - int task = blockIdx.x; - int chunks = n / (blockDim.x * 2); - - if (task < max_task) { - // flattened loop allows parallel processing - uint32_t l = threadIdx.x; - uint32_t loop_limit = blockDim.x; - - if (l < loop_limit) { - uint32_t ntw_i = task % chunks; - - uint32_t shift_s = 1 << s; - uint32_t shift2_s = 1 << (s + 1); - uint32_t n_twiddles_div = n_twiddles >> (s + 1); - - l = ntw_i * blockDim.x + l; // to l from chunks to full - - uint32_t j = l & (shift_s - 1); // Equivalent to: l % (1 << s) - uint32_t i = ((l >> s) * shift2_s) & (n - 1); // (..) % n (assuming n is power of 2) - uint32_t k = i + j + shift_s; - - S tw = *(twiddles + (int)(j * n_twiddles_div)); - - uint32_t offset = (task / chunks) * n; - E u = arr_in[offset + i + j]; - E v = arr_in[offset + k]; - if (!rev) v = tw * v; - arr_out[offset + i + j] = u + v; - v = u - v; - arr_out[offset + k] = rev ? tw * v : v; - } - } - } - - /** - * NTT/INTT inplace batch - * Note: this function does not perform any bit-reverse permutations on its inputs or outputs. - * @param d_input Input array - * @param n Size of `d_input` - * @param d_twiddles Twiddles - * @param n_twiddles Size of `d_twiddles` - * @param batch_size The size of the batch; the length of `d_inout` is `n` * `batch_size`. - * @param inverse true for iNTT - * @param coset should be array of length n or a nullptr if NTT is not computed on a coset - * @param stream CUDA stream - * @param is_async if false, perform sync of the supplied CUDA stream at the end of processing - * @param d_output Output array - */ - template - cudaError_t ntt_inplace_batch_template( - const E* d_input, - int n, - S* d_twiddles, - int n_twiddles, - int batch_size, - int logn, - bool inverse, - bool dit, - S* arbitrary_coset, - int coset_gen_index, - cudaStream_t stream, - E* d_output) - { - CHK_INIT_IF_RETURN(); - - bool is_shared_mem_enabled = sizeof(E) <= MAX_SHARED_MEM_ELEMENT_SIZE; - const int log2_shmem_elems = is_shared_mem_enabled ? int(log(int(MAX_SHARED_MEM / sizeof(E))) / log(2)) : logn; - int max_threads_batch = IS_ECNTT ? MAX_THREADS_BATCH_ECNTT : MAX_THREADS_BATCH; - int num_threads = max(min(min(n / 2, max_threads_batch), 1 << (log2_shmem_elems - 1)), 1); - const int chunks = max(int((n / 2) / num_threads), 1); - const int total_tasks = batch_size * chunks; - int num_blocks = total_tasks; - const int shared_mem = 2 * num_threads * sizeof(E); // TODO: calculator, as shared mem size may be more efficient - // less then max to allow more concurrent blocks on SM - const int logn_shmem = is_shared_mem_enabled ? int(log(2 * num_threads) / log(2)) - : 0; // TODO: shared memory support only for types <= 32 bytes - // Note: for ecntt we limit block size (=#threads per block) since otherwise it doesn't fit the SM resources. - int num_threads_coset = max(min(n / 2, IS_ECNTT ? MAX_THREADS_BATCH_ECNTT : MAX_NUM_THREADS), 1); - int num_blocks_coset = (n * batch_size + num_threads_coset - 1) / num_threads_coset; - - if (inverse) { - d_twiddles = d_twiddles + n_twiddles; - n_twiddles = -n_twiddles; - } - - bool is_on_coset = (coset_gen_index != 0) || arbitrary_coset; - bool direct_coset = (!inverse && is_on_coset); - if (direct_coset) - utils_internal::BatchMulKernel<<>>( - d_input, n, batch_size, arbitrary_coset ? arbitrary_coset : d_twiddles, arbitrary_coset ? 1 : coset_gen_index, - n_twiddles, logn, dit, d_output); - - if (dit) { - if (is_shared_mem_enabled) - ntt_template_kernel_shared<<>>( - direct_coset ? d_output : d_input, 1 << logn_shmem, d_twiddles, n_twiddles, total_tasks, 0, logn_shmem, - d_output); - - for (int s = logn_shmem; s < logn; s++) // TODO: this loop also can be unrolled - { - ntt_template_kernel<<>>( - (direct_coset || (s > 0)) ? d_output : d_input, n, d_twiddles, n_twiddles, total_tasks, s, false, d_output); - } - } else { - for (int s = logn - 1; s >= logn_shmem; s--) // TODO: this loop also can be unrolled - { - ntt_template_kernel<<>>( - (direct_coset || (s < logn - 1)) ? d_output : d_input, n, d_twiddles, n_twiddles, total_tasks, s, true, - d_output); - } - - if (is_shared_mem_enabled) - ntt_template_kernel_shared_rev<<>>( - (direct_coset || (logn > logn_shmem)) ? d_output : d_input, 1 << logn_shmem, d_twiddles, n_twiddles, - total_tasks, 0, logn_shmem, d_output); - } - - if (inverse) { - if (is_on_coset) - utils_internal::BatchMulKernel<<>>( - d_output, n, batch_size, arbitrary_coset ? arbitrary_coset : d_twiddles, - arbitrary_coset ? 1 : -coset_gen_index, -n_twiddles, logn, !dit, d_output); - - utils_internal::NormalizeKernel - <<>>(d_output, S::inv_log_size(logn), n * batch_size); - } - - return CHK_LAST(); - } - - } // namespace - - /** - * @struct Domain - * Struct containing information about the domain on which (i)NTT is evaluated i.e. twiddle factors. - * Twiddle factors are private, static and can only be set using [init_domain](@ref init_domain) function. - * The internal representation of twiddles is prone to change in accordance with changing [NTT](@ref NTT) algorithm. - * @tparam S The type of twiddle factors \f$ \{ \omega^i \} \f$. Must be a field. - */ - template - class Domain - { - // Mutex for protecting access to the domain/device container array - static inline std::mutex device_domain_mutex; - // The domain-per-device container - assumption is init_domain is called once per device per program. - - int max_size = 0; - int max_log_size = 0; - S* twiddles = nullptr; - bool initialized = false; // protection for multi-threaded case - std::unordered_map coset_index = {}; - - S* internal_twiddles = nullptr; // required by mixed-radix NTT - S* basic_twiddles = nullptr; // required by mixed-radix NTT - - // mixed-radix NTT supports a fast-twiddle option at the cost of additional 4N memory (where N is max NTT size) - S* fast_external_twiddles = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - S* fast_internal_twiddles = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - S* fast_basic_twiddles = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - S* fast_external_twiddles_inv = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - S* fast_internal_twiddles_inv = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - S* fast_basic_twiddles_inv = nullptr; // required by mixed-radix NTT (fast-twiddles mode) - - public: - template - friend cudaError_t init_domain(U primitive_root, device_context::DeviceContext& ctx, bool fast_tw); - - template - friend cudaError_t release_domain(device_context::DeviceContext& ctx); - - template - friend U get_root_of_unity(uint64_t logn, device_context::DeviceContext& ctx); - - template - friend U get_root_of_unity_from_domain(uint64_t logn, device_context::DeviceContext& ctx); - - template - friend cudaError_t ntt(const E* input, int size, NTTDir dir, NTTConfig& config, E* output); - }; - - template - static inline Domain domains_for_devices[device_context::MAX_DEVICES] = {}; - - template - cudaError_t init_domain(S primitive_root, device_context::DeviceContext& ctx, bool fast_twiddles_mode) - { - CHK_INIT_IF_RETURN(); - - Domain& domain = domains_for_devices[ctx.device_id]; - - // only generate twiddles if they haven't been generated yet - // please note that this offers just basic thread-safety, - // it's assumed a singleton (non-enforced) that is supposed - // to be initialized once per device per program lifetime - if (!domain.initialized) { - // Mutex is automatically released when lock goes out of scope, even in case of exceptions - std::lock_guard lock(Domain::device_domain_mutex); - // double check locking - if (domain.initialized) return CHK_LAST(); // another thread is already initializing the domain - - bool found_logn = false; - S omega = primitive_root; - unsigned omegas_count = S::get_omegas_count(); - for (int i = 0; i < omegas_count; i++) { - omega = S::sqr(omega); - if (!found_logn) { - ++domain.max_log_size; - found_logn = omega == S::one(); - if (found_logn) break; - } - } - - domain.max_size = (int)pow(2, domain.max_log_size); - if (omega != S::one()) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "Primitive root provided to the InitDomain function is not in the subgroup"); - } - - // allocate and calculate twiddles on GPU - // Note: radix-2 INTT needs ONE in last element (in addition to first element), therefore have n+1 elements - // Managed allocation allows host to read the elements (logn) without copying all (n) TFs back to host - CHK_IF_RETURN(cudaMallocManaged(&domain.twiddles, (domain.max_size + 1) * sizeof(S))); - CHK_IF_RETURN(mxntt::generate_external_twiddles_generic( - primitive_root, domain.twiddles, domain.internal_twiddles, domain.basic_twiddles, domain.max_log_size, - ctx.stream)); - - if (fast_twiddles_mode) { - // generating fast-twiddles (note that this cost 4N additional memory) - CHK_IF_RETURN(cudaMallocAsync(&domain.fast_external_twiddles, domain.max_size * sizeof(S) * 2, ctx.stream)); - CHK_IF_RETURN(cudaMallocAsync(&domain.fast_external_twiddles_inv, domain.max_size * sizeof(S) * 2, ctx.stream)); - - // fast-twiddles forward NTT - CHK_IF_RETURN(mxntt::generate_external_twiddles_fast_twiddles_mode( - primitive_root, domain.fast_external_twiddles, domain.fast_internal_twiddles, domain.fast_basic_twiddles, - domain.max_log_size, ctx.stream)); - - // fast-twiddles inverse NTT - S primitive_root_inv; - CHK_IF_RETURN(cudaMemcpyAsync( - &primitive_root_inv, &domain.twiddles[domain.max_size - 1], sizeof(S), cudaMemcpyDeviceToHost, ctx.stream)); - CHK_IF_RETURN(mxntt::generate_external_twiddles_fast_twiddles_mode( - primitive_root_inv, domain.fast_external_twiddles_inv, domain.fast_internal_twiddles_inv, - domain.fast_basic_twiddles_inv, domain.max_log_size, ctx.stream)); - } - CHK_IF_RETURN(cudaStreamSynchronize(ctx.stream)); - - const bool is_map_only_powers_of_primitive_root = true; - if (is_map_only_powers_of_primitive_root) { - // populate the coset_index map. Note that only powers of the primitive-root are stored (1, PR, PR^2, PR^4, PR^8 - // etc.) - domain.coset_index[S::one()] = 0; - for (int i = 0; i < domain.max_log_size; ++i) { - const int index = (int)pow(2, i); - domain.coset_index[domain.twiddles[index]] = index; - } - } else { - // populate all values - for (int i = 0; i < domain.max_size; ++i) { - domain.coset_index[domain.twiddles[i]] = i; - } - } - domain.initialized = true; - } - - return CHK_LAST(); - } - - template - cudaError_t release_domain(device_context::DeviceContext& ctx) - { - CHK_INIT_IF_RETURN(); - - Domain& domain = domains_for_devices[ctx.device_id]; - - domain.max_size = 0; - domain.max_log_size = 0; - domain.twiddles = nullptr; // allocated via cudaMallocManaged(...) so released without calling cudaFree(...) - CHK_IF_RETURN(cudaFreeAsync(domain.internal_twiddles, ctx.stream)); - domain.internal_twiddles = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.basic_twiddles, ctx.stream)); - domain.basic_twiddles = nullptr; - domain.coset_index.clear(); - - CHK_IF_RETURN(cudaFreeAsync(domain.fast_external_twiddles, ctx.stream)); - domain.fast_external_twiddles = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.fast_internal_twiddles, ctx.stream)); - domain.fast_internal_twiddles = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.fast_basic_twiddles, ctx.stream)); - domain.fast_basic_twiddles = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.fast_external_twiddles_inv, ctx.stream)); - domain.fast_external_twiddles_inv = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.fast_internal_twiddles_inv, ctx.stream)); - domain.fast_internal_twiddles_inv = nullptr; - CHK_IF_RETURN(cudaFreeAsync(domain.fast_basic_twiddles_inv, ctx.stream)); - domain.fast_basic_twiddles_inv = nullptr; - domain.initialized = false; - - return CHK_LAST(); - } - - template - S get_root_of_unity(uint64_t max_size) - { - // ceil up - const auto log_max_size = static_cast(std::ceil(std::log2(max_size))); - return S::omega(log_max_size); - } - // explicit instantiation to avoid having to include this file - template scalar_t get_root_of_unity(uint64_t logn); - - template - S get_root_of_unity_from_domain(uint64_t logn, device_context::DeviceContext& ctx) - { - Domain& domain = domains_for_devices[ctx.device_id]; - if (logn > domain.max_log_size) { - std::ostringstream oss; - oss << "NTT log_size=" << logn - << " is too large for the domain. Consider generating your domain with a higher order root of unity.\n"; - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, oss.str().c_str()); - } - const size_t twiddles_idx = 1ULL << (domain.max_log_size - logn); - return domain.twiddles[twiddles_idx]; - } - // explicit instantiation to avoid having to include this file - template scalar_t get_root_of_unity_from_domain(uint64_t logn, device_context::DeviceContext& ctx); - - template - static bool is_choosing_radix2_algorithm(int logn, int batch_size, const NTTConfig& config) - { - const bool is_mixed_radix_alg_supported = (logn > 3 && logn != 7); - if (!is_mixed_radix_alg_supported && config.columns_batch) - throw IcicleError(IcicleError_t::InvalidArgument, "columns batch is not supported for given NTT size"); - const bool is_user_selected_radix2_alg = config.ntt_algorithm == NttAlgorithm::Radix2; - const bool is_force_radix2 = !is_mixed_radix_alg_supported || is_user_selected_radix2_alg; - if (is_force_radix2) return true; - - const bool is_user_selected_mixed_radix_alg = config.ntt_algorithm == NttAlgorithm::MixedRadix; - if (is_user_selected_mixed_radix_alg) return false; - if (config.columns_batch) return false; // radix2 does not currently support columns batch mode. - - // Heuristic to automatically select an algorithm - // Note that generally the decision depends on {logn, batch, ordering, inverse, coset, in-place, coeff-field} and - // the specific GPU. - // the following heuristic is a simplification based on measurements. Users can try both and select the algorithm - // based on the specific case via the 'NTTConfig.ntt_algorithm' field - - if (logn >= 16) return false; // mixed-radix is typically faster in those cases - if (logn <= 11) return true; // radix-2 is typically faster for batch<=256 in those cases - const int log_batch = (int)log2(batch_size); - return (logn + log_batch <= 18); // almost the cutoff point where both are equal - } - - template - cudaError_t radix2_ntt( - const E* d_input, - E* d_output, - S* twiddles, - int ntt_size, - int max_size, - int batch_size, - bool is_inverse, - Ordering ordering, - S* arbitrary_coset, - int coset_gen_index, - cudaStream_t cuda_stream) - { - CHK_INIT_IF_RETURN(); - - const int logn = int(log2(ntt_size)); - - bool dit = true; - bool reverse_input = false; - switch (ordering) { - case Ordering::kNN: - reverse_input = true; - break; - case Ordering::kNR: - case Ordering::kNM: - dit = false; - break; - case Ordering::kRR: - reverse_input = true; - dit = false; - break; - case Ordering::kRN: - case Ordering::kMN: - reverse_input = false; - } - - if (reverse_input) reverse_order_batch(d_input, ntt_size, logn, batch_size, cuda_stream, d_output); - - CHK_IF_RETURN(ntt_inplace_batch_template( - reverse_input ? d_output : d_input, ntt_size, twiddles, max_size, batch_size, logn, is_inverse, dit, - arbitrary_coset, coset_gen_index, cuda_stream, d_output)); - - return CHK_LAST(); - } - - template - cudaError_t ntt(const E* input, int size, NTTDir dir, NTTConfig& config, E* output) - { - CHK_INIT_IF_RETURN(); - - Domain& domain = domains_for_devices[config.ctx.device_id]; - - if (size > domain.max_size) { - std::ostringstream oss; - oss << "NTT size=" << size - << " is too large for the domain. Consider generating your domain with a higher order root of unity.\n"; - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, oss.str().c_str()); - } - - int logn = int(log2(size)); - const bool is_size_power_of_two = size == (1 << logn); - if (!is_size_power_of_two) { - std::ostringstream oss; - oss << "NTT size=" << size << " is not supported since it is not a power of two.\n"; - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, oss.str().c_str()); - } - - cudaStream_t& stream = config.ctx.stream; - size_t batch_size = config.batch_size; - size_t input_size_bytes = (size_t)size * batch_size * sizeof(E); - bool are_inputs_on_device = config.are_inputs_on_device; - bool are_outputs_on_device = config.are_outputs_on_device; - - const E* d_input; - E* d_allocated_input = nullptr; - if (are_inputs_on_device) { - d_input = input; - } else { - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_input, input_size_bytes, stream)); - CHK_IF_RETURN(cudaMemcpyAsync(d_allocated_input, input, input_size_bytes, cudaMemcpyHostToDevice, stream)); - d_input = d_allocated_input; - } - E* d_output; - E* d_allocated_output = nullptr; - if (are_outputs_on_device) { - d_output = output; - } else { - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_output, input_size_bytes, stream)); - d_output = d_allocated_output; - } - - S* coset = nullptr; - int coset_index = 0; - try { - coset_index = domain.coset_index.at(config.coset_gen); - } catch (...) { - // if coset index is not found in the subgroup, compute coset powers on CPU and move them to device - std::vector h_coset; - h_coset.push_back(S::one()); - S coset_gen = (dir == NTTDir::kInverse) ? S::inverse(config.coset_gen) : config.coset_gen; - for (int i = 1; i < size; i++) { - h_coset.push_back(h_coset.at(i - 1) * coset_gen); - } - CHK_IF_RETURN(cudaMallocAsync(&coset, size * sizeof(S), stream)); - CHK_IF_RETURN(cudaMemcpyAsync(coset, &h_coset.front(), size * sizeof(S), cudaMemcpyHostToDevice, stream)); - h_coset.clear(); - } - - const bool is_inverse = dir == NTTDir::kInverse; - - if constexpr (IS_ECNTT) { - CHK_IF_RETURN(ntt::radix2_ntt( - d_input, d_output, domain.twiddles, size, domain.max_size, batch_size, is_inverse, config.ordering, coset, - coset_index, stream)); - } else { - const bool is_radix2_algorithm = is_choosing_radix2_algorithm(logn, batch_size, config); - if (is_radix2_algorithm) { - CHK_IF_RETURN(ntt::radix2_ntt( - d_input, d_output, domain.twiddles, size, domain.max_size, batch_size, is_inverse, config.ordering, coset, - coset_index, stream)); - } else { - const bool is_fast_twiddles_enabled = (domain.fast_external_twiddles != nullptr); - S* twiddles = is_fast_twiddles_enabled - ? (is_inverse ? domain.fast_external_twiddles_inv : domain.fast_external_twiddles) - : domain.twiddles; - S* internal_twiddles = is_fast_twiddles_enabled - ? (is_inverse ? domain.fast_internal_twiddles_inv : domain.fast_internal_twiddles) - : domain.internal_twiddles; - S* basic_twiddles = is_fast_twiddles_enabled - ? (is_inverse ? domain.fast_basic_twiddles_inv : domain.fast_basic_twiddles) - : domain.basic_twiddles; - S* linear_twiddles = domain.twiddles; // twiddles organized as [1,w,w^2,...] - CHK_IF_RETURN(mxntt::mixed_radix_ntt( - d_input, d_output, twiddles, internal_twiddles, basic_twiddles, linear_twiddles, size, domain.max_log_size, - batch_size, config.columns_batch, is_inverse, is_fast_twiddles_enabled, config.ordering, coset, coset_index, - stream)); - } - } - - if (!are_outputs_on_device) - CHK_IF_RETURN(cudaMemcpyAsync(output, d_output, input_size_bytes, cudaMemcpyDeviceToHost, stream)); - - if (coset) CHK_IF_RETURN(cudaFreeAsync(coset, stream)); - if (d_allocated_input) CHK_IF_RETURN(cudaFreeAsync(d_allocated_input, stream)); - if (d_allocated_output) CHK_IF_RETURN(cudaFreeAsync(d_allocated_output, stream)); - if (!config.is_async) return CHK_STICKY(cudaStreamSynchronize(stream)); - - return CHK_LAST(); - } - - template - NTTConfig default_ntt_config(const device_context::DeviceContext& ctx) - { - NTTConfig config = { - ctx, // ctx - S::one(), // coset_gen - 1, // batch_size - false, // columns_batch - Ordering::kNN, // ordering - false, // are_inputs_on_device - false, // are_outputs_on_device - false, // is_async - NttAlgorithm::Auto, // ntt_algorithm - }; - return config; - } - // explicit instantiation to avoid having to include this file - template NTTConfig default_ntt_config(const device_context::DeviceContext& ctx); -} // namespace ntt \ No newline at end of file diff --git a/icicle/src/ntt/tests/verification.cu b/icicle/src/ntt/tests/verification.cu deleted file mode 100644 index 21e143a03..000000000 --- a/icicle/src/ntt/tests/verification.cu +++ /dev/null @@ -1,207 +0,0 @@ -#include "fields/id.h" -#define FIELD_ID BN254 - -#ifdef ECNTT -#define CURVE_ID BN254 -#include "curves/curve_config.cuh" -typedef field_config::scalar_t test_scalar; -typedef curve_config::projective_t test_data; -#else -#include "fields/field_config.cuh" -typedef field_config::scalar_t test_scalar; -typedef field_config::scalar_t test_data; -#endif - -#include "fields/field.cuh" -#include "curves/projective.cuh" -#include -#include -#include - -#include "ntt.cu" -#include "kernel_ntt.cu" -#include - -void random_samples(test_data* res, uint32_t count) -{ - for (int i = 0; i < count; i++) - res[i] = i < 1000 ? test_data::rand_host() : res[i - 1000]; -} - -void incremental_values(test_scalar* res, uint32_t count) -{ - for (int i = 0; i < count; i++) { - res[i] = i ? res[i - 1] + test_scalar::one() : test_scalar::zero(); - } -} - -__global__ void transpose_batch(test_scalar* in, test_scalar* out, int row_size, int column_size) -{ - int tid = blockDim.x * blockIdx.x + threadIdx.x; - if (tid >= row_size * column_size) return; - out[(tid % row_size) * column_size + (tid / row_size)] = in[tid]; -} - -int main(int argc, char** argv) -{ - cudaEvent_t icicle_start, icicle_stop, new_start, new_stop; - float icicle_time, new_time; - - int NTT_LOG_SIZE = (argc > 1) ? atoi(argv[1]) : 19; - int NTT_SIZE = 1 << NTT_LOG_SIZE; - bool INPLACE = (argc > 2) ? atoi(argv[2]) : false; - int INV = (argc > 3) ? atoi(argv[3]) : false; - int BATCH_SIZE = (argc > 4) ? atoi(argv[4]) : 150; - bool COLUMNS_BATCH = (argc > 5) ? atoi(argv[5]) : false; - int COSET_IDX = (argc > 6) ? atoi(argv[6]) : 2; - const ntt::Ordering ordering = (argc > 7) ? ntt::Ordering(atoi(argv[7])) : ntt::Ordering::kNN; - bool FAST_TW = (argc > 8) ? atoi(argv[8]) : true; - - // Note: NM, MN are not expected to be equal when comparing mixed-radix and radix-2 NTTs - const char* ordering_str = ordering == ntt::Ordering::kNN ? "NN" - : ordering == ntt::Ordering::kNR ? "NR" - : ordering == ntt::Ordering::kRN ? "RN" - : ordering == ntt::Ordering::kRR ? "RR" - : ordering == ntt::Ordering::kNM ? "NM" - : "MN"; - - printf( - "running ntt 2^%d, inplace=%d, inverse=%d, batch_size=%d, columns_batch=%d coset-idx=%d, ordering=%s, fast_tw=%d\n", - NTT_LOG_SIZE, INPLACE, INV, BATCH_SIZE, COLUMNS_BATCH, COSET_IDX, ordering_str, FAST_TW); - - CHK_IF_RETURN(cudaFree(nullptr)); // init GPU context (warmup) - - // init domain - auto ntt_config = ntt::default_ntt_config(); - ntt_config.ordering = ordering; - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.batch_size = BATCH_SIZE; - ntt_config.columns_batch = COLUMNS_BATCH; - - CHK_IF_RETURN(cudaEventCreate(&icicle_start)); - CHK_IF_RETURN(cudaEventCreate(&icicle_stop)); - CHK_IF_RETURN(cudaEventCreate(&new_start)); - CHK_IF_RETURN(cudaEventCreate(&new_stop)); - - auto start = std::chrono::high_resolution_clock::now(); - const scalar_t basic_root = test_scalar::omega(NTT_LOG_SIZE); - ntt::init_domain(basic_root, ntt_config.ctx, FAST_TW); - auto stop = std::chrono::high_resolution_clock::now(); - auto duration = std::chrono::duration_cast(stop - start).count(); - std::cout << "initDomain took: " << duration / 1000 << " MS" << std::endl; - - // cpu allocation - auto CpuScalars = std::make_unique(NTT_SIZE * BATCH_SIZE); - auto CpuOutputOld = std::make_unique(NTT_SIZE * BATCH_SIZE); - auto CpuOutputNew = std::make_unique(NTT_SIZE * BATCH_SIZE); - - // gpu allocation - scalar_t *GpuScalars, *GpuOutputOld, *GpuOutputNew; - scalar_t* GpuScalarsTransposed; - CHK_IF_RETURN(cudaMalloc(&GpuScalars, sizeof(test_data) * NTT_SIZE * BATCH_SIZE)); - CHK_IF_RETURN(cudaMalloc(&GpuScalarsTransposed, sizeof(test_data) * NTT_SIZE * BATCH_SIZE)); - CHK_IF_RETURN(cudaMalloc(&GpuOutputOld, sizeof(test_data) * NTT_SIZE * BATCH_SIZE)); - CHK_IF_RETURN(cudaMalloc(&GpuOutputNew, sizeof(test_data) * NTT_SIZE * BATCH_SIZE)); - - // init inputs - // incremental_values(CpuScalars.get(), NTT_SIZE * BATCH_SIZE); - random_samples(CpuScalars.get(), NTT_SIZE * BATCH_SIZE); - CHK_IF_RETURN( - cudaMemcpy(GpuScalars, CpuScalars.get(), NTT_SIZE * BATCH_SIZE * sizeof(test_data), cudaMemcpyHostToDevice)); - - if (COLUMNS_BATCH) { - transpose_batch<<<(NTT_SIZE * BATCH_SIZE + 256 - 1) / 256, 256>>>( - GpuScalars, GpuScalarsTransposed, NTT_SIZE, BATCH_SIZE); - } - - // inplace - if (INPLACE) { - CHK_IF_RETURN(cudaMemcpy( - GpuOutputNew, COLUMNS_BATCH ? GpuScalarsTransposed : GpuScalars, NTT_SIZE * BATCH_SIZE * sizeof(test_data), - cudaMemcpyDeviceToDevice)); - } - - for (int coset_idx = 0; coset_idx < COSET_IDX; ++coset_idx) { - ntt_config.coset_gen = ntt_config.coset_gen * basic_root; - } - - auto benchmark = [&](bool is_print, int iterations) -> cudaError_t { - // NEW - CHK_IF_RETURN(cudaEventRecord(new_start, ntt_config.ctx.stream)); - ntt_config.ntt_algorithm = ntt::NttAlgorithm::MixedRadix; - for (size_t i = 0; i < iterations; i++) { - CHK_IF_RETURN(ntt::ntt( - INPLACE ? GpuOutputNew - : COLUMNS_BATCH ? GpuScalarsTransposed - : GpuScalars, - NTT_SIZE, INV ? ntt::NTTDir::kInverse : ntt::NTTDir::kForward, ntt_config, GpuOutputNew)); - } - CHK_IF_RETURN(cudaEventRecord(new_stop, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaStreamSynchronize(ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaEventElapsedTime(&new_time, new_start, new_stop)); - - // OLD - CHK_IF_RETURN(cudaEventRecord(icicle_start, ntt_config.ctx.stream)); - ntt_config.ntt_algorithm = ntt::NttAlgorithm::Radix2; - for (size_t i = 0; i < iterations; i++) { - CHK_IF_RETURN( - ntt::ntt(GpuScalars, NTT_SIZE, INV ? ntt::NTTDir::kInverse : ntt::NTTDir::kForward, ntt_config, GpuOutputOld)); - } - CHK_IF_RETURN(cudaEventRecord(icicle_stop, ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaStreamSynchronize(ntt_config.ctx.stream)); - CHK_IF_RETURN(cudaEventElapsedTime(&icicle_time, icicle_start, icicle_stop)); - - if (is_print) { - printf("Old Runtime=%0.3f MS\n", icicle_time / iterations); - printf("New Runtime=%0.3f MS\n", new_time / iterations); - } - - return CHK_LAST(); - }; - - CHK_IF_RETURN(benchmark(false /*=print*/, 1)); // warmup - int count = INPLACE ? 1 : 10; - if (INPLACE) { - CHK_IF_RETURN(cudaMemcpy( - GpuOutputNew, COLUMNS_BATCH ? GpuScalarsTransposed : GpuScalars, NTT_SIZE * BATCH_SIZE * sizeof(test_data), - cudaMemcpyDeviceToDevice)); - } - CHK_IF_RETURN(benchmark(true /*=print*/, count)); - - if (COLUMNS_BATCH) { - transpose_batch<<<(NTT_SIZE * BATCH_SIZE + 256 - 1) / 256, 256>>>( - GpuOutputNew, GpuScalarsTransposed, BATCH_SIZE, NTT_SIZE); - CHK_IF_RETURN(cudaMemcpy( - GpuOutputNew, GpuScalarsTransposed, NTT_SIZE * BATCH_SIZE * sizeof(test_data), cudaMemcpyDeviceToDevice)); - } - - // verify - CHK_IF_RETURN( - cudaMemcpy(CpuOutputNew.get(), GpuOutputNew, NTT_SIZE * BATCH_SIZE * sizeof(test_data), cudaMemcpyDeviceToHost)); - CHK_IF_RETURN( - cudaMemcpy(CpuOutputOld.get(), GpuOutputOld, NTT_SIZE * BATCH_SIZE * sizeof(test_data), cudaMemcpyDeviceToHost)); - - bool success = true; - for (int i = 0; i < NTT_SIZE * BATCH_SIZE; i++) { - // if (i%64==0) printf("\n"); - if (CpuOutputNew[i] != CpuOutputOld[i]) { - success = false; - // std::cout << i << " ref " << CpuOutputOld[i] << " != " << CpuOutputNew[i] << std::endl; - // break; - } else { - // std::cout << i << " ref " << CpuOutputOld[i] << " == " << CpuOutputNew[i] << std::endl; - // break; - } - } - const char* success_str = success ? "SUCCESS!" : "FAIL!"; - printf("%s\n", success_str); - - CHK_IF_RETURN(cudaFree(GpuScalars)); - CHK_IF_RETURN(cudaFree(GpuOutputOld)); - CHK_IF_RETURN(cudaFree(GpuOutputNew)); - - ntt::release_domain(ntt_config.ctx); - - return CHK_LAST(); -} \ No newline at end of file diff --git a/icicle/src/ntt/thread_ntt.cu b/icicle/src/ntt/thread_ntt.cu deleted file mode 100644 index 8fd2764df..000000000 --- a/icicle/src/ntt/thread_ntt.cu +++ /dev/null @@ -1,733 +0,0 @@ -#ifndef T_NTT -#define T_NTT -#pragma once - -#include -#include -#include "gpu-utils/modifiers.cuh" - -struct stage_metadata { - uint32_t th_stride; - uint32_t ntt_block_size; - uint32_t batch_id; - uint32_t ntt_block_id; - uint32_t ntt_inp_id; -}; - -#define STAGE_SIZES_DATA \ - { \ - {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {4, 0, 0, 0, 0}, {5, 0, 0, 0, 0}, \ - {6, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {4, 4, 0, 0, 0}, {5, 4, 0, 0, 0}, {5, 5, 0, 0, 0}, {6, 5, 0, 0, 0}, \ - {6, 6, 0, 0, 0}, {4, 5, 4, 0, 0}, {4, 6, 4, 0, 0}, {5, 5, 5, 0, 0}, {6, 4, 6, 0, 0}, {6, 5, 6, 0, 0}, \ - {6, 6, 6, 0, 0}, {6, 5, 4, 4, 0}, {5, 5, 5, 5, 0}, {6, 5, 5, 5, 0}, {6, 5, 5, 6, 0}, {6, 6, 6, 5, 0}, \ - {6, 6, 6, 6, 0}, {5, 5, 5, 5, 5}, {6, 5, 4, 5, 6}, {6, 5, 5, 5, 6}, {6, 5, 6, 5, 6}, {6, 6, 5, 6, 6}, \ - {6, 6, 6, 6, 6}, \ - } -uint32_t constexpr STAGE_SIZES_HOST[31][5] = STAGE_SIZES_DATA; -__device__ constexpr uint32_t STAGE_SIZES_DEVICE[31][5] = STAGE_SIZES_DATA; - -// construction for fast-twiddles -uint32_t constexpr STAGE_PREV_SIZES[31] = {0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 6, 6, 9, 9, 10, - 11, 11, 12, 15, 15, 16, 16, 18, 18, 20, 21, 21, 22, 23, 24}; - -#define STAGE_SIZES_DATA_FAST_TW \ - { \ - {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {4, 0, 0, 0, 0}, {5, 0, 0, 0, 0}, \ - {6, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {4, 4, 0, 0, 0}, {5, 4, 0, 0, 0}, {5, 5, 0, 0, 0}, {6, 5, 0, 0, 0}, \ - {6, 6, 0, 0, 0}, {5, 4, 4, 0, 0}, {5, 4, 5, 0, 0}, {5, 5, 5, 0, 0}, {6, 5, 5, 0, 0}, {6, 5, 6, 0, 0}, \ - {6, 6, 6, 0, 0}, {5, 5, 5, 4, 0}, {5, 5, 5, 5, 0}, {6, 5, 5, 5, 0}, {6, 5, 5, 6, 0}, {6, 6, 6, 5, 0}, \ - {6, 6, 6, 6, 0}, {5, 5, 5, 5, 5}, {6, 5, 5, 5, 5}, {6, 5, 5, 5, 6}, {6, 5, 5, 6, 6}, {6, 6, 6, 5, 6}, \ - {6, 6, 6, 6, 6}, \ - } -uint32_t constexpr STAGE_SIZES_HOST_FT[31][5] = STAGE_SIZES_DATA_FAST_TW; -__device__ uint32_t constexpr STAGE_SIZES_DEVICE_FT[31][5] = STAGE_SIZES_DATA_FAST_TW; - -template -class NTTEngine -{ -public: - E X[8]; - S WB[3]; - S WI[7]; - S WE[8]; - - DEVICE_INLINE void loadBasicTwiddles(S* basic_twiddles) - { - UNROLL - for (int i = 0; i < 3; i++) { - WB[i] = basic_twiddles[i]; - } - } - - DEVICE_INLINE void loadBasicTwiddlesGeneric(S* basic_twiddles, bool inv) - { - UNROLL - for (int i = 0; i < 3; i++) { - WB[i] = basic_twiddles[inv ? i + 3 : i]; - } - } - - DEVICE_INLINE void loadInternalTwiddles64(S* data, bool stride) - { - UNROLL - for (int i = 0; i < 7; i++) { - WI[i] = data[((stride ? (threadIdx.x >> 3) : (threadIdx.x)) & 0x7) * (i + 1)]; - } - } - - DEVICE_INLINE void loadInternalTwiddles32(S* data, bool stride) - { - UNROLL - for (int i = 0; i < 7; i++) { - WI[i] = data[2 * ((stride ? (threadIdx.x >> 4) : (threadIdx.x)) & 0x3) * (i + 1)]; - } - } - - DEVICE_INLINE void loadInternalTwiddles16(S* data, bool stride) - { - UNROLL - for (int i = 0; i < 7; i++) { - WI[i] = data[4 * ((stride ? (threadIdx.x >> 5) : (threadIdx.x)) & 0x1) * (i + 1)]; - } - } - - DEVICE_INLINE void loadInternalTwiddlesGeneric64(S* data, bool stride, bool inv) - { - UNROLL - for (int i = 0; i < 7; i++) { - uint32_t exp = ((stride ? (threadIdx.x >> 3) : (threadIdx.x)) & 0x7) * (i + 1); - WI[i] = data[(inv && exp) ? 64 - exp : exp]; // if exp = 0 we also take exp and not 64-exp - } - } - - DEVICE_INLINE void loadInternalTwiddlesGeneric32(S* data, bool stride, bool inv) - { - UNROLL - for (int i = 0; i < 7; i++) { - uint32_t exp = 2 * ((stride ? (threadIdx.x >> 4) : (threadIdx.x)) & 0x3) * (i + 1); - WI[i] = data[(inv && exp) ? 64 - exp : exp]; - } - } - - DEVICE_INLINE void loadInternalTwiddlesGeneric16(S* data, bool stride, bool inv) - { - UNROLL - for (int i = 0; i < 7; i++) { - uint32_t exp = 4 * ((stride ? (threadIdx.x >> 5) : (threadIdx.x)) & 0x1) * (i + 1); - WI[i] = data[(inv && exp) ? 64 - exp : exp]; - } - } - - DEVICE_INLINE void loadExternalTwiddles64(S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta) - { - data += tw_order * s_meta.ntt_inp_id + (s_meta.ntt_block_id & (tw_order - 1)); - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - WE[i] = data[8 * i * tw_order + (1 << tw_log_order + 6) - 1]; - } - } - - DEVICE_INLINE void loadExternalTwiddles32(S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta) - { - data += tw_order * s_meta.ntt_inp_id * 2 + (s_meta.ntt_block_id & (tw_order - 1)); - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - WE[4 * j + i] = data[(8 * i + j) * tw_order + (1 << tw_log_order + 5) - 1]; - } - } - } - - DEVICE_INLINE void loadExternalTwiddles16(S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta) - { - data += tw_order * s_meta.ntt_inp_id * 4 + (s_meta.ntt_block_id & (tw_order - 1)); - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - WE[2 * j + i] = data[(8 * i + j) * tw_order + (1 << tw_log_order + 4) - 1]; - } - } - } - - DEVICE_INLINE void loadExternalTwiddlesGeneric64( - S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta, uint32_t tw_log_size, bool inv) - { - UNROLL - for (uint32_t i = 0; i < 8; i++) { - uint32_t exp = (s_meta.ntt_inp_id + 8 * i) * (s_meta.ntt_block_id & (tw_order - 1)) - << (tw_log_size - tw_log_order - 6); - WE[i] = data[(inv && exp) ? ((1 << tw_log_size) - exp) : exp]; - } - } - - DEVICE_INLINE void loadExternalTwiddlesGeneric32( - S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta, uint32_t tw_log_size, bool inv) - { - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - uint32_t exp = (s_meta.ntt_inp_id * 2 + 8 * i + j) * (s_meta.ntt_block_id & (tw_order - 1)) - << (tw_log_size - tw_log_order - 5); - WE[4 * j + i] = data[(inv && exp) ? ((1 << tw_log_size) - exp) : exp]; - } - } - } - - DEVICE_INLINE void loadExternalTwiddlesGeneric16( - S* data, uint32_t tw_order, uint32_t tw_log_order, stage_metadata s_meta, uint32_t tw_log_size, bool inv) - { - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - uint32_t exp = (s_meta.ntt_inp_id * 4 + 8 * i + j) * (s_meta.ntt_block_id & (tw_order - 1)) - << (tw_log_size - tw_log_order - 4); - WE[2 * j + i] = data[(inv && exp) ? ((1 << tw_log_size) - exp) : exp]; - } - } - } - - DEVICE_INLINE void - loadGlobalData(const E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id; - } - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - X[i] = data[s_meta.th_stride * i * data_stride_u64]; - } - } - - DEVICE_INLINE void loadGlobalDataColumnBatch( - const E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - X[i] = data[s_meta.th_stride * i * data_stride_u64 * batch_size]; - } - } - - DEVICE_INLINE void - storeGlobalData(E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id; - } - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - data[s_meta.th_stride * i * data_stride_u64] = X[i]; - } - } - - DEVICE_INLINE void storeGlobalDataColumnBatch( - E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - data[s_meta.th_stride * i * data_stride_u64 * batch_size] = X[i]; - } - } - - DEVICE_INLINE void - loadGlobalData32(const E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 2 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id * 2; - } - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - X[4 * j + i] = data[(8 * i + j) * data_stride_u64]; - } - } - } - - DEVICE_INLINE void loadGlobalData32ColumnBatch( - const E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 2 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - X[4 * j + i] = data[(8 * i + j) * data_stride_u64 * batch_size]; - } - } - } - - DEVICE_INLINE void - storeGlobalData32(E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 2 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id * 2; - } - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - data[(8 * i + j) * data_stride_u64] = X[4 * j + i]; - } - } - } - - DEVICE_INLINE void storeGlobalData32ColumnBatch( - E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 2 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - data[(8 * i + j) * data_stride_u64 * batch_size] = X[4 * j + i]; - } - } - } - - DEVICE_INLINE void - loadGlobalData16(const E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 4 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id * 4; - } - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - X[2 * j + i] = data[(8 * i + j) * data_stride_u64]; - } - } - } - - DEVICE_INLINE void loadGlobalData16ColumnBatch( - const E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 4 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - X[2 * j + i] = data[(8 * i + j) * data_stride_u64 * batch_size]; - } - } - } - - DEVICE_INLINE void - storeGlobalData16(E* data, uint32_t data_stride, uint32_t log_data_stride, bool strided, stage_metadata s_meta) - { - const uint64_t data_stride_u64 = data_stride; - if (strided) { - data += (s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 4 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size; - } else { - data += (uint64_t)s_meta.ntt_block_id * s_meta.ntt_block_size + s_meta.ntt_inp_id * 4; - } - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - data[(8 * i + j) * data_stride_u64] = X[2 * j + i]; - } - } - } - - DEVICE_INLINE void storeGlobalData16ColumnBatch( - E* data, uint32_t data_stride, uint32_t log_data_stride, stage_metadata s_meta, uint32_t batch_size) - { - const uint64_t data_stride_u64 = data_stride; - data += ((s_meta.ntt_block_id & (data_stride - 1)) + data_stride_u64 * s_meta.ntt_inp_id * 4 + - (s_meta.ntt_block_id >> log_data_stride) * data_stride_u64 * s_meta.ntt_block_size) * - batch_size + - s_meta.batch_id; - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - data[(8 * i + j) * data_stride_u64 * batch_size] = X[2 * j + i]; - } - } - } - - DEVICE_INLINE void ntt4_2() - { - UNROLL - for (int i = 0; i < 2; i++) { - ntt4(X[4 * i], X[4 * i + 1], X[4 * i + 2], X[4 * i + 3]); - } - } - - DEVICE_INLINE void ntt2_4() - { - UNROLL - for (int i = 0; i < 4; i++) { - ntt2(X[2 * i], X[2 * i + 1]); - } - } - - DEVICE_INLINE void ntt2(E& X0, E& X1) - { - E T; - - T = X0 + X1; - X1 = X0 - X1; - X0 = T; - } - - DEVICE_INLINE void ntt4(E& X0, E& X1, E& X2, E& X3) - { - E T; - - T = X0 + X2; - X2 = X0 - X2; - X0 = X1 + X3; - X1 = X1 - X3; // T has X0, X0 has X1, X2 has X2, X1 has X3 - - X1 = X1 * WB[0]; - - X3 = X2 - X1; - X1 = X2 + X1; - X2 = T - X0; - X0 = T + X0; - } - - // rbo version - DEVICE_INLINE void ntt4rbo(E& X0, E& X1, E& X2, E& X3) - { - E T; - - T = X0 - X1; - X0 = X0 + X1; - X1 = X2 + X3; - X3 = X2 - X3; // T has X0, X0 has X1, X2 has X2, X1 has X3 - - X3 = X3 * WB[0]; - - X2 = X0 - X1; - X0 = X0 + X1; - X1 = T + X3; - X3 = T - X3; - } - - DEVICE_INLINE void ntt8(E& X0, E& X1, E& X2, E& X3, E& X4, E& X5, E& X6, E& X7) - { - E T; - - // out of 56,623,104 possible mappings, we have: - T = X3 - X7; - X7 = X3 + X7; - X3 = X1 - X5; - X5 = X1 + X5; - X1 = X2 + X6; - X2 = X2 - X6; - X6 = X0 + X4; - X0 = X0 - X4; - - T = T * WB[1]; - X2 = X2 * WB[1]; - - X4 = X6 + X1; - X6 = X6 - X1; - X1 = X3 + T; - X3 = X3 - T; - T = X5 + X7; - X5 = X5 - X7; - X7 = X0 + X2; - X0 = X0 - X2; - - X1 = X1 * WB[0]; - X5 = X5 * WB[1]; - X3 = X3 * WB[2]; - - X2 = X6 + X5; - X6 = X6 - X5; - X5 = X7 - X1; - X1 = X7 + X1; - X7 = X0 - X3; - X3 = X0 + X3; - X0 = X4 + T; - X4 = X4 - T; - } - - DEVICE_INLINE void ntt8win() - { - E T; - - T = X[3] - X[7]; - X[7] = X[3] + X[7]; - X[3] = X[1] - X[5]; - X[5] = X[1] + X[5]; - X[1] = X[2] + X[6]; - X[2] = X[2] - X[6]; - X[6] = X[0] + X[4]; - X[0] = X[0] - X[4]; - - X[2] = X[2] * WB[0]; - - X[4] = X[6] + X[1]; - X[6] = X[6] - X[1]; - X[1] = X[3] + T; - X[3] = X[3] - T; - T = X[5] + X[7]; - X[5] = X[5] - X[7]; - X[7] = X[0] + X[2]; - X[0] = X[0] - X[2]; - - X[1] = X[1] * WB[1]; - X[5] = X[5] * WB[0]; - X[3] = X[3] * WB[2]; - - X[2] = X[6] + X[5]; - X[6] = X[6] - X[5]; - - X[5] = X[1] + X[3]; - X[3] = X[1] - X[3]; - - X[1] = X[7] + X[5]; - X[5] = X[7] - X[5]; - X[7] = X[0] - X[3]; - X[3] = X[0] + X[3]; - X[0] = X[4] + T; - X[4] = X[4] - T; - } - - DEVICE_INLINE void SharedData64Columns8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x7 : threadIdx.x >> 3; - uint32_t column_id = stride ? threadIdx.x >> 3 : threadIdx.x & 0x7; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 64 + i * 8 + column_id] = X[i]; - } else { - X[i] = shmem[ntt_id * 64 + i * 8 + column_id]; - } - } - } - - DEVICE_INLINE void SharedData64Rows8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x7 : threadIdx.x >> 3; - uint32_t row_id = stride ? threadIdx.x >> 3 : threadIdx.x & 0x7; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 64 + row_id * 8 + i] = X[i]; - } else { - X[i] = shmem[ntt_id * 64 + row_id * 8 + i]; - } - } - } - - DEVICE_INLINE void SharedData32Columns8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0xf : threadIdx.x >> 2; - uint32_t column_id = stride ? threadIdx.x >> 4 : threadIdx.x & 0x3; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 32 + i * 4 + column_id] = X[i]; - } else { - X[i] = shmem[ntt_id * 32 + i * 4 + column_id]; - } - } - } - - DEVICE_INLINE void SharedData32Rows8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0xf : threadIdx.x >> 2; - uint32_t row_id = stride ? threadIdx.x >> 4 : threadIdx.x & 0x3; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 32 + row_id * 8 + i] = X[i]; - } else { - X[i] = shmem[ntt_id * 32 + row_id * 8 + i]; - } - } - } - - DEVICE_INLINE void SharedData32Columns4_2(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0xf : threadIdx.x >> 2; - uint32_t column_id = (stride ? threadIdx.x >> 4 : threadIdx.x & 0x3) * 2; - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - if (store) { - shmem[ntt_id * 32 + i * 8 + column_id + j] = X[4 * j + i]; - } else { - X[4 * j + i] = shmem[ntt_id * 32 + i * 8 + column_id + j]; - } - } - } - } - - DEVICE_INLINE void SharedData32Rows4_2(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0xf : threadIdx.x >> 2; - uint32_t row_id = (stride ? threadIdx.x >> 4 : threadIdx.x & 0x3) * 2; - - UNROLL - for (uint32_t j = 0; j < 2; j++) { - UNROLL - for (uint32_t i = 0; i < 4; i++) { - if (store) { - shmem[ntt_id * 32 + row_id * 4 + 4 * j + i] = X[4 * j + i]; - } else { - X[4 * j + i] = shmem[ntt_id * 32 + row_id * 4 + 4 * j + i]; - } - } - } - } - - DEVICE_INLINE void SharedData16Columns8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x1f : threadIdx.x >> 1; - uint32_t column_id = stride ? threadIdx.x >> 5 : threadIdx.x & 0x1; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 16 + i * 2 + column_id] = X[i]; - } else { - X[i] = shmem[ntt_id * 16 + i * 2 + column_id]; - } - } - } - - DEVICE_INLINE void SharedData16Rows8(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x1f : threadIdx.x >> 1; - uint32_t row_id = stride ? threadIdx.x >> 5 : threadIdx.x & 0x1; - - UNROLL - for (uint32_t i = 0; i < 8; i++) { - if (store) { - shmem[ntt_id * 16 + row_id * 8 + i] = X[i]; - } else { - X[i] = shmem[ntt_id * 16 + row_id * 8 + i]; - } - } - } - - DEVICE_INLINE void SharedData16Columns2_4(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x1f : threadIdx.x >> 1; - uint32_t column_id = (stride ? threadIdx.x >> 5 : threadIdx.x & 0x1) * 4; - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - if (store) { - shmem[ntt_id * 16 + i * 8 + column_id + j] = X[2 * j + i]; - } else { - X[2 * j + i] = shmem[ntt_id * 16 + i * 8 + column_id + j]; - } - } - } - } - - DEVICE_INLINE void SharedData16Rows2_4(E* shmem, bool store, bool high_bits, bool stride) - { - uint32_t ntt_id = stride ? threadIdx.x & 0x1f : threadIdx.x >> 1; - uint32_t row_id = (stride ? threadIdx.x >> 5 : threadIdx.x & 0x1) * 4; - - UNROLL - for (uint32_t j = 0; j < 4; j++) { - UNROLL - for (uint32_t i = 0; i < 2; i++) { - if (store) { - shmem[ntt_id * 16 + row_id * 2 + 2 * j + i] = X[2 * j + i]; - } else { - X[2 * j + i] = shmem[ntt_id * 16 + row_id * 2 + 2 * j + i]; - } - } - } - } - - DEVICE_INLINE void twiddlesInternal() - { - UNROLL - for (int i = 1; i < 8; i++) { - X[i] = X[i] * WI[i - 1]; - } - } - - DEVICE_INLINE void twiddlesExternal() - { - UNROLL - for (int i = 0; i < 8; i++) { - X[i] = X[i] * WE[i]; - } - } -}; - -#endif \ No newline at end of file diff --git a/icicle/src/polynomials/cuda_backend/kernels.cuh b/icicle/src/polynomials/cuda_backend/kernels.cuh deleted file mode 100644 index 2d1032b69..000000000 --- a/icicle/src/polynomials/cuda_backend/kernels.cuh +++ /dev/null @@ -1,92 +0,0 @@ -#pragma once -#include "stdint.h" -#include "../../../src/vec_ops/vec_ops.cu" // TODO Yuval: avoid this - -namespace polynomials { - using namespace vec_ops; - - /*============================== add/sub ==============================*/ - template - __global__ void add_sub_kernel(const T* a_vec, const T* b_vec, int a_len, int b_len, bool add1_sub0, T* result) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid >= max(a_len, b_len)) return; - - T a = tid >= a_len ? T::zero() : a_vec[tid]; - T b = tid >= b_len ? T::zero() : b_vec[tid]; - result[tid] = add1_sub0 ? a + b : a - b; - } - - // Note: must be called with 1 block, 1 thread - template - __global__ void add_single_element_inplace(T* self, T v) - { - *self = *self + v; - } - - /*============================== degree ==============================*/ - template - __global__ void highest_non_zero_idx(const T* vec, int len, int64_t* idx) - { - *idx = -1; // zero polynomial is defined with degree -1 - for (int64_t i = len - 1; i >= 0; --i) { - if (vec[i] != T::zero()) { - *idx = i; - return; - } - } - } - - /*============================== evaluate ==============================*/ - // TODO Yuval: implement efficient reduction and support batch evaluation - template - __global__ void dummy_reduce(const T* arr, int size, T* output) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid > 0) return; - - *output = arr[0]; - for (int i = 1; i < size; ++i) { - *output = *output + arr[i]; - } - } - - template - __global__ void evaluate_polynomial_without_reduction(const T* x, const T* coeffs, int num_coeffs, T* tmp) - { - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < num_coeffs) { tmp[tid] = coeffs[tid] * T::pow(*x, tid); } - } - - /*============================== division ==============================*/ - template - __global__ void school_book_division_step(T* r, T* q, const T* b, int deg_r, int deg_b, T lc_b_inv) - { - // computing one step 'r = r-sb' (for 'a = q*b+r') where s is a monomial such that 'r-sb' removes the highest degree - // of r. - const int tid = blockIdx.x * blockDim.x + threadIdx.x; - int64_t monomial = deg_r - deg_b; // monomial=1 is 'x', monomial=2 is x^2 etc. - if (tid > deg_r) return; - - T lc_r = r[deg_r]; - T monomial_coeff = lc_r * lc_b_inv; // lc_r / lc_b - if (tid == 0) { - // adding monomial s to q (q=q+s) - q[monomial] = monomial_coeff; - } - - if (tid < monomial) return; - - T b_coeff = b[tid - monomial]; - r[tid] = r[tid] - monomial_coeff * b_coeff; - } - - /*============================== slice ==============================*/ - template - __global__ void slice_kernel(const T* in, T* out, int offset, int stride, int size) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < size) { out[tid] = in[offset + tid * stride]; } - } - -} // namespace polynomials \ No newline at end of file diff --git a/icicle/src/polynomials/cuda_backend/polynomial_cuda_backend.cu b/icicle/src/polynomials/cuda_backend/polynomial_cuda_backend.cu deleted file mode 100644 index 1b9d5d147..000000000 --- a/icicle/src/polynomials/cuda_backend/polynomial_cuda_backend.cu +++ /dev/null @@ -1,1047 +0,0 @@ - -#include "polynomials/polynomials.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" - -#include "gpu-utils/device_context.cuh" -#include "cuda_runtime.h" -#include "ntt/ntt.cuh" -#include "kernels.cuh" - -using device_context::DeviceContext; - -namespace polynomials { - - static uint64_t ceil_to_power_of_two(uint64_t x) { return 1ULL << uint64_t(ceil(log2(x))); } - /*============================== Polynomial CUDA-context ==============================*/ - - // checking whether a pointer is on host or device and asserts device matches the polynmoial device - static bool is_host_ptr(const void* p, int device_id) - { - // Note: device memory can me managed or not. Host memory can be registered or not. No distinction here. - cudaPointerAttributes attributes; - CHK_STICKY(cudaPointerGetAttributes(&attributes, p)); - const bool is_on_host = attributes.type == cudaMemoryTypeHost || - attributes.type == cudaMemoryTypeUnregistered; // unregistered is host memory - const bool is_on_cur_device = !is_on_host && attributes.device == device_id; - const bool is_valid_ptr = is_on_host || is_on_cur_device; - if (!is_valid_ptr) { THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Invalid ptr for polynomial"); } - - return is_on_host; - } - - template - class CUDAPolynomialContext : public IPolynomialContext - { - typedef IPolynomialContext PolyContext; - using typename IPolynomialContext::State; - using IPolynomialContext::ElementSize; - - protected: - State m_state = State::Invalid; // Current state of the polynomial context. - uint64_t m_nof_elements = 0; // Number of elements managed by the context. - - public: - const DeviceContext& m_device_context; - - CUDAPolynomialContext(const DeviceContext& dev_context) : m_device_context{dev_context} - { - m_integrity_counter = std::make_shared(0); - } - ~CUDAPolynomialContext() { release(); } - - void allocate(uint64_t nof_elements, State init_state, bool is_memset_zeros) override - { - const bool is_already_allocated = this->m_nof_elements >= nof_elements; - this->set_state(init_state); - - if (is_already_allocated) { - // zero the extra elements, if exist - memset_zeros(this->m_storage, nof_elements, this->m_nof_elements); - return; - } - - release(); // in case allocated mem is too small and need to reallocate - this->m_nof_elements = allocate_mem(nof_elements, &this->m_storage, is_memset_zeros); - modified(); - } - - void memset_zeros(void* storage, uint64_t element_start_idx, uint64_t element_end_idx) - { - const uint64_t size = (element_end_idx - element_start_idx) * ElementSize; - if (0 == size) { return; } - - modified(); - - const auto offset = (void*)((uint64_t)storage + element_start_idx * ElementSize); - CHK_STICKY(cudaMemsetAsync(offset, 0, size, m_device_context.stream)); - } - - uint64_t allocate_mem(uint64_t nof_elements, void** storage /*OUT*/, bool is_memset_zeros) - { - const uint64_t nof_elements_nearset_power_of_two = ceil_to_power_of_two(nof_elements); - const uint64_t mem_size = nof_elements_nearset_power_of_two * ElementSize; - - CHK_STICKY(cudaMallocAsync(storage, mem_size, m_device_context.stream)); - - if (is_memset_zeros) { - memset_zeros(*storage, 0, nof_elements_nearset_power_of_two); - } else { - // if allocating more memory than requested, memset only the pad area to avoid higher invalid coefficients - memset_zeros(*storage, nof_elements, nof_elements_nearset_power_of_two); - } - - return nof_elements_nearset_power_of_two; - } - - void set_storage(void* storage, uint64_t nof_elements) - { - release(); - m_storage = storage; - this->m_nof_elements = nof_elements; - - modified(); - } - - // Note: this is protected and only backend can call - void* get_storage_mutable() override - { - // since giving access to internal memory, cannot know if modified or not - // backend should not take it mutable if not mutating - modified(); - return m_storage; - } - const void* get_storage_immutable() override { return m_storage; } - - void extend_mem_and_pad(uint64_t nof_elements) - { - void* new_storage = nullptr; - const uint64_t new_nof_elements = allocate_mem(nof_elements, &new_storage, true /*=memset zeros*/); - const uint64_t old_mem_size = this->m_nof_elements * ElementSize; - - CHK_STICKY( - cudaMemcpyAsync(new_storage, m_storage, old_mem_size, cudaMemcpyDeviceToDevice, m_device_context.stream)); - - set_storage(new_storage, new_nof_elements); - } - - void release() override - { - if (m_storage != nullptr) { CHK_STICKY(cudaFreeAsync(m_storage, m_device_context.stream)); } - - m_storage = nullptr; - this->m_nof_elements = 0; - - modified(); - } - - State get_state() const override { return m_state; } - void set_state(State state) { m_state = state; } - uint64_t get_nof_elements() const override { return m_nof_elements; } - - void from_coefficients(uint64_t nof_coefficients, const C* coefficients) override - { - const bool is_memset_zeros = coefficients == nullptr; - allocate(nof_coefficients, State::Coefficients, is_memset_zeros); - if (coefficients) { - const bool is_ptr_on_host = is_host_ptr(coefficients, m_device_context.device_id); - - CHK_STICKY(cudaMemcpyAsync( - m_storage, coefficients, nof_coefficients * sizeof(C), - is_ptr_on_host ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice, m_device_context.stream)); - CHK_STICKY( - cudaStreamSynchronize(m_device_context.stream)); // protect against coefficients being released too soon - } - } - - void from_rou_evaluations(uint64_t nof_evaluations, const I* evaluations) override - { - const bool is_memset_zeros = evaluations == nullptr; - allocate(nof_evaluations, State::EvaluationsOnRou_Natural, is_memset_zeros); - if (evaluations) { - const bool is_ptr_on_host = is_host_ptr(evaluations, m_device_context.device_id); - - CHK_STICKY(cudaMemcpyAsync( - m_storage, evaluations, nof_evaluations * sizeof(C), - is_ptr_on_host ? cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice, m_device_context.stream)); - CHK_STICKY( - cudaStreamSynchronize(m_device_context.stream)); // protect against evaluations being released too soon - } - } - - void clone(IPolynomialContext& from) override - { - switch (from.get_state()) { - case State::Coefficients: { - auto [coeffs, N_coeffs] = from.get_coefficients(); - from_coefficients(N_coeffs, coeffs); - } break; - case State::EvaluationsOnRou_Natural: { - auto [evals, N_evals] = from.get_rou_evaluations(); - from_rou_evaluations(N_evals, evals); - } break; - default: - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "clone() from non implemented state"); - } - - this->set_state(from.get_state()); // to handle both reversed evaluations case - } - - std::pair get_coefficients() override - { - transform_to_coefficients(); - return std::make_pair(static_cast(m_storage), this->m_nof_elements); - } - - std::tuple, uint64_t, uint64_t> get_coefficients_view() override - { - auto [coeffs, N] = get_coefficients(); - // when reading the pointer, if the counter was modified, the pointer is invalid - IntegrityPointer integrity_pointer(coeffs, m_integrity_counter, *m_integrity_counter); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); - return {std::move(integrity_pointer), N, m_device_context.device_id}; - } - - std::pair get_rou_evaluations() override - { - const bool is_reversed = this->m_state == State::EvaluationsOnRou_Reversed; - transform_to_evaluations(0, is_reversed); - return std::make_pair(static_cast(m_storage), this->m_nof_elements); - } - - void transform_to_coefficients(uint64_t nof_coefficients = 0) override - { - // cannot really get more coefficients but sometimes want to pad for NTT. In that case - // nof_coefficients>m_nof_elements - nof_coefficients = (nof_coefficients == 0) ? this->m_nof_elements : ceil_to_power_of_two(nof_coefficients); - const bool is_same_nof_coefficients = this->m_nof_elements == nof_coefficients; - const bool is_already_in_state = this->m_state == State::Coefficients && is_same_nof_coefficients; - if (is_already_in_state) { return; } - - if (nof_coefficients < this->m_nof_elements) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "polynomial shrinking not supported. Probably encountered a bug"); - } - - modified(); - - const bool is_already_in_coeffs = this->m_state == State::Coefficients; - // case 1: already in coefficients. Need to allocate larger memory and zero pad - if (is_already_in_coeffs) { - extend_mem_and_pad(nof_coefficients); - return; - } - - // case 2: transform from evaluations. May need to allocate larger memory - I* evals = static_cast(m_storage); - C* coeffs = static_cast(m_storage); - const bool is_allocate_new_mem = nof_coefficients > this->m_nof_elements; - if (is_allocate_new_mem) { - void* new_mem = nullptr; - nof_coefficients = allocate_mem(nof_coefficients, &new_mem, true /*=memset zeros*/); - coeffs = static_cast(new_mem); - } - - // transform from evaluations to coefficients - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - - ntt_config.ordering = - (this->m_state == State::EvaluationsOnRou_Natural) ? ntt::Ordering::kNN : ntt::Ordering::kRN; - // Note: it is important to do the NTT with old size because padding in evaluations form is computing another - // (higher order) polynomial - CHK_STICKY(ntt::ntt(evals, this->m_nof_elements, ntt::NTTDir::kInverse, ntt_config, coeffs)); - this->set_state(State::Coefficients); - - if (is_allocate_new_mem) { set_storage(coeffs, nof_coefficients); } // release old memory and use new - } - - void transform_to_evaluations(uint64_t nof_evaluations = 0, bool is_reversed = false) override - { - // TODO Yuval: can maybe optimize this - nof_evaluations = (nof_evaluations == 0) ? this->m_nof_elements : ceil_to_power_of_two(nof_evaluations); - const bool is_same_nof_evaluations = nof_evaluations == this->m_nof_elements; - const bool is_same_order = is_reversed && this->m_state == State::EvaluationsOnRou_Reversed || - (!is_reversed && this->m_state == State::EvaluationsOnRou_Natural); - const bool is_already_in_state = is_same_nof_evaluations && is_same_order; - if (is_already_in_state) { return; } - - if (nof_evaluations < this->m_nof_elements) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "polynomial shrinking not supported. Probably encountered a bug"); - } - - modified(); - - // TODO Yuval: evaluations->evaluations with different ordering can be implemented via inplace reorder more - // efficiently than it is now - - // There are 3 cases: - // (1) coefficients to evaluations - // (1a) same size -> NTT (NR or NN) - // (1b) different_size -> alloc new mem, copy coeffs and NTT inplace - // (2) evaluations to evaluations (interpolation) - // transform to coefficients, extend memory, then NTT back to evals (NR or NN) - - const bool is_eval_to_eval = this->m_state != State::Coefficients; - // interpolating more points requires going back to coefficients first. Note that it muse be done with the - // original size. INTT after padding computes a higher degree polynomial - if (is_eval_to_eval) { transform_to_coefficients(); } - - // reaching this point means polynomial is in coefficient form - const bool is_allocate_new_mem = nof_evaluations > this->m_nof_elements; - // allocate more memory and copy+pad - if (is_allocate_new_mem) { extend_mem_and_pad(nof_evaluations); } - - C* coeffs = static_cast(m_storage); - I* evals = static_cast(m_storage); - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - // already copied the coefficients with padding. Now computing evaluations. - ntt_config.ordering = is_reversed ? ntt::Ordering::kNR : ntt::Ordering::kNN; - CHK_STICKY(ntt::ntt(coeffs, nof_evaluations, ntt::NTTDir::kForward, ntt_config, evals)); - - this->set_state(is_reversed ? State::EvaluationsOnRou_Reversed : State::EvaluationsOnRou_Natural); - } - - void print(std::ostream& os) override - { - if (this->get_state() == State::Coefficients) { - print_coeffs(os); - } else { - print_evals(os); - } - } - - void print_coeffs(std::ostream& os) - { - transform_to_coefficients(); - auto host_coeffs = std::make_unique(this->m_nof_elements); - // using stream since previous ops may still be in progress. Sync stream before reading CPU mem - CHK_STICKY(cudaMemcpyAsync( - host_coeffs.get(), m_storage, this->m_nof_elements * sizeof(C), cudaMemcpyDeviceToHost, - m_device_context.stream)); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); - - os << "(id=" << PolyContext::m_id << ")["; - for (size_t i = 0; i < this->m_nof_elements; ++i) { - os << host_coeffs[i]; - if (i < this->m_nof_elements - 1) { os << ", "; } - } - os << "] (state=coefficients)" << std::endl; - } - - void print_evals(std::ostream& os) - { - transform_to_evaluations(); - auto host_evals = std::make_unique(this->m_nof_elements); - // using stream since previous ops may still be in progress. Sync stream before reading CPU mem - CHK_STICKY(cudaMemcpyAsync( - host_evals.get(), m_storage, this->m_nof_elements * sizeof(I), cudaMemcpyDeviceToHost, - m_device_context.stream)); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); - - os << "(id=" << PolyContext::m_id << ")["; - for (size_t i = 0; i < this->m_nof_elements; ++i) { - os << host_evals[i]; - if (i < this->m_nof_elements - 1) { os << ", "; } - } - - if (this->get_state() == State::EvaluationsOnRou_Reversed) { - os << "] (state=rou evaluations Reversed)" << std::endl; - } else { - os << "] (state=rou evaluations )" << std::endl; - } - } - - private: - // Members - void* m_storage = nullptr; - std::shared_ptr m_integrity_counter; // used to implement integrity of coefficients pointer - - void modified() { (*m_integrity_counter)++; } - }; - - /*============================== Polynomial CUDA-backend ==============================*/ - - template - class CUDAPolynomialBackend : public IPolynomialBackend - { - typedef std::shared_ptr> PolyContext; - typedef typename IPolynomialContext::State State; - - int64_t* d_degree = nullptr; // used to avoid alloc/release every time - - public: - const DeviceContext& m_device_context; - CUDAPolynomialBackend(const DeviceContext& dev_context) : m_device_context{dev_context} - { - CHK_STICKY(cudaMallocAsync(&d_degree, sizeof(int64_t), m_device_context.stream)); - } - ~CUDAPolynomialBackend() { CHK_STICKY(cudaFreeAsync(d_degree, m_device_context.stream)); } - - void from_coefficients(PolyContext p, uint64_t nof_coefficients, const C* coefficients) override - { - p->from_coefficients(nof_coefficients, coefficients); - } - - void from_rou_evaluations(PolyContext p, uint64_t nof_evaluations, const I* evaluations) override - { - p->from_rou_evaluations(nof_evaluations, evaluations); - } - - void clone(PolyContext out, PolyContext in) override { out->clone(*in); } - - template - T* get_context_storage_mutable(PolyContext p) - { - return static_cast(IPolynomialBackend::get_context_storage_mutable(p)); - } - - template - const T* get_context_storage_immutable(PolyContext& p) - { - return static_cast(IPolynomialBackend::get_context_storage_immutable(p)); - } - - void slice(PolyContext out, PolyContext in, uint64_t offset, uint64_t stride, uint64_t size) override - { - assert_device_compatability(out, in); - auto [in_coeffs, in_size] = in->get_coefficients(); - // size=0 means take as much as elements as there are to take - uint64_t out_size = (size > 0) ? size : (1 + (in_size - 1 - offset) / stride); - - out->allocate(out_size, State::Coefficients, false /*=memset zeros*/); - auto out_coeffs = get_context_storage_mutable(out); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (out_size + NOF_THREADS - 1) / NOF_THREADS; - slice_kernel<<>>( - in_coeffs, out_coeffs, offset, stride, out_size); - - CHK_LAST(); - } - - void add_sub(PolyContext& res, PolyContext a, PolyContext b, bool add1_sub0) - { - assert_device_compatability(a, b); - assert_device_compatability(a, res); - - // add/sub can be done in both coefficients or evaluations, but operands must be in the same state. - // For evaluations, same state also means same number of evaluations (and on same domain). - // If not same state, compute in coefficients since computing in evaluations may require to interpolate a large - // size. Consider a+b where a is degree 128 and b degree 4. In coefficients b has 4 elements but in evaluations - // need 128. - const bool is_same_size = a->get_nof_elements() == b->get_nof_elements(); - const bool is_same_state = a->get_state() == b->get_state(); - const auto output_state = (is_same_size && is_same_state) ? a->get_state() : State::Coefficients; - const auto output_size = max(a->get_nof_elements(), b->get_nof_elements()); - - if (State::Coefficients == output_state) { - a->transform_to_coefficients(); - b->transform_to_coefficients(); - } - const auto a_mem_p = get_context_storage_immutable(a); - const auto b_mem_p = get_context_storage_immutable(b); - - res->allocate(output_size, output_state); - auto res_mem_p = get_context_storage_mutable(res); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (output_size + NOF_THREADS - 1) / NOF_THREADS; - add_sub_kernel<<>>( - a_mem_p, b_mem_p, a->get_nof_elements(), b->get_nof_elements(), add1_sub0, res_mem_p); - - CHK_LAST(); - } - - void add(PolyContext& res, PolyContext a, PolyContext b) override { add_sub(res, a, b, true /*=add*/); } - void subtract(PolyContext res, PolyContext a, PolyContext b) override { add_sub(res, a, b, false /*=sub*/); } - - void multiply(PolyContext c, PolyContext a, PolyContext b) override - { - assert_device_compatability(a, b); - assert_device_compatability(a, c); - - const bool is_a_scalar = a->get_nof_elements() == 1; - const bool is_b_scalar = b->get_nof_elements() == 1; - - // TODO: can add kernel that takes the scalar as device memory - if (is_a_scalar) { - return multiply(c, b, get_coeff(a, 0)); - } else if (is_b_scalar) { - return multiply(c, a, get_coeff(b, 0)); - } - - const bool is_multiply_with_cosets = true; // TODO Yuval: check when faster to do so. - if (is_multiply_with_cosets) { return multiply_with_cosets(c, a, b); } - return multiply_with_padding(c, a, b); - } - - void multiply(PolyContext out, PolyContext p, D scalar) override - { - assert_device_compatability(out, p); - - // element wise multiplication is similar both in coefficients and evaluations (regardless of order too) - const auto state = p->get_state(); - const auto N = p->get_nof_elements(); - - auto p_elements_p = - state == State::Coefficients ? get_context_storage_immutable(p) : get_context_storage_immutable(p); - - out->allocate(N, state, false /*=memset zeros*/); - auto out_evals_p = - state == State::Coefficients ? get_context_storage_mutable(out) : get_context_storage_mutable(out); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (N + NOF_THREADS - 1) / NOF_THREADS; - mul_scalar_kernel<<>>(p_elements_p, scalar, N, out_evals_p); - - CHK_LAST(); - } - - void multiply_with_padding(PolyContext c, PolyContext a, PolyContext b) - { - // TODO Yuval: by using the degree I can optimize the memory size and avoid redundant computations too - const uint64_t a_N_orig = a->get_nof_elements(); - const uint64_t b_N_orig = b->get_nof_elements(); - const uint64_t N = max(a_N_orig, b_N_orig); - const uint64_t c_N = 2 * N; - - // (1) transform a,b to 2N evaluations - a->transform_to_evaluations(c_N, true /*=reversed*/); - b->transform_to_evaluations(c_N, true /*=reversed*/); - auto [a_evals_p, a_N] = a->get_rou_evaluations(); - auto [b_evals_p, b_N] = b->get_rou_evaluations(); - - // (2) allocate c (c=a*b) and compute element-wise multiplication on evaluations - c->allocate(c_N, State::EvaluationsOnRou_Reversed, false /*=memset zeros*/); - auto c_evals_p = get_context_storage_mutable(c); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (c_N + NOF_THREADS - 1) / NOF_THREADS; - mul_kernel<<>>(a_evals_p, b_evals_p, c_N, c_evals_p); - - CHK_LAST(); - } - - void multiply_with_cosets(PolyContext c, PolyContext a, PolyContext b) - { - const uint64_t a_N = a->get_nof_elements(); - const uint64_t b_N = b->get_nof_elements(); - const uint64_t N = max(a_N, b_N); - - // (1) transform a,b to coefficients such that both have N coefficients - a->transform_to_coefficients(N); - b->transform_to_coefficients(N); - auto [a_coeff_p, _] = a->get_coefficients(); - auto [b_coeff_p, __] = b->get_coefficients(); - // (2) allocate c (c=a*b) - const uint64_t c_N = 2 * N; - c->allocate(c_N, State::EvaluationsOnRou_Reversed, false /*=memset zeros*/); - auto c_evals_low_p = get_context_storage_mutable(c); - I* c_evals_high_p = c_evals_low_p + N; - - // (3) compute NTT of a,b on coset and write to c - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - ntt_config.ordering = ntt::Ordering::kNR; - ntt_config.coset_gen = ntt::get_root_of_unity_from_domain((uint64_t)log2(c_N), ntt_config.ctx); - - CHK_STICKY(ntt::ntt(a_coeff_p, N, ntt::NTTDir::kForward, ntt_config, c_evals_low_p)); // a_H1 - CHK_STICKY(ntt::ntt(b_coeff_p, N, ntt::NTTDir::kForward, ntt_config, c_evals_high_p)); // b_H1 - - // (4) compute a_H1 * b_H1 inplace - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (N + NOF_THREADS - 1) / NOF_THREADS; - mul_kernel<<>>( - c_evals_low_p, c_evals_high_p, N, c_evals_high_p); - // (5) transform a,b to evaluations - a->transform_to_evaluations(N, true /*=reversed*/); - b->transform_to_evaluations(N, true /*=reversed*/); - auto [a_evals_p, a_nof_evals] = a->get_rou_evaluations(); - auto [b_evals_p, b_nof_evals] = b->get_rou_evaluations(); - - // (6) compute a_H0 * b_H0 - mul_kernel<<>>(a_evals_p, b_evals_p, N, c_evals_low_p); - - CHK_LAST(); - } - - void divide(PolyContext Q /*OUT*/, PolyContext R /*OUT*/, PolyContext a, PolyContext b) override - { - assert_device_compatability(a, b); - assert_device_compatability(a, Q); - assert_device_compatability(a, R); - - auto [a_coeffs, a_N] = a->get_coefficients(); - auto [b_coeffs, b_N] = b->get_coefficients(); - - const int64_t deg_a = degree(a); - const int64_t deg_b = degree(b); - if (deg_b < 0) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "Polynomial division (CUDA backend): divide by zeropolynomial "); - } - - // init: Q=0, R=a - Q->allocate(deg_a - deg_b + 1, State::Coefficients, true /*=memset zeros*/); - auto Q_coeffs = get_context_storage_mutable(Q); - - // TODO Yuval: Can do better in terms of memory allocation? deg(R) <= deg(b) by definition but it starts as - R->allocate(a_N, State::Coefficients, false /*=memset_zeros*/); - auto R_coeffs = get_context_storage_mutable(R); - CHK_STICKY( - cudaMemcpyAsync(R_coeffs, a_coeffs, a_N * sizeof(C), cudaMemcpyDeviceToDevice, m_device_context.stream)); - - const C& lc_b_inv = C::inverse(get_coeff(b, deg_b)); // largest coeff of b - - int64_t deg_r = deg_a; - while (deg_r >= deg_b) { - // each iteration is removing the largest monomial in r until deg(r)>>( - R_coeffs, Q_coeffs, b_coeffs, deg_r, deg_b, lc_b_inv); - - // faster than degree(R) based on the fact that degree is decreasing - deg_r = degree_internal(R, deg_r + 1 /*size of R*/); - } - - CHK_LAST(); - } - - void quotient(PolyContext Q, PolyContext op_a, PolyContext op_b) override - { - // TODO: can implement more efficiently? - auto R = std::make_shared>(m_device_context); - divide(Q, R, op_a, op_b); - } - - void remainder(PolyContext R, PolyContext op_a, PolyContext op_b) override - { - // TODO: can implement more efficiently? - auto Q = std::make_shared>(m_device_context); - divide(Q, R, op_a, op_b); - } - - void divide_by_vanishing_polynomial(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) override - { - assert_device_compatability(numerator, out); - - // vanishing polynomial of degree N is the polynomial V(x) such that V(r)=0 for r Nth root-of-unity. - // For example for N=4 it vanishes on the group [1,W,W^2,W^3] where W is the 4th root of unity. In that - // case V(x)=(x-1)(x-w)(x-w^2)(x-w^3). It can be easily shown that V(x)=x^N-1. This holds since x^N=1 on this - // domain (since x is the Nth root of unity). - - // Note that we always represent polynomials with N elements for N a power of two. This is required for NTTs. - // In addition we consider deg(P) to be this number of elements N even though the real degree may be lower. for - // example 1+x-2x^2 is degree 2 but we store 4 elements and consider it degree 3. - - // when dividing a polynomial P(x)/V(x) (The vanishing polynomial) the output is of degree deg(P)-deg(V). There - // are three cases where V(x) divides P(x) (this is assumed since otherwise the output polynomial does not - // exist!): - // (1) deg(P)=2*deg(V): in that case deg(P/V)=deg(V)=N. This is an efficient case since on a domain of size N, the - // vanishing polynomial evaluates to a constant value. - // (2) deg(P)=deg(V)=N: in that case the output is a degree 0 polynomial. - // polynomial (i.e. scalar). (3) general case: deg(P)>2*deg(V): in that case deg(P) is a least 4*deg(V) since N is - // a power of two. This means that deg(P/V)=deg(P). For example deg(P)=16, deg(V)=4 --> deg(P/V)=12 ceiled to 16. - - // When computing we want to divide P(x)'s evals by V(x)'s evals. Since V(x)=0 on this domain we have to compute - // on a coset. - // for case (3) we must evaluate V(x) on deg(P) domain size and compute elementwise division on a coset. - // case (1) is more efficient because we need N evaluations of V(x) on a coset. Note that V(x)=constant on a coset - // of rou. This is because V(wu)=(wu)^N-1=W^N*u^N-1 = 1*u^N-1 (as w^N=1 for w Nth root of unity). case (2) can be - // computed like case (1). - - const bool is_case_2N = numerator->get_nof_elements() == 2 * vanishing_poly_degree; - const bool is_case_N = numerator->get_nof_elements() == vanishing_poly_degree; - if (is_case_2N) { - divide_by_vanishing_case_2N(out, numerator, vanishing_poly_degree); - } else if (is_case_N) { - divide_by_vanishing_case_N(out, numerator, vanishing_poly_degree); - } else { - divide_by_vanishing_general_case(out, numerator, vanishing_poly_degree); - } - } - - void divide_by_vanishing_general_case(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) - { - // General case: P(x)/V(x) where v is of degree N and p of any degree>N - - // (1) allocate vanishing polynomial in coefficients form - // TODO Yuval: maybe instead of taking numerator memory and modiyfing it diretcly add a state for evaluations - // on coset of rou. In that case I can remain in this state and also won't need to access input memory - // directly - numerator->transform_to_coefficients(); - auto numerator_coeffs = get_context_storage_mutable(numerator); - const auto N = numerator->get_nof_elements(); - if (vanishing_poly_degree > N) { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "divide_by_vanishing_polynomial(): degree is too large"); - } - out->allocate(N, State::Coefficients, true /*=set zeros*/); - add_monomial_inplace(out, C::zero() - C::one(), 0); //-1 - add_monomial_inplace(out, C::one(), vanishing_poly_degree); //+x^n - - // (2) NTT on coset. Note that NTT on ROU evaluates to zeros for vanihsing polynomials by definition. - // Therefore evaluation on coset is required to compute non-zero evaluations, which make element-wise division - // possible - auto out_coeffs = get_context_storage_mutable(out); - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - ntt_config.ordering = ntt::Ordering::kNM; - ntt_config.coset_gen = ntt::get_root_of_unity_from_domain((uint64_t)log2(2 * N), ntt_config.ctx); - - CHK_STICKY(ntt::ntt(out_coeffs, N, ntt::NTTDir::kForward, ntt_config, out_coeffs)); - CHK_STICKY(ntt::ntt(numerator_coeffs, N, ntt::NTTDir::kForward, ntt_config, numerator_coeffs)); - - // (3) element wise division - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (N + NOF_THREADS - 1) / NOF_THREADS; - div_element_wise_kernel<<>>( - numerator_coeffs, out_coeffs, N, out_coeffs); - - // (4) INTT back both numerator and out - ntt_config.ordering = ntt::Ordering::kMN; - CHK_STICKY(ntt::ntt(out_coeffs, N, ntt::NTTDir::kInverse, ntt_config, out_coeffs)); - CHK_STICKY(ntt::ntt(numerator_coeffs, N, ntt::NTTDir::kInverse, ntt_config, numerator_coeffs)); - } - - void divide_by_vanishing_case_2N(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) - { - // in that special case the numertaor has 2N elements and output will be N elements - if (numerator->get_nof_elements() != 2 * vanishing_poly_degree) { - THROW_ICICLE_ERR(IcicleError_t::UndefinedError, "invalid input size. Expecting numerator to be of size 2N"); - } - - // In the case where deg(P)=2N, I can transform numerator to Reversed-evals -> The second half is - // a reversed-coset of size N with coset-gen the 2N-th root of unity. - const int N = vanishing_poly_degree; - numerator->transform_to_evaluations(2 * N, true /*=reversed*/); - // allocate output in coeffs because it will be calculated on a coset but I don't have such a state so will have - // to INTT back to coeffs - auto numerator_evals_reversed_p = get_context_storage_immutable(numerator); - out->allocate(N, State::Coefficients, false /*=set zeros*/); - auto out_evals_reversed_p = get_context_storage_mutable(out); - - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.coset_gen = ntt::get_root_of_unity_from_domain((uint64_t)log2(2 * N), ntt_config.ctx); - // compute inv(u^N-1); - D v_coset_eval = D::inverse(D::pow(ntt_config.coset_gen, N) - D::one()); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (N + NOF_THREADS - 1) / NOF_THREADS; - mul_scalar_kernel<<>>( - numerator_evals_reversed_p + N /*second half is the reversed coset*/, v_coset_eval, N, out_evals_reversed_p); - - // INTT back from reversed evals on coset to coeffs - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - ntt_config.ordering = ntt::Ordering::kRN; - ntt::ntt(out_evals_reversed_p, N, ntt::NTTDir::kInverse, ntt_config, out_evals_reversed_p); - - CHK_LAST(); - } - - void divide_by_vanishing_case_N(PolyContext out, PolyContext numerator, uint64_t vanishing_poly_degree) - { - // in that special case the numertaor has N elements and output will be N elements - if (numerator->get_nof_elements() != vanishing_poly_degree) { - THROW_ICICLE_ERR(IcicleError_t::UndefinedError, "invalid input size. Expecting numerator to be of size N"); - } - - const int N = vanishing_poly_degree; - numerator->transform_to_coefficients(N); - auto numerator_evals_reversed_p = get_context_storage_immutable(numerator); - out->allocate(N, State::Coefficients, false /*=set zeros*/); - auto out_evals_reversed_p = get_context_storage_mutable(out); - - // (1) NTT numerator to coset evals (directly to out) - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.coset_gen = ntt::get_root_of_unity_from_domain((uint64_t)log2(2 * N), ntt_config.ctx); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - ntt_config.ordering = ntt::Ordering::kNM; - ntt::ntt(numerator_evals_reversed_p, N, ntt::NTTDir::kForward, ntt_config, out_evals_reversed_p); - - // (2) divide by constant value (that V(x) evaluates to on the coset) - D v_coset_eval = D::inverse(D::pow(ntt_config.coset_gen, N) - D::one()); - - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (N + NOF_THREADS - 1) / NOF_THREADS; - mul_scalar_kernel<<>>( - out_evals_reversed_p, v_coset_eval, N, out_evals_reversed_p); - - // (3) INTT back from coset to coeffs - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - ntt_config.ordering = ntt::Ordering::kMN; - ntt::ntt(out_evals_reversed_p, N, ntt::NTTDir::kInverse, ntt_config, out_evals_reversed_p); - - CHK_LAST(); - } - - // arithmetic with monomials - void add_monomial_inplace(PolyContext& poly, C monomial_coeff, uint64_t monomial) override - { - const uint64_t new_nof_elements = max(poly->get_nof_elements(), monomial + 1); - poly->transform_to_coefficients(new_nof_elements); - auto coeffs = get_context_storage_mutable(poly); - add_single_element_inplace<<<1, 1, 0, m_device_context.stream>>>(coeffs + monomial, monomial_coeff); - - CHK_LAST(); - } - - void sub_monomial_inplace(PolyContext& poly, C monomial_coeff, uint64_t monomial) override - { - add_monomial_inplace(poly, C::zero() - monomial_coeff, monomial); - } - - int64_t degree(PolyContext p) override { return degree_internal(p, p->get_nof_elements()); } - - // search degree starting from len, searching down (towards coeff0) - int64_t degree_internal(PolyContext p, uint64_t len) - { - // TODO: parallelize kernel? Note that typically the largest coefficient is expected in the higher half since - // memory is allocate based on #coefficients - - auto [coeff, _] = p->get_coefficients(); - - int64_t h_degree; - highest_non_zero_idx<<<1, 1, 0, m_device_context.stream>>>(coeff, len, d_degree); - CHK_STICKY( - cudaMemcpyAsync(&h_degree, d_degree, sizeof(int64_t), cudaMemcpyDeviceToHost, m_device_context.stream)); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); // sync to make sure return value is copied to host - - return h_degree; - } - - public: - void evaluate(PolyContext p, const D* x, I* eval) override - { - // TODO Yuval: maybe use Horner's rule and just evaluate each domain point per thread. Alternatively Need to - // reduce in parallel. - - auto [coeff, nof_coeff] = p->get_coefficients(); - - const bool is_x_on_host = is_host_ptr(x, m_device_context.device_id); - const bool is_eval_on_host = is_host_ptr(eval, m_device_context.device_id); - - const D* d_x = x; - D* allocated_x = nullptr; - if (is_x_on_host) { - CHK_STICKY(cudaMallocAsync(&allocated_x, sizeof(I), m_device_context.stream)); - CHK_STICKY(cudaMemcpyAsync(allocated_x, x, sizeof(I), cudaMemcpyHostToDevice, m_device_context.stream)); - d_x = allocated_x; - } - I* d_eval = eval; - if (is_eval_on_host) { CHK_STICKY(cudaMallocAsync(&d_eval, sizeof(I), m_device_context.stream)); } - - // TODO Yuval: other methods can avoid this allocation. Also for eval_on_domain() no need to reallocate every time - I* d_tmp = nullptr; - CHK_STICKY(cudaMallocAsync(&d_tmp, sizeof(I) * nof_coeff, m_device_context.stream)); - const int NOF_THREADS = 32; - const int NOF_BLOCKS = (nof_coeff + NOF_THREADS - 1) / NOF_THREADS; - evaluate_polynomial_without_reduction<<>>( - d_x, coeff, nof_coeff, d_tmp); // TODO Yuval: parallelize kernel - dummy_reduce<<<1, 1, 0, m_device_context.stream>>>(d_tmp, nof_coeff, d_eval); - - if (is_eval_on_host) { - CHK_STICKY(cudaMemcpyAsync(eval, d_eval, sizeof(I), cudaMemcpyDeviceToHost, m_device_context.stream)); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); // sync to make sure return value is copied to host - CHK_STICKY(cudaFreeAsync(d_eval, m_device_context.stream)); - } - if (allocated_x) { CHK_STICKY(cudaFreeAsync(allocated_x, m_device_context.stream)); } - CHK_STICKY(cudaFreeAsync(d_tmp, m_device_context.stream)); - } - - void evaluate_on_domain(PolyContext p, const D* domain, uint64_t size, I* evaluations /*OUT*/) override - { - // TODO Yuval: implement more efficiently ?? - for (uint64_t i = 0; i < size; ++i) { - evaluate(p, &domain[i], &evaluations[i]); - } - } - - void evaluate_on_rou_domain(PolyContext p, uint64_t domain_log_size, I* evals /*OUT*/) override - { - const uint64_t poly_size = p->get_nof_elements(); - const uint64_t domain_size = 1 << domain_log_size; - const bool is_evals_on_host = is_host_ptr(evals, m_device_context.device_id); - - I* d_evals = evals; - // if evals on host, allocate CUDA memory - if (is_evals_on_host) { CHK_STICKY(cudaMallocAsync(&d_evals, domain_size * sizeof(I), m_device_context.stream)); } - - // If domain size is smaller the polynomial size -> transform to evals and copy the evals with stride. - // Else, if in coeffs copy coeffs to evals mem and NTT inplace to compute the evals, else INTT to d_evals and back - // inplace to larger domain - const bool is_domain_size_smaller_than_poly_size = domain_size <= poly_size; - if (is_domain_size_smaller_than_poly_size) { - // TODO Yuval: in reversed evals, can reverse the first 'domain_size' elements to d_evals instead of - // transforming back to evals. - p->transform_to_evaluations(); - const auto stride = poly_size / domain_size; - const int NOF_THREADS = 128; - const int NOF_BLOCKS = (domain_size + NOF_THREADS - 1) / NOF_THREADS; - slice_kernel<<>>( - get_context_storage_immutable(p), d_evals, 0 /*offset*/, stride, domain_size); - } else { - CHK_STICKY(cudaMemset(d_evals, 0, domain_size * sizeof(I))); - auto ntt_config = ntt::default_ntt_config(m_device_context); - ntt_config.are_inputs_on_device = true; - ntt_config.are_outputs_on_device = true; - ntt_config.is_async = true; - // TODO Yuval: in evals I can NTT directly to d_evals without changing my state - switch (p->get_state()) { - case State::Coefficients: { - // copy to evals memory and inplace NTT of domain size - CHK_STICKY( - cudaMemcpy(d_evals, get_context_storage_immutable(p), poly_size * sizeof(I), cudaMemcpyDeviceToDevice)); - ntt_config.ordering = ntt::Ordering::kNN; - ntt::ntt(d_evals, domain_size, ntt::NTTDir::kForward, ntt_config, d_evals); - } break; - case State::EvaluationsOnRou_Natural: - case State::EvaluationsOnRou_Reversed: { - const bool is_from_natrual = p->get_state() == State::EvaluationsOnRou_Natural; - // INTT to coeffs and back to evals - ntt_config.ordering = is_from_natrual ? ntt::Ordering::kNM : ntt::Ordering::kRN; - ntt::ntt(get_context_storage_immutable(p), poly_size, ntt::NTTDir::kInverse, ntt_config, d_evals); - ntt_config.ordering = is_from_natrual ? ntt::Ordering::kMN : ntt::Ordering::kNN; - ntt::ntt(d_evals, poly_size, ntt::NTTDir::kForward, ntt_config, d_evals); - } break; - default: - THROW_ICICLE_ERR(IcicleError_t::UndefinedError, "Invalid state to compute evaluations"); - break; - } - } - - // release CUDA memory if allocated - if (is_evals_on_host) { - CHK_STICKY( - cudaMemcpyAsync(evals, d_evals, domain_size * sizeof(I), cudaMemcpyDeviceToHost, m_device_context.stream)); - CHK_STICKY(cudaFreeAsync(d_evals, m_device_context.stream)); - } - - // sync since user cannot reuse this stream so need to make sure evals are computed - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); // sync to make sure return value is copied to host - - CHK_LAST(); - } - - uint64_t copy_coeffs(PolyContext op, C* out_coeffs, uint64_t start_idx, uint64_t end_idx) override - { - const uint64_t nof_coeffs = op->get_nof_elements(); - if (nullptr == out_coeffs) { return nof_coeffs; } // no allocated memory - - const bool is_valid_start_idx = start_idx < nof_coeffs; - const bool is_valid_end_idx = end_idx < nof_coeffs && end_idx >= start_idx; - const bool is_valid_indices = is_valid_start_idx && is_valid_end_idx; - if (!is_valid_indices) { - // return -1 instead? I could but 'get_coeff()' cannot with its current declaration - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "copy_coeffs() invalid indices"); - } - - op->transform_to_coefficients(); - auto [device_coeffs, _] = op->get_coefficients(); - const size_t nof_coeffs_to_copy = end_idx - start_idx + 1; - const bool is_copy_to_host = is_host_ptr(out_coeffs, m_device_context.device_id); - CHK_STICKY(cudaMemcpyAsync( - out_coeffs, device_coeffs + start_idx, nof_coeffs_to_copy * sizeof(C), - is_copy_to_host ? cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice, m_device_context.stream)); - CHK_STICKY(cudaStreamSynchronize(m_device_context.stream)); // sync to make sure return value is copied - - return nof_coeffs_to_copy; - } - - // read coefficients to host - C get_coeff(PolyContext op, uint64_t coeff_idx) override - { - C host_coeff; - copy_coeffs(op, &host_coeff, coeff_idx, coeff_idx); - return host_coeff; - } - - std::tuple< - IntegrityPointer, - uint64_t /*size*/ - , - uint64_t /*device_id*/> - get_coefficients_view(PolyContext p) override - { - return p->get_coefficients_view(); - } - - inline void assert_device_compatability(PolyContext a, PolyContext b) const - { - CUDAPolynomialContext* a_cuda = static_cast*>(a.get()); - CUDAPolynomialContext* b_cuda = static_cast*>(b.get()); - - const bool is_same_device = a_cuda->m_device_context.device_id == b_cuda->m_device_context.device_id; - if (!is_same_device) { - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "CUDA backend: incompatible polynomials, on different devices"); - } - } - }; - - /*============================== Polynomial CUDA-factory ==============================*/ - template - CUDAPolynomialFactory::CUDAPolynomialFactory() - { - int nof_cuda_devices = -1; - CHK_STICKY(cudaGetDeviceCount(&nof_cuda_devices)); - int orig_device = -1; - - CHK_STICKY(cudaGetDevice(&orig_device)); - m_device_streams.resize(nof_cuda_devices, nullptr); - - for (int dev_id = 0; dev_id < nof_cuda_devices; ++dev_id) { - CHK_STICKY(cudaSetDevice(dev_id)); - CHK_STICKY(cudaStreamCreate(&m_device_streams[dev_id])); - DeviceContext context = {m_device_streams[dev_id], (size_t)dev_id, 0x0 /*mempool*/}; - m_device_contexts.push_back(context); - } - CHK_STICKY(cudaSetDevice(orig_device)); // setting back original device - } - - template - CUDAPolynomialFactory::~CUDAPolynomialFactory() - { - for (auto stream_it : m_device_streams) { - CHK_STICKY(cudaStreamDestroy(stream_it)); // TODO Yuval: why does it fail? - } - } - - template - std::shared_ptr> CUDAPolynomialFactory::create_context() - { - int cuda_device_id = -1; - CHK_STICKY(cudaGetDevice(&cuda_device_id)); - return std::make_shared>(m_device_contexts[cuda_device_id]); - } - - template - std::shared_ptr> CUDAPolynomialFactory::create_backend() - { - int cuda_device_id = -1; - CHK_STICKY(cudaGetDevice(&cuda_device_id)); - return std::make_shared>(m_device_contexts[cuda_device_id]); - } - - // explicit instantiation for default type (scalar) - template class CUDAPolynomialContext<>; - template class CUDAPolynomialBackend<>; - template class CUDAPolynomialFactory<>; - -} // namespace polynomials \ No newline at end of file diff --git a/icicle/src/polynomials/polynomials.cu b/icicle/src/polynomials/polynomials.cpp similarity index 91% rename from icicle/src/polynomials/polynomials.cu rename to icicle/src/polynomials/polynomials.cpp index 6e250d367..ffac74d7e 100644 --- a/icicle/src/polynomials/polynomials.cu +++ b/icicle/src/polynomials/polynomials.cpp @@ -1,15 +1,19 @@ -#include "polynomials/polynomials.h" +#include "icicle/polynomials/polynomials.h" +#include "icicle/fields/field_config.h" -namespace polynomials { +using namespace field_config; + +namespace icicle { + + template + std::shared_ptr> get_polynomial_abstract_factory(const S* phantom = nullptr); template Polynomial::Polynomial() { - if (nullptr == s_factory) { - throw std::runtime_error("Polynomial factory not initialized. Must call Polynomial::initialize(factory)"); - } - m_context = s_factory->create_context(); - m_backend = s_factory->create_backend(); + auto backend_factory = get_polynomial_abstract_factory(); + m_context = backend_factory->create_context(); + m_backend = backend_factory->create_backend(); } template @@ -190,8 +194,7 @@ namespace polynomials { } template - std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> - Polynomial::get_coefficients_view() + std::tuple, uint64_t /*size*/> Polynomial::get_coefficients_view() { return m_backend->get_coefficients_view(m_context); } @@ -200,4 +203,4 @@ namespace polynomials { template class Polynomial; template Polynomial operator*(const scalar_t& c, const Polynomial& rhs); -} // namespace polynomials \ No newline at end of file +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/polynomials/polynomials_abstract_factory.cpp b/icicle/src/polynomials/polynomials_abstract_factory.cpp new file mode 100644 index 000000000..49bc0718c --- /dev/null +++ b/icicle/src/polynomials/polynomials_abstract_factory.cpp @@ -0,0 +1,20 @@ +#include "icicle/polynomials/polynomial_abstract_factory.h" +#include "icicle/dispatcher.h" +#include "icicle/fields/field_config.h" +using namespace field_config; + +namespace icicle { + + ICICLE_OBJECT_DISPATCHER_INST(PolynomialFactory, polynomial_factory, AbstractPolynomialFactory) + + template + std::shared_ptr> get_polynomial_abstract_factory(const S* phantom = nullptr); + + template <> + std::shared_ptr> + get_polynomial_abstract_factory(const scalar_t* phantom) + { + return PolynomialFactory::get_factory(); + } + +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/polynomials/polynomials_c_api.cu b/icicle/src/polynomials/polynomials_c_api.cpp similarity index 89% rename from icicle/src/polynomials/polynomials_c_api.cu rename to icicle/src/polynomials/polynomials_c_api.cpp index 8672fbd90..f7b82c5b0 100644 --- a/icicle/src/polynomials/polynomials_c_api.cu +++ b/icicle/src/polynomials/polynomials_c_api.cpp @@ -1,22 +1,16 @@ -#include "polynomials/polynomials.h" -#include "fields/field_config.cuh" -#include "utils/utils.h" -#include "utils/integrity_pointer.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" +#include "icicle/polynomials/polynomials.h" +#include "icicle/fields/field_config.h" +#include "icicle/utils/utils.h" +#include "icicle/utils/integrity_pointer.h" -namespace polynomials { +using namespace field_config; + +namespace icicle { extern "C" { // Defines a polynomial instance based on the scalar type from the FIELD configuration. typedef Polynomial PolynomialInst; - bool CONCAT_EXPAND(FIELD, polynomial_init_cuda_backend)() - { - static auto cuda_factory = std::make_shared>(); - PolynomialInst::initialize(cuda_factory); - return cuda_factory != nullptr; - } - // Constructs a polynomial from a set of coefficients. // coeffs: Array of coefficients. // size: Number of coefficients in the array. @@ -90,9 +84,9 @@ namespace polynomials { // a: Pointer to the polynomial instance. // scalar: Scalar to multiply by. // Returns a pointer to the resulting polynomial instance. - PolynomialInst* CONCAT_EXPAND(FIELD, polynomial_multiply_by_scalar)(const PolynomialInst* a, const scalar_t& scalar) + PolynomialInst* CONCAT_EXPAND(FIELD, polynomial_multiply_by_scalar)(const PolynomialInst* a, const scalar_t* scalar) { - auto result = new PolynomialInst(std::move(*a * scalar)); + auto result = new PolynomialInst(std::move(*a * (*scalar))); return result; } @@ -143,9 +137,9 @@ namespace polynomials { // monomial_coeff: Coefficient of the monomial to add. // monomial: Degree of the monomial to add. void CONCAT_EXPAND(FIELD, polynomial_add_monomial_inplace)( - PolynomialInst* p, const scalar_t& monomial_coeff, uint64_t monomial) + PolynomialInst* p, const scalar_t* monomial_coeff, uint64_t monomial) { - p->add_monomial_inplace(monomial_coeff, monomial); + p->add_monomial_inplace(*monomial_coeff, monomial); } // Subtracts a monomial from a polynomial in place. @@ -153,9 +147,9 @@ namespace polynomials { // monomial_coeff: Coefficient of the monomial to subtract. // monomial: Degree of the monomial to subtract. void CONCAT_EXPAND(FIELD, polynomial_sub_monomial_inplace)( - PolynomialInst* p, const scalar_t& monomial_coeff, uint64_t monomial) + PolynomialInst* p, const scalar_t* monomial_coeff, uint64_t monomial) { - p->sub_monomial_inplace(monomial_coeff, monomial); + p->sub_monomial_inplace(*monomial_coeff, monomial); } // Creates a new polynomial instance by slicing an existing polynomial. @@ -232,12 +226,10 @@ namespace polynomials { // size: Output parameter for the size of the view. // device_id: Output parameter for the device ID. // Returns a raw mutable pointer to the coefficients. - scalar_t* CONCAT_EXPAND(FIELD, polynomial_get_coeffs_raw_ptr)( - PolynomialInst* p, uint64_t* size /*OUT*/, uint64_t* device_id /*OUT*/) + scalar_t* CONCAT_EXPAND(FIELD, polynomial_get_coeffs_raw_ptr)(PolynomialInst* p, uint64_t* size /*OUT*/) { - auto [coeffs, _size, _device_id] = p->get_coefficients_view(); + auto [coeffs, _size] = p->get_coefficients_view(); *size = _size; - *device_id = _device_id; return const_cast(coeffs.get()); } @@ -246,12 +238,10 @@ namespace polynomials { // size: Output parameter for the size of the view. // device_id: Output parameter for the device ID. // Returns a pointer to an integrity pointer encapsulating the coefficients view. - IntegrityPointer* CONCAT_EXPAND(FIELD, polynomial_get_coeff_view)( - PolynomialInst* p, uint64_t* size /*OUT*/, uint64_t* device_id /*OUT*/) + IntegrityPointer* CONCAT_EXPAND(FIELD, polynomial_get_coeff_view)(PolynomialInst* p, uint64_t* size /*OUT*/) { - auto [coeffs, _size, _device_id] = p->get_coefficients_view(); + auto [coeffs, _size] = p->get_coefficients_view(); *size = _size; - *device_id = _device_id; return new IntegrityPointer(std::move(coeffs)); } @@ -274,4 +264,4 @@ namespace polynomials { } // extern "C" -} // namespace polynomials +} // namespace icicle diff --git a/icicle/src/poseidon/.gitignore b/icicle/src/poseidon/.gitignore deleted file mode 100644 index 60bb1be5d..000000000 --- a/icicle/src/poseidon/.gitignore +++ /dev/null @@ -1 +0,0 @@ -test_poseidon diff --git a/icicle/src/poseidon/Makefile b/icicle/src/poseidon/Makefile deleted file mode 100644 index 11400b418..000000000 --- a/icicle/src/poseidon/Makefile +++ /dev/null @@ -1,5 +0,0 @@ -test_poseidon: test.cu - nvcc -o test_poseidon -I../../include -DFIELD=bls12_381 -DFIELD_ID=2 -DCURVE_ID=2 -DDEVMODE -DDEBUG extern.cu test.cu - -test_poseidon_m31: test_m31.cu - nvcc -o test_poseidon -I../../include -DFIELD=m31 -DFIELD_ID=1003 -DDEVMODE -DDEBUG extern.cu test_m31.cu diff --git a/icicle/src/poseidon/constants.cu b/icicle/src/poseidon/constants.cu deleted file mode 100644 index bed5a9be0..000000000 --- a/icicle/src/poseidon/constants.cu +++ /dev/null @@ -1,152 +0,0 @@ -#include "poseidon/constants.cuh" -#include "gpu-utils/device_context.cuh" - -/// These are pre-calculated constants for different curves -#include "fields/id.h" -#if FIELD_ID == BN254 -#include "poseidon/constants/bn254_poseidon.h" -using namespace poseidon_constants_bn254; -#elif FIELD_ID == BLS12_381 -#include "poseidon/constants/bls12_381_poseidon.h" -using namespace poseidon_constants_bls12_381; -#elif FIELD_ID == BLS12_377 -#include "poseidon/constants/bls12_377_poseidon.h" -using namespace poseidon_constants_bls12_377; -#elif FIELD_ID == BW6_761 -#include "poseidon/constants/bw6_761_poseidon.h" -using namespace poseidon_constants_bw6_761; -#elif FIELD_ID == GRUMPKIN -#include "poseidon/constants/grumpkin_poseidon.h" -using namespace poseidon_constants_grumpkin; -#elif FIELD_ID == M31 -#include "poseidon/constants/m31_poseidon.h" -using namespace poseidon_constants_m31; -#endif - -namespace poseidon { - template - cudaError_t create_optimized_poseidon_constants( - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const S* round_constants, - const S* mds_matrix, - const S* non_sparse_matrix, - const S* sparse_matrices, - const S domain_tag, - PoseidonConstants* poseidon_constants, - device_context::DeviceContext& ctx) - { - CHK_INIT_IF_RETURN(); - cudaStream_t& stream = ctx.stream; - int width = arity + 1; - int round_constants_len = width * full_rounds_half * 2 + partial_rounds; - int mds_matrix_len = width * width; - int sparse_matrices_len = (width * 2 - 1) * partial_rounds; - int constants_len = round_constants_len + mds_matrix_len * 2 + sparse_matrices_len; - - // Malloc memory for copying constants - S* d_constants; - CHK_IF_RETURN(cudaMallocAsync(&d_constants, sizeof(S) * constants_len, stream)); - - S* d_round_constants = d_constants; - S* d_mds_matrix = d_round_constants + round_constants_len; - S* d_non_sparse_matrix = d_mds_matrix + mds_matrix_len; - S* d_sparse_matrices = d_non_sparse_matrix + mds_matrix_len; - - // Copy constants - CHK_IF_RETURN(cudaMemcpyAsync( - d_round_constants, round_constants, sizeof(S) * round_constants_len, cudaMemcpyHostToDevice, stream)); - CHK_IF_RETURN( - cudaMemcpyAsync(d_mds_matrix, mds_matrix, sizeof(S) * mds_matrix_len, cudaMemcpyHostToDevice, stream)); - CHK_IF_RETURN(cudaMemcpyAsync( - d_non_sparse_matrix, non_sparse_matrix, sizeof(S) * mds_matrix_len, cudaMemcpyHostToDevice, stream)); - CHK_IF_RETURN(cudaMemcpyAsync( - d_sparse_matrices, sparse_matrices, sizeof(S) * sparse_matrices_len, cudaMemcpyHostToDevice, stream)); - - // Make sure all the constants have been copied - CHK_IF_RETURN(cudaStreamSynchronize(stream)); - *poseidon_constants = { - arity, - alpha, - partial_rounds, - full_rounds_half, - d_round_constants, - d_mds_matrix, - d_non_sparse_matrix, - d_sparse_matrices, - domain_tag}; - - return CHK_LAST(); - } - - template - cudaError_t init_optimized_poseidon_constants( - int arity, device_context::DeviceContext& ctx, PoseidonConstants* poseidon_constants) - { - CHK_INIT_IF_RETURN(); - unsigned int full_rounds_half = FULL_ROUNDS_DEFAULT; - unsigned int partial_rounds; - unsigned char* constants; - switch (arity) { - case 2: - constants = poseidon_constants_2; - partial_rounds = partial_rounds_2; - break; - case 4: - constants = poseidon_constants_4; - partial_rounds = partial_rounds_4; - break; - case 8: - constants = poseidon_constants_8; - partial_rounds = partial_rounds_8; - break; - case 11: - constants = poseidon_constants_11; - partial_rounds = partial_rounds_11; - break; - default: - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "init_optimized_poseidon_constants: #arity must be one of [2, 4, 8, 11]"); - } - S* h_constants = reinterpret_cast(constants); - - unsigned int width = arity + 1; - unsigned int round_constants_len = width * full_rounds_half * 2 + partial_rounds; - unsigned int mds_matrix_len = width * width; - - S* round_constants = h_constants; - S* mds_matrix = round_constants + round_constants_len; - S* non_sparse_matrix = mds_matrix + mds_matrix_len; - S* sparse_matrices = non_sparse_matrix + mds_matrix_len; - - // Pick the domain_tag accordinaly - // For now, we only support Merkle tree mode - uint32_t tree_domain_tag_value = 1; - tree_domain_tag_value = (tree_domain_tag_value << (width - 1)) - tree_domain_tag_value; - S domain_tag = S::from(tree_domain_tag_value); - - create_optimized_poseidon_constants( - arity, 5, partial_rounds, full_rounds_half, round_constants, mds_matrix, non_sparse_matrix, sparse_matrices, - domain_tag, poseidon_constants, ctx); - - return CHK_LAST(); - } - - template - cudaError_t release_optimized_poseidon_constants(PoseidonConstants* constants, device_context::DeviceContext& ctx) - { - CHK_INIT_IF_RETURN(); - CHK_IF_RETURN(cudaFreeAsync(constants->round_constants, ctx.stream)); - - constants->arity = 0; - constants->partial_rounds = 0; - constants->full_rounds_half = 0; - constants->round_constants = nullptr; - constants->mds_matrix = nullptr; - constants->non_sparse_matrix = nullptr; - constants->sparse_matrices = nullptr; - return CHK_LAST(); - } -} // namespace poseidon \ No newline at end of file diff --git a/icicle/src/poseidon/extern.cu b/icicle/src/poseidon/extern.cu deleted file mode 100644 index 16145bb21..000000000 --- a/icicle/src/poseidon/extern.cu +++ /dev/null @@ -1,69 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "poseidon/poseidon.cuh" -#include "constants.cu" - -#include "gpu-utils/device_context.cuh" -#include "utils/utils.h" - -namespace poseidon { - typedef class Poseidon PoseidonInst; - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon_create_cuda)( - PoseidonInst** poseidon, - unsigned int arity, - unsigned int alpha, - unsigned int partial_rounds, - unsigned int full_rounds_half, - const scalar_t* round_constants, - const scalar_t* mds_matrix, - const scalar_t* non_sparse_matrix, - const scalar_t* sparse_matrices, - const scalar_t& domain_tag, - device_context::DeviceContext& ctx) - { - try { - *poseidon = new PoseidonInst( - arity, alpha, partial_rounds, full_rounds_half, round_constants, mds_matrix, non_sparse_matrix, sparse_matrices, - domain_tag, ctx); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon_load_cuda)( - PoseidonInst** poseidon, unsigned int arity, device_context::DeviceContext& ctx) - { - try { - *poseidon = new PoseidonInst(arity, ctx); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon_hash_many_cuda)( - const PoseidonInst* poseidon, - const scalar_t* inputs, - scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - const HashConfig& cfg) - { - return poseidon->hash_many(inputs, output, number_of_states, input_block_len, output_len, cfg); - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon_delete_cuda)(PoseidonInst* poseidon) - { - try { - poseidon->~Poseidon(); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } -} // namespace poseidon \ No newline at end of file diff --git a/icicle/src/poseidon/test.cu b/icicle/src/poseidon/test.cu deleted file mode 100644 index 461b57ae0..000000000 --- a/icicle/src/poseidon/test.cu +++ /dev/null @@ -1,1097 +0,0 @@ -// #define DEBUG - -#include "curves/curve_config.cuh" -using namespace curve_config; - -#include "gpu-utils/device_context.cuh" - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include - -#include "api/bls12_381.h" -using namespace bls12_381; - -#include "poseidon/poseidon.cuh" -using namespace poseidon; - -#define A 2 -#define T (A + 1) - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - // Load poseidon constants - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - Poseidon poseidon(A, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - START_TIMER(allocation_timer); - // Prepare input data of [0, 1, 2 ... (number_of_blocks * arity) - 1] - int number_of_blocks = argc > 1 ? 1 << atoi(argv[1]) : 1024; - scalar_t input = scalar_t::zero(); - scalar_t* in_ptr = static_cast(malloc(number_of_blocks * A * sizeof(scalar_t))); - for (uint32_t i = 0; i < number_of_blocks * A; i++) { - in_ptr[i] = input; - input = input + scalar_t::one(); - } - END_TIMER(allocation_timer, "Allocate mem and fill input"); - - scalar_t* out_ptr = static_cast(malloc(number_of_blocks * sizeof(scalar_t))); - - HashConfig cfg = default_hash_config(); - - START_TIMER(poseidon_timer); - poseidon.hash_many(in_ptr, out_ptr, number_of_blocks, A, 1, cfg); - END_TIMER(poseidon_timer, "Poseidon") - - scalar_t expected[1024] = { - {2583881727, 773864502, 2634393245, 2801510707, 49275233, 1939738585, 1584833899, 962922711}, - {1482052501, 2945755510, 2790332687, 3994795689, 2690398473, 2055226187, 3927265331, 526041267}, - {908959580, 3968357170, 168369822, 4279251122, 172491869, 1810633943, 1108167336, 461319268}, - {4027140347, 2893980563, 921480450, 501609030, 2327498415, 3169336813, 3929258806, 737280672}, - {4055714606, 2743326044, 2857702856, 1584420791, 1660813445, 3348655617, 3787458226, 3699425}, - {1985741293, 4089215036, 1210827339, 2345137603, 1161636418, 1599639659, 321012535, 194290036}, - {1750888684, 3456174369, 3318295268, 1497259571, 2073515266, 4072260308, 3437570998, 1635895511}, - {4034710333, 2610124732, 1290861184, 4036862757, 883362062, 567869034, 2024849712, 1701263846}, - {2625474608, 2754606612, 361203290, 1487232625, 2688318352, 2549310207, 2466099371, 1733038062}, - {3345001546, 3042331439, 4011626368, 3133515243, 2486233703, 1331042170, 1361957740, 650666142}, - {1584567590, 1084515947, 4098195566, 3866758584, 3356823276, 1832067525, 1818740948, 741849726}, - {3286393376, 4136788043, 10996123, 492094606, 528500176, 2438101940, 2875284654, 1786544386}, - {1969597223, 790536143, 2237882132, 3796626452, 2790109442, 1011819674, 3528538383, 589770849}, - {407685073, 1453168838, 2179621584, 788291901, 1106952489, 1446367022, 4069426728, 1396878930}, - {3290109019, 4015017806, 3934117633, 4158617411, 2939969774, 106961513, 14918808, 1001708086}, - {996002275, 1928470656, 2623701620, 2551598991, 3751170806, 508902944, 369242849, 1498870796}, - {565255546, 3022177816, 293742057, 2157713418, 1983002409, 1107184702, 1767026869, 1183824511}, - {2311948870, 2800572440, 3145540846, 3682970284, 1080117063, 2915402481, 3269455294, 716122601}, - {2295776029, 501186048, 942983026, 4051534110, 2477546403, 2935458647, 254557289, 178729694}, - {3337435330, 4030596207, 3117011884, 2422164923, 2397253028, 875773417, 3477084352, 1126142070}, - {3431800845, 4060912624, 2245793080, 2499622891, 91998757, 2164075869, 3803348693, 166480081}, - {966663497, 630611456, 167291166, 773313755, 1503785959, 1202596199, 3191514300, 1306382185}, - {3933637844, 1226332868, 3032390356, 1110249575, 3533687435, 1934216432, 1494095598, 1682810774}, - {663270725, 384860935, 2827178047, 827465888, 3087237951, 3255426106, 1373579066, 499572712}, - {2577656678, 3066691302, 1684488911, 1957939881, 4019083797, 1755409167, 1832816180, 1223903171}, - {1063044264, 1259737746, 3048015260, 1826994984, 3349938836, 637174051, 3186708364, 1072388481}, - {424144692, 2929334085, 3898061065, 1345722322, 2247127222, 2049099905, 2811348666, 1160935385}, - {1742356760, 455132233, 567937646, 1446680057, 606998240, 4160248945, 3056763692, 304816744}, - {3902143865, 2475092117, 3760400589, 355228026, 1716941022, 3657064101, 3335169844, 311397023}, - {3136484494, 2477484115, 3189327926, 3887284100, 1520306748, 1408584587, 360476494, 468824213}, - {2650171511, 3917673809, 3045237931, 4207688203, 429415446, 1750523088, 1223306704, 1525104464}, - {3339231665, 2528413500, 2046295048, 418068806, 685542366, 1682284588, 1485485833, 1473238879}, - {711161825, 3514077185, 3534145946, 1223731975, 2083004206, 1591181325, 146921769, 1335013702}, - {3360230958, 1823167614, 1476318054, 3466563514, 2415011524, 1769592146, 1575481551, 948928279}, - {2203262174, 1048259904, 2990768499, 1722929916, 163385007, 3862551299, 3245410259, 660227543}, - {2420337797, 388803468, 2995221236, 178657789, 724044101, 1928913452, 391326078, 738986320}, - {376206369, 1284511059, 2214401189, 891395247, 1960216581, 3830403005, 3904641289, 286711373}, - {599464569, 2411718164, 3735534528, 1577933164, 1934209663, 208697469, 4240204984, 1878780213}, - {2248371995, 1913736731, 845860752, 2437601421, 2181099977, 3047079036, 2994731640, 1056395413}, - {1616682327, 1644006237, 4168209898, 668467850, 2455928538, 1293832315, 1951304978, 733478871}, - {2918737298, 3521094856, 1829452807, 2725951918, 200736933, 3770832430, 302979408, 1669366138}, - {387191313, 3173401215, 4094986219, 4046955536, 2468524524, 1397566203, 1547260481, 1630494205}, - {1611875529, 3743344191, 2871657641, 2077834981, 2265555313, 2220128636, 3401817316, 730340725}, - {2970289894, 2504545394, 2645966447, 1893372766, 211607532, 48403161, 2511733484, 1777123856}, - {4026002355, 2848115047, 2779427621, 1719017788, 1519304609, 3000001502, 1762163589, 1651472793}, - {2675453183, 2789246503, 4130550033, 1391534849, 892965520, 840706700, 694359255, 1471724826}, - {820595074, 3339511113, 1226826204, 4002836485, 3062031653, 3909036982, 3234099926, 478805481}, - {2754248472, 2077338143, 1320736316, 1958645234, 3844752893, 3889630124, 2015730162, 1490420502}, - {460441047, 350047886, 2731923076, 3535777260, 1340261262, 1608902027, 4234035213, 1536752667}, - {114082443, 3561864573, 3250444094, 2685014404, 665145438, 2794487913, 2897233421, 1911369787}, - {3964405473, 1106848460, 2730715961, 2096494338, 432077991, 1817935912, 2825329005, 364568296}, - {1541495233, 533720033, 1539839704, 963087454, 3318790731, 3533292343, 3370280345, 1088979921}, - {31440674, 1086708697, 3205357768, 1917821690, 4056968740, 2683764451, 1041894546, 548238649}, - {1917051981, 2014974056, 4094529913, 154561501, 2688770477, 2718119219, 2705815412, 1716758521}, - {4067501667, 2229507042, 1856704407, 3392292278, 517345750, 3716036323, 3218796244, 1233797639}, - {493354704, 810143557, 149270290, 3129000957, 1614740539, 852245369, 1103758623, 1444391322}, - {1605729361, 2765340595, 2072059042, 272378393, 200608211, 1405978231, 2442707275, 1356618195}, - {614194557, 2969487285, 3926681252, 3394676563, 2726949908, 16435247, 2403689085, 1204665742}, - {2460966997, 186611346, 3986216756, 4264387316, 2217732635, 1106203394, 615954162, 1452862218}, - {1974864998, 3557372049, 2306291317, 1337077016, 3157009519, 3694567151, 2200295624, 951056188}, - {1787294003, 4192197592, 3312641269, 2356072500, 3461522896, 108687088, 4157440844, 526413998}, - {2399361279, 1706831513, 3887495132, 2793491621, 1958115904, 3177075308, 2155894953, 145589220}, - {2352454770, 864469346, 2670274751, 1614948498, 3267466158, 3191597377, 1776987447, 1357175017}, - {2740743875, 3345691126, 2816549323, 3348140332, 1516932249, 897622960, 2037058063, 995705082}, - {880606236, 1189688463, 4234153518, 2339439358, 2923718870, 1042013320, 1678648541, 1104462183}, - {1073654866, 3413686839, 685570659, 3037652080, 947636738, 3700390221, 3693398039, 415494178}, - {2419572877, 1150916625, 2249277119, 683261527, 1864331069, 1244761785, 552125966, 1347677685}, - {3396762050, 2693884475, 424685848, 1664147381, 1834283263, 2542432283, 3974116944, 68536520}, - {3063924165, 1932015124, 2217710816, 3830128168, 3370081034, 574155316, 3313875014, 1855459315}, - {3386803395, 2820493340, 3308680514, 1427392357, 1583014142, 1749204707, 221657680, 547450834}, - {1041777630, 161530099, 1155566993, 1892707944, 3757054220, 2087391796, 3585530204, 977318577}, - {2665571342, 2429563664, 3195162290, 2644863207, 2114978363, 3231080092, 3457160023, 725625708}, - {1287942936, 2906541389, 3888844666, 3045944547, 1704436269, 935499132, 2453611416, 1213734667}, - {1117593248, 2743290884, 2066542181, 3835707713, 1920393447, 1618056951, 1297468894, 900716017}, - {2453584163, 3404571319, 2858950303, 115950125, 4024397824, 646888038, 2302216357, 2756808}, - {2613043977, 3417804754, 1884878530, 3335038875, 6972604, 711348538, 2748028015, 826527564}, - {2713903867, 3794506523, 790592691, 1992735309, 2847369131, 4204363625, 1087865122, 1443814505}, - {1146497564, 848229830, 1946260269, 3010322816, 2207800574, 2200410833, 2844194823, 342476999}, - {536000215, 56179675, 101847642, 3011739994, 2872487441, 89501866, 898367473, 331781205}, - {792457657, 1377513795, 2577314200, 3373384733, 4274706950, 2925205477, 890772987, 1095800397}, - {182796072, 1578145882, 2336921029, 3162145845, 4062838559, 3023577617, 2330808132, 1428434925}, - {2403626545, 3974944436, 3485684469, 553830322, 38386987, 4043096538, 2935160779, 1605533456}, - {3592979798, 3846514876, 4128835545, 3365534930, 3661819211, 206722386, 3701547698, 1155118261}, - {3518352518, 552097980, 1106877825, 3098440123, 1904097843, 798323810, 2937297984, 306844313}, - {846683726, 1141034129, 2737947207, 3948706817, 3691508076, 1515816987, 3737359958, 479108481}, - {1925088217, 3196619024, 3151602276, 1132584884, 398312424, 3664593321, 1435127993, 640739107}, - {4007582001, 4056833411, 1681991589, 3159809312, 3733176952, 2658324364, 4156115728, 793757139}, - {2186520143, 188914433, 3154274450, 702884605, 687995516, 3197971561, 1743850711, 439513656}, - {3681243065, 4194035329, 3413995370, 207340710, 2792859700, 3621365942, 690238239, 650694641}, - {4194002326, 2754965636, 2026612339, 3417461073, 3389514679, 289083315, 945831954, 1646504390}, - {3728664917, 874441051, 4237550353, 1267640098, 1493897726, 235351278, 1343778823, 512604703}, - {1832471478, 3107338776, 1857313534, 3280811411, 3036888090, 3889765552, 1968816396, 1225855424}, - {744301333, 3406502346, 783212095, 2374949182, 781907448, 2557584046, 3381526102, 1029233876}, - {344800482, 222185159, 2883488241, 779251249, 2822944331, 3330005398, 3591215672, 302028091}, - {1210514521, 1750377041, 2565419, 2503857968, 1425360604, 2528973098, 857792357, 1195257810}, - {2196986518, 237838621, 1892525295, 286690471, 1622780101, 2639346316, 4286156169, 1597775594}, - {2338818263, 2938274921, 1230254141, 727173531, 3273895384, 2463754633, 41765405, 490358949}, - {3971405844, 2427101257, 4260135480, 3905725796, 2272941250, 1815202830, 2674840136, 1151632468}, - {772068597, 2077570282, 3573641073, 3594585180, 1796738974, 69390997, 2819669018, 109156866}, - {3407797779, 4273031067, 2447795211, 2617274976, 3350369456, 1810165409, 495004487, 280805960}, - {1854400152, 1015997936, 4178837499, 2969860395, 3276208401, 2266705868, 3474085081, 381373149}, - {4273910617, 3432009007, 1963113415, 3478788384, 4263358486, 1474675855, 1363869172, 1404241549}, - {3347173429, 3488817444, 3586742747, 1881341598, 2866825457, 4123090347, 1133449072, 1012876069}, - {3766326425, 2549726737, 2395067040, 94384143, 2981277617, 3903715059, 1418402831, 1086843578}, - {1066353224, 947945827, 641160302, 3710315689, 2100152022, 362375464, 3382102350, 696249971}, - {3715078923, 4177862627, 3773159689, 752863577, 1567881938, 2890233387, 2275434392, 761611465}, - {1136563047, 293855491, 907210887, 3459013925, 2467328309, 2508366795, 1653911948, 171845987}, - {612379611, 1887075760, 487087448, 225291858, 132676616, 2064671605, 2406766416, 1233233811}, - {2092184335, 1015806827, 1146757670, 1090496205, 1043022102, 3737237655, 3806015317, 14600533}, - {499304627, 4098834222, 3759308706, 1388356421, 210173650, 1741203929, 1772159179, 495805071}, - {2229669327, 1545664598, 349055216, 3833190775, 358708937, 2983722085, 3017903221, 1531390637}, - {4072290516, 1556732269, 4022277258, 1306533569, 2391722068, 3016774265, 1908302439, 494704407}, - {1167973235, 1627746966, 219299288, 110675411, 4208405345, 3855064237, 4061394129, 1651264307}, - {807895823, 1850319638, 3286622806, 1614836268, 2867522342, 411515703, 1793528862, 552472260}, - {2900983917, 3294231504, 2202011636, 3574159155, 1974489724, 2886293794, 3281774714, 484959753}, - {2742711752, 1287345215, 3181531962, 2021440514, 3439017902, 715452484, 4180842775, 134122585}, - {1531199361, 2983901779, 3903415668, 3964463567, 2644568942, 2487255696, 2599734899, 1249314973}, - {1518027012, 198792757, 3469220298, 2822826170, 3866069214, 2099371040, 1756790447, 285411729}, - {3165530355, 895959300, 809561874, 2072573055, 3986839699, 793131249, 2061077774, 1133461696}, - {2582197220, 213769082, 274179281, 3142523875, 3736763344, 1583183690, 4062325045, 1061009665}, - {644857811, 886625807, 1464693834, 3050181877, 3725483719, 1505440349, 4086205221, 991794257}, - {3162388994, 1162627825, 3204861587, 811326633, 1365038410, 3031769664, 1687403961, 915654805}, - {1995779911, 3004580585, 1515285492, 522932916, 1212660322, 2864409226, 3097856431, 693847144}, - {2053050441, 1115202172, 1549567112, 655697921, 3671500054, 3835118574, 3214011205, 1821384668}, - {1872231643, 872021024, 1336655960, 211273340, 3354505888, 1417807145, 456513608, 963893896}, - 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{2468766879, 778346573, 1474514048, 4121315751, 1512180969, 904418434, 1136251375, 1319914909}, - {2501603373, 3538565524, 284824834, 1451336583, 1293267847, 3170116086, 3254403159, 719213547}, - {2474812956, 77386015, 1931765089, 3098132001, 420441098, 2456483837, 1422426453, 641318198}, - {3644137427, 4287985815, 394664607, 1233537396, 421825453, 651742535, 387125474, 662823303}, - {4212030094, 1122465335, 1624022888, 3610548406, 353634777, 2552519412, 3801211635, 307132084}, - {1812732959, 3053502143, 2631811210, 2586361940, 1962179385, 3483070162, 4155788085, 1550838787}, - {1003031331, 1302317500, 2617903797, 2734240305, 989510592, 4089046563, 2821236351, 414611967}, - {1484639241, 726371698, 2901761099, 1076175113, 1726435871, 1077578729, 1890799589, 1909509214}, - {3700769167, 1752821084, 513320038, 1307836100, 578628329, 2406730051, 2995527212, 837313727}, - {122300200, 2966018197, 2685843456, 753118815, 1457197440, 957992679, 1985405656, 883670266}, - {1406210640, 2351047507, 3269280518, 3490188984, 4142280886, 3025376962, 3963346097, 994860097}, - {2508852230, 2631286947, 258546584, 3864880274, 1835714337, 2164344383, 266610560, 1805588083}, - {1399945673, 4184106804, 789724757, 1079151694, 2482798800, 3333268030, 1316021989, 1255596472}, - {803951911, 2989032923, 3770186442, 1016864736, 2468907700, 2913064459, 1185103480, 636741796}, - {1231369251, 4135232566, 2733627621, 833380325, 29278262, 1838736816, 282517621, 838375152}, - {1317232431, 1026204235, 1068913493, 407775287, 3334561628, 795893931, 4235491726, 1615001328}, - {553856711, 971855917, 3933094274, 2073072684, 3392281711, 2796369744, 2729547217, 1071905806}, - {1887240778, 16049011, 1301729181, 3042530676, 936796797, 1994942578, 2223529819, 647936271}, - {2704422729, 1675170249, 3552773842, 3396498429, 3400652126, 3505392417, 761033286, 124643040}, - {1889942262, 3227942591, 2292339076, 3457625997, 2126114223, 2180302021, 284476605, 44924534}, - {141984372, 2951276745, 35118693, 593555826, 3272461528, 3950614747, 3504508970, 885326384}, - {2305593269, 453447852, 4198398217, 3289740015, 3148298721, 2531766886, 1665567516, 31461126}, - {2961594960, 2531642714, 1639520373, 2621506607, 2179476118, 2885320184, 1723902844, 1018079279}, - {2946288140, 3272340504, 3540734279, 4197505952, 3276858996, 1346238140, 4252445552, 353887417}, - {1372501495, 824753971, 860420574, 2514019588, 3560899823, 4247421862, 4088400720, 1657596004}, - {3547216933, 213080010, 1327670122, 612432652, 1467988350, 1684096876, 1071303132, 628211428}, - {3090209150, 1759114382, 3187913922, 3152697956, 1184367906, 1160970938, 2392528111, 800739539}, - {621315835, 1089798189, 674639458, 3890294633, 2648105579, 3553900581, 4112938456, 1490904470}, - {3765350890, 759301313, 1861950123, 4059907539, 1733775257, 2250513344, 1658744551, 1153214746}, - {1170138394, 1137625749, 2897796390, 3416594251, 391570540, 4019828978, 3251064953, 816833270}, - {1231309804, 3780640118, 2711898201, 3491043249, 3439535076, 4207721813, 2305741506, 1028659493}, - {1253871575, 2948351374, 1135039949, 1734394040, 142360803, 3446688949, 4049427930, 944770588}, - {1941541664, 410182229, 946427815, 1738300148, 1710743312, 726482062, 635454546, 357241893}, - {1020511136, 2100021722, 3298155473, 2108863026, 3280566308, 1760065744, 3252469090, 1429387071}, - {1217925349, 3637413438, 1551351736, 423738310, 2990537205, 3220442810, 3931295343, 906723644}, - {972280401, 2780016393, 3173872770, 1356012643, 2461239415, 4162849661, 3861310034, 1831469808}, - {1389822966, 2952619570, 4129543154, 1090053600, 2373269118, 1012273040, 3572998746, 735743939}, - {1860915644, 4058588150, 3985807003, 3283071968, 1648222944, 3385052127, 3497619137, 385683862}, - {884383025, 3552407191, 3469324339, 3465009688, 3813476498, 3729803703, 3537345067, 234943732}, - {555727800, 626558964, 1415912308, 2973550418, 2237386952, 1244544807, 4213281194, 809110235}, - {2588631464, 469863063, 3737851331, 837541418, 1967059868, 2427883562, 3072041960, 960796293}, - {1207065556, 1770203065, 3879628650, 973162225, 2309503005, 2047327014, 3465067208, 336588005}, - {2785167982, 2995223308, 72923062, 974823923, 1628617410, 4097389313, 542947051, 539026017}, - {2236323182, 3282563504, 1224066908, 3047814769, 1295019432, 438565816, 619226031, 223436864}, - {786918017, 2048377421, 589200564, 4079431549, 590547127, 1581163706, 2544682669, 52267199}, - {2655976805, 2376527940, 3402099846, 3970317208, 3576646612, 1791464619, 1974470062, 1726193434}, - {7025984, 1951598457, 3431147596, 3998422323, 2645714763, 1438112310, 3971388058, 1607757765}, - {518311553, 3785088372, 1131104604, 1849385860, 1783473879, 2100330799, 2095617988, 1865596046}, - {1681239869, 1999613256, 1685314892, 3568687870, 2223144938, 1715202611, 2718808748, 1010745768}, - {3996328427, 801631366, 806151229, 1284360371, 3127991234, 4258117530, 2210082045, 1193672326}, - {3906397586, 3290474371, 1675512984, 4178526889, 2952985253, 3592646253, 1572272369, 508740262}, - {4126315652, 3786312147, 3699923125, 3674356834, 1459586774, 373249965, 2436297855, 1091365949}}; - - if (number_of_blocks == 1024) { - for (int i = 0; i < number_of_blocks; i++) { -#ifdef DEBUG - // std::cout << out_ptr[i] << std::endl; -#endif - assert((out_ptr[i] == expected[i])); - } - printf("Expected output matches\n"); - } - - free(in_ptr); - free(out_ptr); -} - -#endif \ No newline at end of file diff --git a/icicle/src/poseidon/test_m31.cu b/icicle/src/poseidon/test_m31.cu deleted file mode 100644 index 7f6bd7ce4..000000000 --- a/icicle/src/poseidon/test_m31.cu +++ /dev/null @@ -1,70 +0,0 @@ -// #define DEBUG - -#include "fields/field_config.cuh" -using namespace field_config; - -#include "gpu-utils/device_context.cuh" -#include "poseidon/poseidon.cuh" - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include - -using namespace poseidon; - -#define A 11 -#define T (A + 1) - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - // Load poseidon constants - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - PoseidonConstants constants; - init_optimized_poseidon_constants(A, ctx, &constants); - END_TIMER(timer_const, "Load poseidon constants"); - - START_TIMER(allocation_timer); - // Prepare input data of [0, 1, 2 ... (number_of_blocks * arity) - 1] - int number_of_blocks = argc > 1 ? 1 << atoi(argv[1]) : 1024; - scalar_t input = scalar_t::zero(); - scalar_t* in_ptr = static_cast(malloc(number_of_blocks * A * sizeof(scalar_t))); - for (uint32_t i = 0; i < number_of_blocks * A; i++) { - in_ptr[i] = input; - input = input + scalar_t::one(); - } - END_TIMER(allocation_timer, "Allocate mem and fill input"); - - scalar_t* out_ptr = static_cast(malloc(number_of_blocks * sizeof(scalar_t))); - - START_TIMER(poseidon_timer); - PoseidonConfig config = default_poseidon_config(T); - poseidon_hash(in_ptr, out_ptr, number_of_blocks, constants, config); - END_TIMER(poseidon_timer, "Poseidon") - - // scalar_t expected[0] = {} - - if (number_of_blocks == 1024) { - for (int i = 0; i < number_of_blocks; i++) { -#ifdef DEBUG - // std::cout << out_ptr[i] << std::endl; -#endif - // assert((out_ptr[i] == expected[i])); - } - printf("Expected output matches\n"); - } - - free(in_ptr); - free(out_ptr); -} - -#endif \ No newline at end of file diff --git a/icicle/src/poseidon2/.gitignore b/icicle/src/poseidon2/.gitignore deleted file mode 100644 index bf4d73d82..000000000 --- a/icicle/src/poseidon2/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -test_poseidon -test_poseidon_release \ No newline at end of file diff --git a/icicle/src/poseidon2/Makefile b/icicle/src/poseidon2/Makefile deleted file mode 100644 index 705bba499..000000000 --- a/icicle/src/poseidon2/Makefile +++ /dev/null @@ -1,5 +0,0 @@ -test_poseidon: test.cu - nvcc -o test_poseidon -I../../include -DFIELD=bn254 -DFIELD_ID=1 -DCURVE_ID=1 extern.cu test.cu - -test_poseidon_m31: test_m31.cu - nvcc -o test_poseidon_m31 -I../../include -DFIELD=m31 -DFIELD_ID=1003 extern.cu test_m31.cu \ No newline at end of file diff --git a/icicle/src/poseidon2/constants.cu b/icicle/src/poseidon2/constants.cu deleted file mode 100644 index 3fa157d2a..000000000 --- a/icicle/src/poseidon2/constants.cu +++ /dev/null @@ -1,136 +0,0 @@ -#include "poseidon2/constants.cuh" -#include "gpu-utils/device_context.cuh" - -/// These are pre-calculated constants for different curves -#include "fields/id.h" -#if FIELD_ID == BN254 -#include "poseidon2/constants/bn254_poseidon2.h" -using namespace poseidon2_constants_bn254; -#elif FIELD_ID == BLS12_381 -#include "poseidon2/constants/bls12_381_poseidon2.h" -using namespace poseidon2_constants_bls12_381; -#elif FIELD_ID == BLS12_377 -#include "poseidon2/constants/bls12_377_poseidon2.h" -using namespace poseidon2_constants_bls12_377; -#elif FIELD_ID == BW6_761 -#include "poseidon2/constants/bw6_761_poseidon2.h" -using namespace poseidon2_constants_bw6_761; -#elif FIELD_ID == GRUMPKIN -#include "poseidon2/constants/grumpkin_poseidon2.h" -using namespace poseidon2_constants_grumpkin; -#elif FIELD_ID == BABY_BEAR -#include "poseidon2/constants/babybear_poseidon2.h" -using namespace poseidon2_constants_babybear; -#elif FIELD_ID == M31 -#include "poseidon2/constants/m31_poseidon2.h" -using namespace poseidon2_constants_m31; -#endif - -namespace poseidon2 { - template - cudaError_t create_poseidon2_constants( - int width, - int alpha, - int internal_rounds, - int external_rounds, - const S* round_constants, - const S* internal_matrix_diag, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx, - Poseidon2Constants* poseidon_constants) - { - if (!(alpha == 3 || alpha == 5 || alpha == 7 || alpha == 11)) { - THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Invalid alpha value"); - } - if (external_rounds % 2) { THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "Invalid external rounds"); } - - CHK_INIT_IF_RETURN(); - cudaStream_t& stream = ctx.stream; - - int round_constants_len = width * (external_rounds) + internal_rounds; - int internal_matrix_len = width; - - // Malloc memory for copying round constants and internal matrix - S* d_constants; - CHK_IF_RETURN(cudaMallocAsync(&d_constants, sizeof(S) * (internal_matrix_len + round_constants_len), stream)); - - S* d_internal_matrix = d_constants; - S* d_round_constants = d_constants + internal_matrix_len; - - // Copy internal matrix - CHK_IF_RETURN(cudaMemcpyAsync( - d_internal_matrix, internal_matrix_diag, sizeof(S) * internal_matrix_len, cudaMemcpyHostToDevice, stream)); - // Copy round constants - CHK_IF_RETURN(cudaMemcpyAsync( - d_round_constants, round_constants, sizeof(S) * round_constants_len, cudaMemcpyHostToDevice, stream)); - - // Make sure all the constants have been copied - CHK_IF_RETURN(cudaStreamSynchronize(stream)); - *poseidon_constants = {width, alpha, internal_rounds, external_rounds, d_round_constants, d_internal_matrix, - mds_type, diffusion}; - - return CHK_LAST(); - } - - template - cudaError_t init_poseidon2_constants( - int width, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx, - Poseidon2Constants* poseidon2_constants) - { - CHK_INIT_IF_RETURN(); - -#define P2_CONSTANTS_DEF(width) \ - case width: \ - internal_rounds = t##width::internal_rounds; \ - round_constants = t##width::round_constants; \ - internal_matrix = t##width::mat_diag_m_1; \ - alpha = t##width::alpha; \ - break; - - int alpha; - int external_rounds = EXTERNAL_ROUNDS_DEFAULT; - int internal_rounds; - unsigned char* round_constants; - unsigned char* internal_matrix; - switch (width) { - P2_CONSTANTS_DEF(2) - P2_CONSTANTS_DEF(3) - P2_CONSTANTS_DEF(4) - P2_CONSTANTS_DEF(8) - P2_CONSTANTS_DEF(12) - P2_CONSTANTS_DEF(16) - P2_CONSTANTS_DEF(20) - P2_CONSTANTS_DEF(24) - default: - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "init_poseidon2_constants: #width must be one of [2, 3, 4, 8, 12, 16, 20, 24]"); - } - S* h_round_constants = reinterpret_cast(round_constants); - S* h_internal_matrix = reinterpret_cast(internal_matrix); - - create_poseidon2_constants( - width, alpha, internal_rounds, external_rounds, h_round_constants, h_internal_matrix, mds_type, diffusion, ctx, - poseidon2_constants); - - return CHK_LAST(); - } - - template - cudaError_t release_poseidon2_constants(Poseidon2Constants* constants, device_context::DeviceContext& ctx) - { - CHK_INIT_IF_RETURN(); - CHK_IF_RETURN(cudaFreeAsync(constants->internal_matrix_diag, ctx.stream)); - - constants->alpha = 0; - constants->width = 0; - constants->external_rounds = 0; - constants->internal_rounds = 0; - constants->round_constants = nullptr; - constants->internal_matrix_diag = nullptr; - return CHK_LAST(); - } -} // namespace poseidon2 \ No newline at end of file diff --git a/icicle/src/poseidon2/extern.cu b/icicle/src/poseidon2/extern.cu deleted file mode 100644 index 7741d29a4..000000000 --- a/icicle/src/poseidon2/extern.cu +++ /dev/null @@ -1,73 +0,0 @@ -#include "utils/utils.h" - -#include "fields/field_config.cuh" -using namespace field_config; - -#include "gpu-utils/error_handler.cuh" -#include "poseidon2/poseidon2.cuh" -#include "./constants.cu" - -namespace poseidon2 { - template class Poseidon2; - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon2_create_cuda)( - Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - unsigned int alpha, - unsigned int internal_rounds, - unsigned int external_rounds, - const scalar_t* round_constants, - const scalar_t* internal_matrix_diag, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx) - { - try { - *poseidon = new Poseidon2( - width, rate, alpha, internal_rounds, external_rounds, round_constants, internal_matrix_diag, mds_type, - diffusion, ctx); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon2_load_cuda)( - Poseidon2** poseidon, - unsigned int width, - unsigned int rate, - MdsType mds_type, - DiffusionStrategy diffusion, - device_context::DeviceContext& ctx) - { - try { - *poseidon = new Poseidon2(width, rate, mds_type, diffusion, ctx); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon2_hash_many_cuda)( - const Poseidon2* poseidon, - const scalar_t* inputs, - scalar_t* output, - unsigned int number_of_states, - unsigned int input_block_len, - unsigned int output_len, - hash::HashConfig& cfg) - { - return poseidon->hash_many(inputs, output, number_of_states, input_block_len, output_len, cfg); - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, poseidon2_delete_cuda)(Poseidon2* poseidon) - { - try { - poseidon->~Poseidon2(); - return cudaError_t::cudaSuccess; - } catch (const IcicleError& _error) { - return cudaError_t::cudaErrorUnknown; - } - } -} // namespace poseidon2 \ No newline at end of file diff --git a/icicle/src/poseidon2/test.cu b/icicle/src/poseidon2/test.cu deleted file mode 100644 index c98bfd63e..000000000 --- a/icicle/src/poseidon2/test.cu +++ /dev/null @@ -1,1100 +0,0 @@ -#include "gpu-utils/device_context.cuh" - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include - -#include "poseidon2/poseidon2.cuh" -using namespace poseidon2; - -#include "api/bn254.h" -using namespace bn254; - -#define T 3 - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - // Load poseidon - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - Poseidon2 poseidon(T, T, MdsType::DEFAULT_MDS, DiffusionStrategy::DEFAULT_DIFFUSION, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - START_TIMER(allocation_timer); - // Prepare input data of [0, 1, 2 ... (number_of_blocks * arity) - 1] - int number_of_blocks = argc > 1 ? 1 << atoi(argv[1]) : 1024; - scalar_t input = scalar_t::zero(); - scalar_t* in_ptr = static_cast(malloc(number_of_blocks * T * sizeof(scalar_t))); - for (uint32_t i = 0; i < number_of_blocks * T; i++) { - in_ptr[i] = input; - input = input + scalar_t::one(); - } - END_TIMER(allocation_timer, "Allocate mem and fill input"); - - scalar_t* out_ptr = static_cast(malloc(number_of_blocks * sizeof(scalar_t))); - - HashConfig cfg = default_hash_config(); - - START_TIMER(poseidon_timer); - poseidon.hash_many(in_ptr, out_ptr, number_of_blocks, T, 1, cfg); - END_TIMER(poseidon_timer, "Poseidon") - - // for (int i = 0; i < number_of_blocks; i++) { - // std::cout << "{"; - // for (int j = 0; j < 8; j++) { - // std::cout << ((uint32_t*)&out_ptr[i].limbs_storage)[j]; - // if (j != 7) { std::cout << ", "; } - // } - // std::cout << "}," << std::endl; - // } - - scalar_t expected[1024] = { - {1330262067, 1207394309, 2844697848, 2167681869, 2187661106, 3165754010, 3670693358, 196484388}, - {2236392765, 600060750, 920367053, 4125622762, 428886165, 3752903772, 92553574, 608728004}, - {53079953, 2044994699, 1612837193, 3764112501, 921018970, 215015938, 4218629082, 313892139}, - {240632961, 2431092835, 2770597124, 2848675875, 19279134, 2161114546, 3258041613, 195237450}, - {1144444226, 1816524584, 1400436280, 1310534496, 3335237601, 672832907, 2619262577, 517424659}, - {4001299929, 24782803, 2915500963, 503076019, 1587499556, 1016033679, 4048754075, 703614866}, - {3870416371, 3500958645, 3196065382, 1787860069, 2653283160, 3975435483, 811024008, 619872398}, - {3257385449, 10709933, 3512509755, 1525391636, 393333600, 2625894693, 1001942429, 566742296}, - {2212264614, 2485795935, 2871857667, 4085934335, 3896342670, 67629647, 3021072388, 717653957}, - {1459682963, 2203507019, 783146303, 240296772, 3678756997, 534595114, 1364856586, 236470007}, - {159320223, 3066923011, 3905929927, 813751638, 1272105506, 2054561423, 558073897, 491148798}, - {354125696, 870160205, 2498994790, 2533361477, 2594002109, 2471904983, 3904457723, 247186446}, - {154016665, 347050547, 4150214044, 775724788, 808085303, 1489183822, 1909870203, 27059986}, - {972853074, 1813315419, 1218883667, 3914855854, 4270798551, 2493057338, 2578375822, 439202350}, - {376195724, 2511373605, 1225987496, 267171352, 1188726291, 2225914597, 3934663048, 763202568}, - {516164134, 4219992554, 748812227, 2633513342, 4219133415, 1671232764, 2259242475, 110959844}, - {1920450427, 1085278665, 4064798364, 3930912353, 3005811270, 1369983266, 4090712151, 390171389}, - {3188088228, 2633073243, 1132503960, 1491856560, 1530847088, 115748594, 248454518, 804888656}, - {764457179, 2602251903, 2106284557, 2531999946, 3447126725, 3070990175, 1649968581, 554065178}, - {1645305436, 2517441714, 80447348, 1055149976, 3222032642, 4049107922, 515266657, 686284948}, - {3108525115, 1952169072, 2988179707, 1092867311, 3539146673, 1227133614, 1396060956, 566905942}, - {1898939116, 361181927, 3337680016, 3050453578, 657871993, 3839721684, 2488117256, 770463329}, - {1030661856, 3250959640, 4098817638, 1328508594, 3911280090, 2003468409, 3112136565, 189270617}, - {302332641, 1486583472, 3601938499, 682617123, 4102868379, 3108200778, 4174515428, 441007099}, - {1429776541, 2322278664, 1963150721, 2926568443, 2731188573, 709744840, 1119993632, 770041417}, - {1617401597, 2258449853, 3538375956, 2852084751, 3021082981, 3801835429, 494811601, 134759927}, - {2371193840, 3523588452, 285262742, 707943575, 1335467882, 1823758325, 2514037136, 314698820}, - {4042844027, 1516042613, 1569764209, 3494627160, 1500404905, 1740680576, 1153051103, 357511930}, - {3547546351, 755259386, 3710266810, 167683086, 1589791606, 2013271621, 93754638, 25455560}, - {1735887853, 2031684974, 2757257258, 2005044953, 1824078570, 1584413084, 252861025, 533479878}, - {1440181371, 3402955917, 203588234, 4163770690, 545720855, 4170893597, 1073246220, 15778079}, - {2070200352, 3689652800, 1818973571, 2757568210, 655600433, 3731355331, 2142658411, 69626968}, - {259320585, 1029575889, 3428555912, 1840850479, 2240920968, 1307550458, 4109509704, 281799105}, - {3782106986, 3685894266, 4114719647, 89348843, 1542895027, 3733705869, 3692528823, 23538209}, - {801026714, 1610445083, 1972544505, 3067387081, 3031268174, 2419944180, 3558632564, 530767436}, - {2369575494, 3398002242, 3235964161, 3678244557, 3296910014, 3451423645, 3788417501, 370412107}, - {1578941607, 632422277, 1626410362, 746283811, 1457067576, 2498872370, 2474369124, 436432360}, - {2660336025, 129614971, 3128569213, 355957430, 4111558851, 2534982597, 4057856642, 648328734}, - {888901348, 3555078824, 746766713, 24541328, 4035866800, 1037620242, 90800467, 284850855}, - {2221491636, 3528804395, 3161215674, 2706304467, 3908039646, 2179579913, 3686643738, 311637562}, - {1867797487, 2390104913, 3947097245, 720127936, 3906743062, 3694596253, 680248757, 709035685}, - {944902977, 785134558, 2731605584, 1277565960, 3911439092, 2595800391, 820009005, 216709515}, - {3922104899, 2694011057, 3845262823, 2014754633, 2481027977, 3672224411, 345853385, 14027699}, - {318282882, 3005990165, 3318368609, 1405064701, 888227771, 1633057223, 3795555193, 110001398}, - {1252030505, 1510730744, 4262164225, 872221794, 2823867413, 1090799591, 865846185, 86876422}, - {3419732116, 143466471, 3846374892, 1466869364, 1142359350, 1629016503, 3037319135, 187456818}, - {2424300209, 2652881349, 855063117, 729564982, 3477904713, 2438727071, 3386878946, 384319861}, - {346392377, 3037166087, 3971769069, 1309527314, 23335737, 1184515570, 25131896, 662780359}, - {740417200, 1977795293, 1327849709, 762039685, 1202558175, 4224025570, 2986249505, 310285851}, - {3733533423, 353831281, 3249939540, 3506197874, 448208918, 3078472744, 804829244, 286032252}, - {739919545, 3664337679, 3835663909, 2286078749, 3774674816, 2856492400, 1775855762, 453250427}, - {3486338536, 2639357945, 2545898989, 4005908631, 2092803172, 2576973083, 4135351675, 791626014}, - {4151271088, 3901149876, 3936019577, 2488370525, 1942895942, 3337315606, 934590515, 713140378}, - {2805044120, 261048936, 1041641539, 2075596896, 3984919901, 1294021373, 1657626134, 170203795}, - {2684724033, 307299828, 3739463933, 2459954236, 670978081, 1082744070, 3456647196, 109587474}, - {1474701256, 2924412102, 2875054509, 4145340141, 1798353896, 2932260442, 940685862, 234727314}, - {971875683, 2590911298, 1469165255, 3819523328, 2232525082, 2345748391, 757801873, 207608628}, - {2938172150, 921509004, 1991408720, 1660920415, 3875783009, 3104339729, 1448942190, 27348821}, - {185962586, 4105692341, 1472112852, 4098195842, 2001439162, 1063691209, 2442116042, 226143900}, - {3306000283, 883749023, 2670799983, 2777520226, 1837751396, 1856942575, 3497661033, 170100509}, - {3866716700, 2142594000, 1940403183, 3439717096, 1073068309, 3339941021, 2760049735, 625293661}, - {2915080529, 4121382646, 3028302365, 2036697084, 393231289, 1230446883, 3748992904, 777504476}, - {140800889, 223502574, 3854300724, 1499250194, 2634166602, 1858999910, 2198861963, 790108400}, - {1789336627, 117895160, 1048063453, 2074825413, 2265113230, 3879124072, 728325453, 70637459}, - {4180169324, 3101307653, 3413665448, 3868382944, 658211966, 1088134741, 4123322806, 396523527}, - {379432149, 2328664365, 914632872, 930657939, 2664026988, 968143760, 1011536700, 150740396}, - {4268541977, 395004233, 3901436952, 3195235118, 560033136, 324442250, 4042968724, 792767957}, - {4004656386, 781098846, 2205305163, 1896393417, 2890138150, 2948465434, 4176958849, 13154496}, - {4180051580, 4038466158, 1488241507, 842061845, 3049161244, 1450353066, 1031332932, 721893459}, - {1338337258, 4128724225, 3131385798, 2694851331, 3960563323, 1567344004, 2716378165, 209995913}, - {1977586748, 1369217488, 3993922476, 1654039010, 1365048306, 531190693, 1700206681, 523429568}, - {3902579180, 4211229041, 1537618794, 326440714, 3364025195, 2655708210, 1684827156, 504120104}, - {4147550620, 3900026905, 2769028012, 3480874313, 3137012469, 2489117402, 3312732199, 703133481}, - {965932430, 3128188484, 1953219338, 2569919972, 2584584078, 1251338152, 277710855, 723174290}, - {3662708486, 3034676012, 2622857619, 960603012, 3400381362, 1361056719, 1360090013, 371512642}, - {2746906091, 1787379388, 644757631, 965650528, 3941573626, 4074847753, 2787115514, 24830907}, - {2520122033, 3188949118, 2119123548, 4036391145, 938169934, 1908438801, 3183726538, 422040098}, - {3203285805, 2089177748, 1144477911, 3620315382, 3282078902, 1438065600, 1405801350, 551420478}, - {2603088710, 576958186, 2056196588, 136802801, 3419733766, 3827548802, 1201569385, 140054794}, - {564491297, 1584640653, 968754236, 906979133, 1089597987, 2946573851, 35445107, 60108779}, - {3313604055, 3387700136, 1971945961, 2632433136, 219768805, 1593315812, 3654669834, 122052669}, - {623789567, 3836895393, 1955781388, 4291477883, 758807826, 2656627995, 1852710988, 296150998}, - {3333309712, 895974491, 1389041341, 1098083085, 849625960, 3009669299, 805824342, 491747099}, - {875941243, 2511444264, 1333673009, 238060557, 3486570256, 1045190315, 515344987, 236019924}, - {3643619504, 2641292512, 214149033, 1341365870, 1093028952, 1404618391, 298203407, 797717040}, - {523029266, 3391100520, 851724507, 1440696181, 184627942, 707164924, 2575628332, 540351717}, - {3179703921, 862273365, 56858079, 1690092364, 3278694368, 252793818, 4106403922, 44476669}, - {1212569959, 3818568155, 484560970, 1510853316, 294260483, 1502007204, 3320093261, 666452108}, - {934062377, 2407003997, 1214066170, 501806021, 3182407720, 3235809024, 2510266228, 56521921}, - {2083784377, 77878658, 4136395901, 2489917051, 3624581680, 1733942175, 1551661674, 321690486}, - {786966688, 2697456846, 3379331505, 1807542412, 3858723323, 3828666076, 3474596227, 218365795}, - {119533502, 1088695972, 222122133, 1550678528, 1768300836, 2059219593, 2784521270, 230517209}, - {2697852283, 440128251, 3276871261, 2100506592, 2999770993, 273966875, 4244671887, 702596255}, - {2005908548, 3148366366, 84399879, 3418487451, 1768348423, 17224085, 1119138655, 655057214}, - {3164772611, 925732270, 1130829410, 1088878014, 400645026, 3160089487, 619150531, 264468503}, - {1150112239, 3495716392, 1301896261, 2250404584, 2708940386, 3833336355, 3351948298, 203193309}, - {753070873, 3035264139, 1804078603, 106847361, 2976175793, 3519137472, 2581440294, 495516644}, - {1074853302, 998954174, 3330080271, 3777083835, 1835530787, 2720801436, 3012397029, 297905393}, - {1886891144, 2424931612, 2551113092, 3303144698, 2942507875, 1908701482, 1425160780, 624487016}, - {1289627605, 4250309010, 2506793608, 211178228, 3362564747, 3065104348, 2803894598, 488586134}, - {3879181343, 2834005232, 132016662, 1333093964, 3523637782, 2895951165, 1252191248, 146883521}, - {3724720605, 3192846523, 518546700, 3389476320, 3146530121, 1302946216, 855574855, 310687701}, - {4007267181, 1424328682, 2077312669, 3913839902, 2237388684, 3346948774, 1420848914, 696861578}, - {1267136763, 2148978483, 1231752222, 2311819084, 3669480908, 471870847, 2391468968, 183152219}, - {2463109525, 3598355614, 4071843556, 578903964, 351059323, 1943352876, 3257486299, 4725844}, - {1921664859, 3793506919, 3947266066, 1215083185, 2724347230, 471819002, 3136941013, 281957823}, - {1463492753, 2066557266, 2979063114, 3421110894, 152915634, 2864470612, 4138627762, 562591346}, - {2805736963, 4184074337, 4090997024, 3175282783, 3205779689, 2461074705, 3949780078, 208674984}, - {2083622720, 3107549669, 1199553748, 3604769017, 2297035340, 2527073324, 4117286202, 83220498}, - {3988312818, 3668370141, 438178640, 249469429, 2168370965, 725322364, 693129472, 228304554}, - {596762951, 2268436688, 1695516855, 4101869954, 3325908636, 1083714944, 2133818510, 365382086}, - {2675002495, 2674608476, 880912295, 107803139, 1864541509, 2992464116, 1087763300, 45921074}, - {2434415545, 2433901123, 3595496809, 1569035236, 2793780781, 2513113263, 3504311318, 497145121}, - {735114691, 2069874646, 2716907026, 1596662587, 2307687803, 299963400, 250979365, 409002032}, - {2233859655, 1396444301, 2649392204, 1475895247, 5658348, 2438889220, 2742581223, 316888158}, - {344463616, 823021251, 3838323174, 3987836226, 2005957815, 2410558011, 1564864351, 73717447}, - {2085665835, 1327199901, 370902248, 1413066762, 4107579345, 405586777, 4033339553, 484063036}, - {2761141565, 1948480554, 1727578466, 1563334827, 2293943059, 4180364835, 1072123632, 353834111}, - {2141791875, 4128529696, 1497439698, 3008198623, 3518891901, 3315474205, 22965393, 523857645}, - {749389192, 1676656516, 2765179388, 4036019798, 28885493, 4181021362, 3254909637, 97221854}, - {1760984869, 1105179710, 2316017149, 125148063, 1865024055, 1270464494, 2109898001, 604572077}, - {2374922074, 717320342, 3033464457, 2793014509, 3185262628, 2437508150, 2377897732, 595689119}, - {3292619500, 3197421045, 1457587766, 1546683143, 2189857192, 3222046471, 1804794081, 208832831}, - {3525730487, 3853655412, 4153892471, 814877585, 1880437341, 659671587, 4089464156, 779230234}, - {780926, 1787445940, 399202445, 3904397447, 3530363980, 2484471520, 1387039016, 41389004}, - {3316070948, 2666015506, 1753241389, 1248106609, 2595410087, 3923044587, 3935502059, 161610473}, - {128337305, 1204362932, 1699144368, 3268536820, 448659947, 3002057824, 4098854530, 774013210}, - {3268489612, 1069969477, 2359779864, 522411728, 274525547, 207893542, 988240959, 126392874}, - {2726959297, 200679582, 4235639908, 3161358667, 1606108279, 3054483102, 4222042180, 218716087}, - {4178653205, 2863756274, 2543593986, 320112393, 1086146668, 2709526773, 207889362, 126320009}, - {1025901945, 1101882040, 3707218906, 3301669537, 3776605729, 3690071372, 1109720624, 545962543}, - {1174965022, 4012996672, 3185909048, 4094141660, 16180519, 2588645966, 2619567723, 495919204}, - {609527160, 775355464, 293807170, 1559721580, 826233259, 3777696456, 4178390116, 804322433}, - {4292776515, 3156270919, 3553375520, 4261893484, 4056131527, 2777750474, 4034120010, 347595242}, - {3819420131, 2364908359, 59183402, 1547428694, 310830735, 2046970846, 2654514846, 533904512}, - {295825520, 3388314649, 928336257, 2552985387, 3871500862, 996238672, 2258450807, 474531371}, - {1391171714, 3383713466, 1170722798, 1357165169, 2927152308, 3415062151, 2169293048, 743064476}, - {499196558, 4202942761, 1397272647, 1890354869, 3765532154, 2029014415, 1019134619, 298198360}, - {1948231225, 2044968606, 2445039953, 3855963460, 3045839048, 1074631595, 1511921026, 173477793}, - {3517044664, 3487930379, 2696814161, 2826851323, 495198648, 2911822566, 296426785, 726014678}, - {3944868604, 381069237, 2065494055, 983667803, 3585671813, 2600384445, 2532627636, 653346753}, - {638209103, 1990859745, 2510770250, 2877360435, 3930879604, 3571109570, 1079712045, 532964931}, - {169690164, 4228736802, 2628502721, 1263948838, 2851375698, 821518475, 4048160451, 479215775}, - {2504556352, 1638069193, 1640477594, 1070310698, 3608341261, 2367945167, 3540594774, 674109433}, - {71639129, 3314081255, 783244373, 4197555510, 797929110, 2668561179, 1471257939, 247129730}, - {3213971006, 2106498753, 4157449884, 550245530, 1847074011, 1468737606, 667785441, 720680018}, - {3356891880, 1468005459, 3938072224, 3687687966, 1361649507, 1788691405, 375466333, 707008835}, - {3066970206, 1044525202, 587594118, 123187887, 3189494582, 150311547, 594901349, 790543409}, - {947531444, 1365222080, 885795481, 1284718379, 656141567, 120705438, 690320618, 527923192}, - {3960058750, 1803033875, 3067648250, 2502579101, 3294380864, 1183716779, 1647651941, 736686268}, - {1438593711, 2790895589, 4086771482, 3826122150, 320864388, 707113930, 2451419527, 201881924}, - {2527236867, 3332489089, 3409309658, 3470543839, 959886777, 821322772, 1676260679, 459302439}, - {773135051, 2814302716, 1084266266, 2428838044, 1928074383, 3154854494, 387486246, 129438169}, - {3696422303, 3967060489, 3645380362, 1870474368, 3510864436, 3083405535, 2972396041, 224873366}, - {701278291, 938379886, 4032315092, 785482753, 1646177130, 2772382317, 1756516848, 150035716}, - 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{2991685069, 370072676, 1801343335, 3939927366, 533556254, 3460099817, 3626811103, 189847548}, - {544158540, 692544768, 2104902938, 194437846, 1545517236, 1345650773, 368870194, 604814212}, - {2267831451, 2342689297, 3029497490, 635796422, 313370717, 2208819618, 1936830542, 545179923}, - {1204069312, 162118119, 2304540202, 948141484, 2551583145, 3597458519, 2513742141, 125208380}, - {1595152083, 3892638617, 549840816, 2252399889, 3317891650, 3875364058, 414620482, 568479178}, - {1241085481, 2656387059, 2963923266, 4150774343, 3770411267, 4282380932, 232450342, 76966187}, - {4203385604, 2527564380, 3014770544, 2659456612, 1815799659, 1500650462, 4137106698, 332110358}, - {4203245377, 1407158826, 2210962765, 2404084829, 4234549103, 3485418953, 3373672533, 721825057}, - {1276600703, 466365582, 2373467282, 501424221, 1996770026, 3214722369, 29036755, 486982842}, - {4007331428, 933499895, 1826629588, 486263852, 1992359682, 1178560907, 2803259956, 181205313}, - {2735501369, 3047089899, 159554757, 1887661319, 4267060487, 1198704753, 1682117104, 46317092}, - {73802200, 1631385950, 1566817196, 2761880429, 894847607, 340186117, 1282432912, 548197887}, - {3021792345, 1565885488, 874346872, 962096747, 364734492, 2714350700, 1049014517, 440398419}, - {2057812450, 3715714502, 2624601698, 4287236073, 3119141580, 2906811182, 1123527827, 537888208}, - {2415538370, 872414306, 4156873540, 368838140, 3555846514, 211542189, 3617648261, 749725581}, - {143927639, 2402297145, 1874443352, 3195074622, 4102023538, 853380081, 1087880124, 233898744}, - {1740523175, 3242505699, 521460015, 3685268115, 2327276635, 3073883883, 2330877978, 795580498}, - {1760155380, 4024143843, 3039243352, 711680256, 1583541273, 3122177871, 3909200820, 607025917}, - {2582239983, 1794725826, 955371102, 2413673143, 393768164, 2607567850, 3090792987, 120882992}, - {1517867461, 3259044536, 2417828878, 1688019206, 4067667274, 2130202910, 3714714984, 691303128}, - {2456608441, 3288745736, 2107712096, 1046208946, 2361580709, 1049741184, 4220380399, 693587988}, - {3155328558, 3316681447, 1474793783, 2186205891, 1398183598, 1739357018, 2456255887, 594778236}}; - - if (number_of_blocks == 1024) { - for (int i = 0; i < number_of_blocks; i++) { -#ifdef DEBUG - // std::cout << out_ptr[i] << std::endl; -#endif - assert((out_ptr[i] == expected[i])); - } - printf("Expected output matches\n"); - } - - free(in_ptr); - free(out_ptr); -} - -#endif \ No newline at end of file diff --git a/icicle/src/poseidon2/test_m31.cu b/icicle/src/poseidon2/test_m31.cu deleted file mode 100644 index 378d281f0..000000000 --- a/icicle/src/poseidon2/test_m31.cu +++ /dev/null @@ -1,88 +0,0 @@ -#include "gpu-utils/device_context.cuh" - -#ifndef __CUDA_ARCH__ -#include -#include -#include -#include - -#include "poseidon2/poseidon2.cuh" -using namespace poseidon2; - -#include "fields/field_config.cuh" -using namespace field_config; - -#include "hash/hash.cuh" - -#define T 16 - -#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); -#define END_TIMER(timer, msg) \ - printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); - -int main(int argc, char* argv[]) -{ - using FpMilliseconds = std::chrono::duration; - using FpMicroseconds = std::chrono::duration; - - // Load poseidon - START_TIMER(timer_const); - device_context::DeviceContext ctx = device_context::get_default_device_context(); - Poseidon2 poseidon(T, T, MdsType::DEFAULT_MDS, DiffusionStrategy::DEFAULT_DIFFUSION, ctx); - END_TIMER(timer_const, "Load poseidon constants"); - - int number_of_blocks = argc > 1 ? 1 << atoi(argv[1]) : 1024; - scalar_t* in_ptr = static_cast(malloc(number_of_blocks * T * sizeof(scalar_t))); - scalar_t* out_ptr = static_cast(malloc(number_of_blocks * sizeof(scalar_t))); - scalar_t input = scalar_t::zero(); - - hash::HashConfig cfg = hash::default_hash_config(); - - size_t number_of_repetitions = argc > 2 ? 1 << atoi(argv[2]) : 32; - - // Prepare input data of [0, 1, 2 ... (number_of_blocks * arity) - 1] - for (uint32_t i = 0; i < number_of_blocks * T; i++) { - in_ptr[i] = input; - input = input + scalar_t::one(); - } - - // Warm up - poseidon.hash_many(in_ptr, out_ptr, number_of_blocks, T, 1, cfg); - - auto total_time_start = std::chrono::high_resolution_clock::now(); - size_t avg_time = 0; - for (int i = 0; i < number_of_repetitions; i++) { - auto poseidon_start = std::chrono::high_resolution_clock::now(); - poseidon.hash_many(in_ptr, out_ptr, number_of_blocks, T, 1, cfg); - avg_time += FpMilliseconds(std::chrono::high_resolution_clock::now() - poseidon_start).count(); - } - auto total_time = FpMilliseconds(std::chrono::high_resolution_clock::now() - total_time_start).count(); - - std::cout << "Block size: " << number_of_blocks << std::endl; - std::cout << "Total time: " << total_time << " ms" << std::endl; - std::cout << "Avg time: " << avg_time / number_of_repetitions << " ms" << std::endl; - - // for (int i = 0; i < number_of_blocks; i++) { - // std::cout << "{"; - // for (int j = 0; j < 8; j++) { - // std::cout << ((uint32_t*)&out_ptr[i].limbs_storage)[j]; - // if (j != 7) { std::cout << ", "; } - // } - // std::cout << "}," << std::endl; - // } - - if (number_of_blocks == 1024) { - for (int i = 0; i < number_of_blocks; i++) { -#ifdef DEBUG - // std::cout << out_ptr[i] << std::endl; -#endif - // assert((out_ptr[i] == expected[i])); - } - printf("Expected output matches\n"); - } - - free(in_ptr); - free(out_ptr); -} - -#endif \ No newline at end of file diff --git a/icicle/src/poseidon2/test_poseidon_m31 b/icicle/src/poseidon2/test_poseidon_m31 deleted file mode 100755 index 3358ecbcb..000000000 Binary files a/icicle/src/poseidon2/test_poseidon_m31 and /dev/null differ diff --git a/icicle/src/runtime.cpp b/icicle/src/runtime.cpp new file mode 100644 index 000000000..007d987e9 --- /dev/null +++ b/icicle/src/runtime.cpp @@ -0,0 +1,360 @@ +#include +#include +#include +#include +#include +#include + +#include "icicle/runtime.h" +#include "icicle/device_api.h" +#include "icicle/errors.h" +#include "icicle/utils/log.h" + +using namespace icicle; + +extern "C" eIcicleError icicle_set_device(const Device& device) { return DeviceAPI::set_thread_local_device(device); } + +extern "C" eIcicleError icicle_get_active_device(icicle::Device& device) +{ + const Device& active_device = DeviceAPI::get_thread_local_device(); + device = active_device; + return eIcicleError::SUCCESS; +} + +extern "C" eIcicleError icicle_is_host_memory(const void* ptr) +{ + auto it = DeviceAPI::get_global_memory_tracker().identify_device(ptr); + return (it == std::nullopt) ? eIcicleError::SUCCESS : eIcicleError::INVALID_POINTER; +} + +extern "C" eIcicleError icicle_is_active_device_memory(const void* ptr) +{ + auto it = DeviceAPI::get_global_memory_tracker().identify_device(ptr); + if (it == std::nullopt) { return eIcicleError::INVALID_POINTER; } + return (**it == DeviceAPI::get_thread_local_device()) ? eIcicleError::SUCCESS : eIcicleError::INVALID_POINTER; +} + +extern "C" eIcicleError icicle_get_device_count(int& device_count /*OUT*/) +{ + return DeviceAPI::get_thread_local_deviceAPI()->get_device_count(device_count); +} + +extern "C" eIcicleError icicle_malloc(void** ptr, size_t size) +{ + auto err = DeviceAPI::get_thread_local_deviceAPI()->allocate_memory(ptr, size); + if (err == eIcicleError::SUCCESS) { + DeviceAPI::get_global_memory_tracker().add_allocation(*ptr, size, DeviceAPI::get_thread_local_device()); + } + return err; +} + +extern "C" eIcicleError icicle_malloc_async(void** ptr, size_t size, icicleStreamHandle stream) +{ + auto err = DeviceAPI::get_thread_local_deviceAPI()->allocate_memory_async(ptr, size, stream); + if (err == eIcicleError::SUCCESS) { + DeviceAPI::get_global_memory_tracker().add_allocation(*ptr, size, DeviceAPI::get_thread_local_device()); + } + return err; +} + +extern "C" eIcicleError icicle_free(void* ptr) +{ + // If releasing memory of non-active device, switch device, release and switch back + // Alternatively I would have to consider it an error but it means that user has to switch device before dropping + // (when scope is closed) + + auto& tracker = DeviceAPI::get_global_memory_tracker(); + const auto& ptr_dev = tracker.identify_device(ptr); + const auto& cur_device = DeviceAPI::get_thread_local_device(); + + if (ptr_dev == std::nullopt) { + ICICLE_LOG_ERROR << "Trying to release host memory from device " << cur_device.type << " " << cur_device.id; + return eIcicleError::INVALID_DEVICE; + } + const bool is_active_device = **ptr_dev == cur_device; + if (is_active_device) { + auto err = DeviceAPI::get_thread_local_deviceAPI()->free_memory(ptr); + if (err == eIcicleError::SUCCESS) { DeviceAPI::get_global_memory_tracker().remove_allocation(ptr); } + return err; + } + + // Getting here means memory does not belong to active device + auto err = icicle_set_device(**ptr_dev); + if (err == eIcicleError::SUCCESS) err = icicle_free(ptr); + err = icicle_set_device(cur_device); + return err; +} + +extern "C" eIcicleError icicle_free_async(void* ptr, icicleStreamHandle stream) +{ + auto& tracker = DeviceAPI::get_global_memory_tracker(); + const auto& ptr_dev = tracker.identify_device(ptr); + const auto& cur_device = DeviceAPI::get_thread_local_device(); + + if (ptr_dev == std::nullopt) { + ICICLE_LOG_ERROR << "Trying to release host memory from device " << cur_device.type << " " << cur_device.id; + return eIcicleError::INVALID_DEVICE; + } + + // Note that in that case, not switching device, since the stream may be wrong too. User has to handle it. + const bool is_active_device = **ptr_dev == cur_device; + if (!is_active_device) { + ICICLE_LOG_ERROR << "Trying to release memory allocated by " << (**ptr_dev).type << "(" << (**ptr_dev).id + << ") from device " << cur_device.type << "(" << cur_device.id << ")"; + return eIcicleError::INVALID_DEVICE; + } + + auto err = DeviceAPI::get_thread_local_deviceAPI()->free_memory_async(ptr, stream); + if (err == eIcicleError::SUCCESS) { DeviceAPI::get_global_memory_tracker().remove_allocation(ptr); } + return err; +} + +extern "C" eIcicleError icicle_get_available_memory(size_t& total /*OUT*/, size_t& free /*OUT*/) +{ + return DeviceAPI::get_thread_local_deviceAPI()->get_available_memory(total, free); +} + +extern "C" eIcicleError icicle_memset(void* ptr, int value, size_t size) +{ + if (eIcicleError::SUCCESS == icicle_is_active_device_memory(ptr)) { + return DeviceAPI::get_thread_local_deviceAPI()->memset(ptr, value, size); + } + ICICLE_LOG_ERROR << "icicle_memset API not expecting host memory"; + return eIcicleError::INVALID_POINTER; +} + +extern "C" eIcicleError icicle_memset_async(void* ptr, int value, size_t size, icicleStreamHandle stream) +{ + if (eIcicleError::SUCCESS == icicle_is_active_device_memory(ptr)) { + return DeviceAPI::get_thread_local_deviceAPI()->memset_async(ptr, value, size, stream); + } + ICICLE_LOG_ERROR << "icicle_memset_async API not expecting host memory"; + return eIcicleError::INVALID_POINTER; +} + +/** + * @brief Enum for specifying the type of memory. + */ +enum class MemoryType { + Untracked, ///< Memory is not tracked, assumed to be host memory + ActiveDevice, ///< Memory is on the active device + NonActiveDevice, ///< Memory is on a non-active device +}; + +static MemoryType _get_memory_type(const void* ptr) +{ + auto it = DeviceAPI::get_global_memory_tracker().identify_device(ptr); + // Untracked address assumed to be host memory but could be invalid + if (it == std::nullopt) { return MemoryType::Untracked; } + + const bool is_active_device_ptr = (**it == DeviceAPI::get_thread_local_device()); + return is_active_device_ptr ? MemoryType::ActiveDevice : MemoryType::NonActiveDevice; +} + +static eIcicleError _determine_copy_direction(void* dst, const void* src, eCopyDirection& direction) +{ + // Determine the type of memory for dst and src + MemoryType dstType = _get_memory_type(dst); + MemoryType srcType = _get_memory_type(src); + + // Validate memory combinations + if (dstType == MemoryType::Untracked && srcType == MemoryType::Untracked) { + ICICLE_LOG_ERROR << "Host to Host copy, not handled by DeviceAPI"; + return eIcicleError::INVALID_POINTER; + } + if (dstType == MemoryType::NonActiveDevice || srcType == MemoryType::NonActiveDevice) { + ICICLE_LOG_ERROR << "Either dst or src is on a non-active device memory"; + return eIcicleError::INVALID_POINTER; + } + + // Determine the copy direction + direction = srcType == MemoryType::ActiveDevice && dstType == MemoryType::Untracked ? eCopyDirection::DeviceToHost + : srcType == MemoryType::Untracked && dstType == MemoryType::ActiveDevice ? eCopyDirection::HostToDevice + : srcType == MemoryType::ActiveDevice && dstType == MemoryType::ActiveDevice + ? eCopyDirection::DeviceToDevice + : eCopyDirection::HostToDevice; // This line should never be reached + + return eIcicleError::SUCCESS; +} + +extern "C" eIcicleError icicle_copy(void* dst, const void* src, size_t size) +{ + eCopyDirection direction; + auto err = _determine_copy_direction(dst, src, direction); + if (eIcicleError::SUCCESS != err) { return err; } + // Call the appropriate copy method + return DeviceAPI::get_thread_local_deviceAPI()->copy(dst, src, size, direction); +} + +extern "C" eIcicleError icicle_copy_async(void* dst, const void* src, size_t size, icicleStreamHandle stream) +{ + eCopyDirection direction; + auto err = _determine_copy_direction(dst, src, direction); + if (eIcicleError::SUCCESS != err) { return err; } + // Call the appropriate copy method + return DeviceAPI::get_thread_local_deviceAPI()->copy_async(dst, src, size, direction, stream); +} + +extern "C" eIcicleError icicle_copy_to_host(void* dst, const void* src, size_t size) +{ + return DeviceAPI::get_thread_local_deviceAPI()->copy(dst, src, size, eCopyDirection::DeviceToHost); +} + +extern "C" eIcicleError icicle_copy_to_host_async(void* dst, const void* src, size_t size, icicleStreamHandle stream) +{ + return DeviceAPI::get_thread_local_deviceAPI()->copy_async(dst, src, size, eCopyDirection::DeviceToHost, stream); +} + +extern "C" eIcicleError icicle_copy_to_device(void* dst, const void* src, size_t size) +{ + return DeviceAPI::get_thread_local_deviceAPI()->copy(dst, src, size, eCopyDirection::HostToDevice); +} + +extern "C" eIcicleError icicle_copy_to_device_async(void* dst, const void* src, size_t size, icicleStreamHandle stream) +{ + return DeviceAPI::get_thread_local_deviceAPI()->copy_async(dst, src, size, eCopyDirection::HostToDevice, stream); +} + +extern "C" eIcicleError icicle_stream_synchronize(icicleStreamHandle stream) +{ + return DeviceAPI::get_thread_local_deviceAPI()->synchronize(stream); +} + +extern "C" eIcicleError icicle_device_synchronize() +{ + return DeviceAPI::get_thread_local_deviceAPI()->synchronize(nullptr); +} + +extern "C" eIcicleError icicle_get_device_properties(DeviceProperties& properties) +{ + return DeviceAPI::get_thread_local_deviceAPI()->get_device_properties(properties); +} + +extern "C" eIcicleError icicle_is_device_available(const Device& dev) +{ + return is_device_registered(dev.type) ? eIcicleError::SUCCESS : eIcicleError::INVALID_DEVICE; +} + +extern "C" eIcicleError icicle_get_registered_devices(char* output, size_t output_size) +{ + return get_registered_devices(output, output_size); +} + +extern "C" eIcicleError icicle_create_stream(icicleStreamHandle* stream) +{ + return DeviceAPI::get_thread_local_deviceAPI()->create_stream(stream); +} + +extern "C" eIcicleError icicle_destroy_stream(icicleStreamHandle stream) +{ + return DeviceAPI::get_thread_local_deviceAPI()->destroy_stream(stream); +} + +// Determine the shared library extension based on the operating system +#ifdef __linux__ +const std::string SHARED_LIB_EXTENSION = ".so"; +#elif __APPLE__ +const std::string SHARED_LIB_EXTENSION = ".dylib"; +#else +#error "Unsupported operating system" +#endif + +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive) +{ + auto is_directory = [](const char* path) { + struct stat pathStat; + if (stat(path, &pathStat) != 0) { return false; } + return S_ISDIR(pathStat.st_mode); + }; + + auto is_shared_library = [](const std::string& filename) { + return filename.size() >= SHARED_LIB_EXTENSION.size() && + filename.compare( + filename.size() - SHARED_LIB_EXTENSION.size(), SHARED_LIB_EXTENSION.size(), SHARED_LIB_EXTENSION) == 0; + }; + + auto load_library = [](const char* filePath) { + // Convert the file path to a std::string for easier manipulation + std::string path(filePath); + + // Extract the library name from the full path + std::string fileName = path.substr(path.find_last_of("/\\") + 1); + + // Check if the library name contains "icicle_backend" to avoid loading + if (fileName.find("icicle_backend") == std::string::npos) { + ICICLE_LOG_VERBOSE << "Skipping: " << filePath << " - Not an Icicle backend library."; + return; + } + + // Check if the library name contains "device". If yes, load it with GLOBAL visibility, otherwise LOCAL. + // The logic behind it is to avoid symbol conflicts by using LOCAL visibility but allow backends to expose symbols + // to the other backend libs. For example to reuse some device context or any initialization required by APIs that + // we want to do once. + int flags = (fileName.find("device") != std::string::npos) ? (RTLD_LAZY | RTLD_GLOBAL) : (RTLD_LAZY | RTLD_LOCAL); + + // Attempt to load the library with the appropriate flags + ICICLE_LOG_VERBOSE << "Attempting to load: " << filePath; + void* handle = dlopen(filePath, flags); + if (!handle) { ICICLE_LOG_DEBUG << "Failed to load " << filePath << ": " << dlerror(); } + }; + + if (is_directory(path)) { + // Path is a directory, recursively search for libraries + DIR* dir = opendir(path); + if (!dir) { + ICICLE_LOG_ERROR << "Cannot open directory: " << path; + return eIcicleError::INVALID_ARGUMENT; + } + + struct dirent* entry; + while ((entry = readdir(dir)) != nullptr) { + const std::string& entryPath = std::string(path) + "/" + entry->d_name; + + // Skip "." and ".." entries + if (std::string(entry->d_name) == "." || std::string(entry->d_name) == "..") { continue; } + + // Recurse into subdirectories and load libraries in files + const bool is_nested_dir = is_directory(entryPath.c_str()); + if (is_recursive || !is_nested_dir) { icicle_load_backend(entryPath.c_str(), is_recursive); } + } + + closedir(dir); + } else if (is_shared_library(path)) { + load_library(path); + } + + return eIcicleError::SUCCESS; +} + +extern "C" eIcicleError icicle_load_backend_from_env_or_default() +{ + // First, check the environment variable + const char* env_dir = std::getenv("ICICLE_BACKEND_INSTALL_DIR"); + if (env_dir && std::filesystem::exists(env_dir)) { + // Attempt to load the backend from the environment variable directory + eIcicleError result = icicle_load_backend(env_dir, true /*=recursive*/); + if (result == eIcicleError::SUCCESS) { + ICICLE_LOG_INFO << "ICICLE backend loaded from $ICICLE_BACKEND_INSTALL_DIR=" << env_dir; + return result; + } else { + ICICLE_LOG_WARNING << "Loading ICICLE backend from $ICICLE_BACKEND_INSTALL_DIR=" << env_dir + << " resulted in an error"; + } + } + + // If not found or failed, fall back to the default directory + const std::string default_dir = "/opt/icicle/lib/backend"; + if (std::filesystem::exists(default_dir)) { + eIcicleError result = icicle_load_backend(default_dir.c_str(), true /*=recursive*/); + if (result == eIcicleError::SUCCESS) { + ICICLE_LOG_INFO << "ICICLE backend loaded from " << default_dir; + return result; + } else { + ICICLE_LOG_WARNING << "Loading ICICLE backend from " << default_dir << " resulted in an error"; + } + } + + // If neither works, return a failure status + ICICLE_LOG_INFO << "Failed to load backend from any known directory."; + return eIcicleError::BACKEND_LOAD_FAILED; +} \ No newline at end of file diff --git a/icicle/src/vec_ops.cpp b/icicle/src/vec_ops.cpp new file mode 100644 index 000000000..d42fa0dca --- /dev/null +++ b/icicle/src/vec_ops.cpp @@ -0,0 +1,399 @@ +#include "icicle/backend/vec_ops_backend.h" +#include "icicle/dispatcher.h" + +namespace icicle { + + /*********************************** ADD ***********************************/ + ICICLE_DISPATCHER_INST(VectorAddDispatcher, vector_add, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, vector_add)( + const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return VectorAddDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError + vector_add(const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, vector_add)(vec_a, vec_b, n, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(VectorAddExtFieldDispatcher, extension_vector_add, extFieldVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_vector_add)( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig* config, extension_t* output) + { + return VectorAddExtFieldDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError vector_add( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_vector_add)(vec_a, vec_b, n, &config, output); + } +#endif // EXT_FIELD + + /*********************************** ACCUMULATE ***********************************/ + ICICLE_DISPATCHER_INST(VectorAccumulateDispatcher, vector_accumulate, scalarVectorOpImplInplaceA); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, vector_accumulate)( + scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config) + { + return VectorAccumulateDispatcher::execute(vec_a, vec_b, n, *config); + } + + template <> + eIcicleError vector_accumulate(scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config) + { + return CONCAT_EXPAND(FIELD, vector_accumulate)(vec_a, vec_b, n, &config); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(VectorAccumulateExtFieldDispatcher, extension_vector_accumulate, extFieldVectorOpImplInplaceA); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_vector_accumulate)( + extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig* config) + { + return VectorAccumulateExtFieldDispatcher::execute(vec_a, vec_b, n, *config); + } + + template <> + eIcicleError vector_accumulate(extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig& config) + { + return CONCAT_EXPAND(FIELD, extension_vector_accumulate)(vec_a, vec_b, n, &config); + } +#endif // EXT_FIELD + + /*********************************** SUB ***********************************/ + ICICLE_DISPATCHER_INST(VectorSubDispatcher, vector_sub, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, vector_sub)( + const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return VectorSubDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError + vector_sub(const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, vector_sub)(vec_a, vec_b, n, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(VectorSubExtFieldDispatcher, extension_vector_sub, extFieldVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_vector_sub)( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig* config, extension_t* output) + { + return VectorSubExtFieldDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError vector_sub( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_vector_sub)(vec_a, vec_b, n, &config, output); + } +#endif // EXT_FIELD + + /*********************************** MUL ***********************************/ + ICICLE_DISPATCHER_INST(VectorMulDispatcher, vector_mul, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, vector_mul)( + const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return VectorMulDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError + vector_mul(const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, vector_mul)(vec_a, vec_b, n, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(VectorMulExtFieldDispatcher, extension_vector_mul, extFieldVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_vector_mul)( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig* config, extension_t* output) + { + return VectorMulExtFieldDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError vector_mul( + const extension_t* vec_a, const extension_t* vec_b, uint64_t n, const VecOpsConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_vector_mul)(vec_a, vec_b, n, &config, output); + } +#endif // EXT_FIELD + + /*********************************** DIV ***********************************/ + ICICLE_DISPATCHER_INST(VectorDivDispatcher, vector_div, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, vector_div)( + const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return VectorDivDispatcher::execute(vec_a, vec_b, n, *config, output); + } + + template <> + eIcicleError + vector_div(const scalar_t* vec_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, vector_div)(vec_a, vec_b, n, &config, output); + } + + /*********************************** (Scalar + Vector) ELEMENT WISE ***********************************/ + ICICLE_DISPATCHER_INST(ScalarAddDispatcher, scalar_add_vec, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, scalar_add_vec)( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return ScalarAddDispatcher::execute(scalar_a, vec_b, n, *config, output); + } + + template <> + eIcicleError scalar_add_vec( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, scalar_add_vec)(scalar_a, vec_b, n, &config, output); + } + + /*********************************** (Scalar - Vector) ELEMENT WISE ***********************************/ + ICICLE_DISPATCHER_INST(ScalarSubDispatcher, scalar_sub_vec, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, scalar_sub_vec)( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return ScalarSubDispatcher::execute(scalar_a, vec_b, n, *config, output); + } + + template <> + eIcicleError scalar_sub_vec( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, scalar_sub_vec)(scalar_a, vec_b, n, &config, output); + } + /*********************************** MUL BY SCALAR ***********************************/ + ICICLE_DISPATCHER_INST(ScalarMulDispatcher, scalar_mul_vec, scalarVectorOpImpl); + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, scalar_mul_vec)( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig* config, scalar_t* output) + { + return ScalarMulDispatcher::execute(scalar_a, vec_b, n, *config, output); + } + + template <> + eIcicleError scalar_mul_vec( + const scalar_t* scalar_a, const scalar_t* vec_b, uint64_t n, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, scalar_mul_vec)(scalar_a, vec_b, n, &config, output); + } + + /*********************************** CONVERT MONTGOMERY ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarConvertMontgomeryDispatcher, scalar_convert_montgomery, scalarConvertMontgomeryImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, scalar_convert_montgomery)( + const scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, scalar_t* output) + { + return ScalarConvertMontgomeryDispatcher::execute(input, size, is_into, *config, output); + } + + template <> + eIcicleError + convert_montgomery(const scalar_t* input, uint64_t size, bool is_into, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, scalar_convert_montgomery)(input, size, is_into, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST( + ExtFieldConvertMontgomeryDispatcher, extension_scalar_convert_montgomery, extFieldConvertMontgomeryImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_scalar_convert_montgomery)( + const extension_t* input, uint64_t size, bool is_into, const VecOpsConfig* config, extension_t* output) + { + return ExtFieldConvertMontgomeryDispatcher::execute(input, size, is_into, *config, output); + } + + template <> + eIcicleError convert_montgomery( + const extension_t* input, uint64_t size, bool is_into, const VecOpsConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_scalar_convert_montgomery)(input, size, is_into, &config, output); + } +#endif // EXT_FIELD + + /*********************************** BIT REVERSE ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarBitReverseDispatcher, scalar_bit_reverse, scalarBitReverseOpImpl) + + extern "C" eIcicleError + CONCAT_EXPAND(FIELD, bit_reverse)(const scalar_t* input, uint64_t size, const VecOpsConfig* config, scalar_t* output) + { + return ScalarBitReverseDispatcher::execute(input, size, *config, output); + } + + template <> + eIcicleError bit_reverse(const scalar_t* input, uint64_t size, const VecOpsConfig& config, scalar_t* output) + { + return CONCAT_EXPAND(FIELD, bit_reverse)(input, size, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(ExtFieldBitReverseDispatcher, extension_bit_reverse, extFieldBitReverseOpImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_bit_reverse)( + const extension_t* input, uint64_t size, const VecOpsConfig* config, extension_t* output) + { + return ExtFieldBitReverseDispatcher::execute(input, size, *config, output); + } + + template <> + eIcicleError bit_reverse(const extension_t* input, uint64_t size, const VecOpsConfig& config, extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_bit_reverse)(input, size, &config, output); + } +#endif // EXT_FIELD + + /*********************************** SLICE ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarSliceDispatcher, slice, scalarSliceOpImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, slice)( + const scalar_t* input, + uint64_t offset, + uint64_t stride, + uint64_t size, + const VecOpsConfig* config, + scalar_t* output) + { + return ScalarSliceDispatcher::execute(input, offset, stride, size, *config, output); + } + + template <> + eIcicleError slice( + const scalar_t* input, + uint64_t offset, + uint64_t stride, + uint64_t size, + const VecOpsConfig& config, + scalar_t* output) + { + return CONCAT_EXPAND(FIELD, slice)(input, offset, stride, size, &config, output); + } + +#ifdef EXT_FIELD + ICICLE_DISPATCHER_INST(ExtFieldSliceDispatcher, extension_slice, extFieldSliceOpImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, extension_slice)( + const extension_t* input, + uint64_t offset, + uint64_t stride, + uint64_t size, + const VecOpsConfig* config, + extension_t* output) + { + return ExtFieldSliceDispatcher::execute(input, offset, stride, size, *config, output); + } + + template <> + eIcicleError slice( + const extension_t* input, + uint64_t offset, + uint64_t stride, + uint64_t size, + const VecOpsConfig& config, + extension_t* output) + { + return CONCAT_EXPAND(FIELD, extension_slice)(input, offset, stride, size, &config, output); + } +#endif // EXT_FIELD + + /*********************************** HIGHEST NON ZERO IDX ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarHighestNonZeroIdxDispatcher, highest_non_zero_idx, scalarHighNonZeroIdxOpImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, highest_non_zero_idx)( + const scalar_t* input, uint64_t size, const VecOpsConfig* config, int64_t* out_idx /*OUT*/) + { + return ScalarHighestNonZeroIdxDispatcher::execute(input, size, *config, out_idx); + } + + template <> + eIcicleError + highest_non_zero_idx(const scalar_t* input, uint64_t size, const VecOpsConfig& config, int64_t* out_idx /*OUT*/) + { + return CONCAT_EXPAND(FIELD, highest_non_zero_idx)(input, size, &config, out_idx); + } + + /*********************************** POLY EVAL ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarPolyEvalDispatcher, poly_eval, scalarPolyEvalImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, poly_eval)( + const scalar_t* coeffs, + uint64_t coeffs_size, + const scalar_t* domain, + uint64_t domain_size, + const VecOpsConfig* config, + scalar_t* evals /*OUT*/) + { + return ScalarPolyEvalDispatcher::execute(coeffs, coeffs_size, domain, domain_size, *config, evals); + } + + template <> + eIcicleError polynomial_eval( + const scalar_t* coeffs, + uint64_t coeffs_size, + const scalar_t* domain, + uint64_t domain_size, + const VecOpsConfig& config, + scalar_t* evals /*OUT*/) + { + return CONCAT_EXPAND(FIELD, poly_eval)(coeffs, coeffs_size, domain, domain_size, &config, evals); + } + + /*********************************** POLY DIVISION ***********************************/ + + ICICLE_DISPATCHER_INST(ScalarPolyDivDispatcher, poly_division, scalarPolyDivImpl) + + extern "C" eIcicleError CONCAT_EXPAND(FIELD, poly_division)( + const scalar_t* numerator, + int64_t numerator_deg, + const scalar_t* denumerator, + int64_t denumerator_deg, + const VecOpsConfig* config, + scalar_t* q_out /*OUT*/, + uint64_t q_size, + scalar_t* r_out /*OUT*/, + uint64_t r_size) + { + return ScalarPolyDivDispatcher::execute( + numerator, numerator_deg, denumerator, denumerator_deg, *config, q_out, q_size, r_out, r_size); + } + + template <> + eIcicleError polynomial_division( + const scalar_t* numerator, + int64_t numerator_deg, + const scalar_t* denumerator, + int64_t denumerator_deg, + const VecOpsConfig& config, + scalar_t* q_out /*OUT*/, + uint64_t q_size, + scalar_t* r_out /*OUT*/, + uint64_t r_size) + { + return CONCAT_EXPAND(FIELD, poly_division)( + numerator, numerator_deg, denumerator, denumerator_deg, &config, q_out, q_size, r_out, r_size); + } + +} // namespace icicle \ No newline at end of file diff --git a/icicle/src/vec_ops/extern.cu b/icicle/src/vec_ops/extern.cu deleted file mode 100644 index 78a7467cf..000000000 --- a/icicle/src/vec_ops/extern.cu +++ /dev/null @@ -1,80 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "utils/utils.h" -#include "vec_ops.cu" - -namespace vec_ops { - /** - * Extern version of [Mul](@ref Mul) function with the template parameters - * `S` and `E` being the [field](@ref scalar_t) (either scalar field of the curve given by `-DCURVE` - * or standalone "STARK field" given by `-DFIELD`). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, mul_cuda)(scalar_t* vec_a, scalar_t* vec_b, int n, VecOpsConfig& config, scalar_t* result) - { - return mul(vec_a, vec_b, n, config, result); - } - - /** - * Extern version of [Add](@ref Add) function with the template parameter - * `E` being the [field](@ref scalar_t) (either scalar field of the curve given by `-DCURVE` - * or standalone "STARK field" given by `-DFIELD`). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, add_cuda)(scalar_t* vec_a, scalar_t* vec_b, int n, VecOpsConfig& config, scalar_t* result) - { - return add(vec_a, vec_b, n, config, result); - } - - /** - * Accumulate (as vec_a[i] += vec_b[i]) function with the template parameter - * `E` being the [field](@ref scalar_t) (either scalar field of the curve given by `-DCURVE` - * or standalone "STARK field" given by `-DFIELD`). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, accumulate_cuda)(scalar_t* vec_a, scalar_t* vec_b, int n, VecOpsConfig& config) - { - return add(vec_a, vec_b, n, config, vec_a); - } - - /** - * Extern version of [Sub](@ref Sub) function with the template parameter - * `E` being the [field](@ref scalar_t) (either scalar field of the curve given by `-DCURVE` - * or standalone "STARK field" given by `-DFIELD`). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, sub_cuda)(scalar_t* vec_a, scalar_t* vec_b, int n, VecOpsConfig& config, scalar_t* result) - { - return sub(vec_a, vec_b, n, config, result); - } - - /** - * Extern version of transpose_batch function with the template parameter - * `E` being the [field](@ref scalar_t) (either scalar field of the curve given by `-DCURVE` - * or standalone "STARK field" given by `-DFIELD`). - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, transpose_matrix_cuda)( - const scalar_t* input, - uint32_t row_size, - uint32_t column_size, - scalar_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async) - { - return transpose_matrix(input, output, row_size, column_size, ctx, on_device, is_async); - } - - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, bit_reverse_cuda)(const scalar_t* input, uint64_t n, BitReverseConfig& config, scalar_t* output) - { - return bit_reverse(input, n, config, output); - } -} // namespace vec_ops \ No newline at end of file diff --git a/icicle/src/vec_ops/extern_extension.cu b/icicle/src/vec_ops/extern_extension.cu deleted file mode 100644 index 80653c79c..000000000 --- a/icicle/src/vec_ops/extern_extension.cu +++ /dev/null @@ -1,76 +0,0 @@ -#include "fields/field_config.cuh" - -using namespace field_config; - -#include "utils/utils.h" -#include "vec_ops.cu" - -namespace vec_ops { - /** - * Extern version of [Mul](@ref Mul) function with the template parameters - * `S` and `E` being the [extension field](@ref extension_t) of the base field given by `-DFIELD` env variable - * during build. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_mul_cuda)( - extension_t* vec_a, extension_t* vec_b, int n, VecOpsConfig& config, extension_t* result) - { - return mul(vec_a, vec_b, n, config, result); - } - - /** - * Extern version of [Add](@ref Add) function with the template parameter - * `E` being the [extension field](@ref extension_t) of the base field given by `-DFIELD` env variable during build. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_add_cuda)( - extension_t* vec_a, extension_t* vec_b, int n, VecOpsConfig& config, extension_t* result) - { - return add(vec_a, vec_b, n, config, result); - } - - /** - * Accumulate (as vec_a[i] += vec_b[i]) function with the template parameter - * `E` being the [extension field](@ref extension_t) of the base field given by `-DFIELD` env variable during build. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t - CONCAT_EXPAND(FIELD, extension_accumulate_cuda)(extension_t* vec_a, extension_t* vec_b, int n, VecOpsConfig& config) - { - return add(vec_a, vec_b, n, config, vec_a); - } - - /** - * Extern version of [Sub](@ref Sub) function with the template parameter - * `E` being the [extension field](@ref extension_t) of the base field given by `-DFIELD` env variable during build. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_sub_cuda)( - extension_t* vec_a, extension_t* vec_b, int n, VecOpsConfig& config, extension_t* result) - { - return sub(vec_a, vec_b, n, config, result); - } - - /** - * Extern version of transpose_batch function with the template parameter - * `E` being the [extension field](@ref extension_t) of the base field given by `-DFIELD` env variable during build. - * @return `cudaSuccess` if the execution was successful and an error code otherwise. - */ - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_transpose_matrix_cuda)( - const extension_t* input, - uint32_t row_size, - uint32_t column_size, - extension_t* output, - device_context::DeviceContext& ctx, - bool on_device, - bool is_async) - { - return transpose_matrix(input, output, row_size, column_size, ctx, on_device, is_async); - } - - extern "C" cudaError_t CONCAT_EXPAND(FIELD, extension_bit_reverse_cuda)( - const extension_t* input, uint64_t n, BitReverseConfig& config, extension_t* output) - { - return bit_reverse(input, n, config, output); - } -} // namespace vec_ops \ No newline at end of file diff --git a/icicle/src/vec_ops/vec_ops.cu b/icicle/src/vec_ops/vec_ops.cu deleted file mode 100644 index 9cfa85e60..000000000 --- a/icicle/src/vec_ops/vec_ops.cu +++ /dev/null @@ -1,238 +0,0 @@ -#include -#include - -#include "vec_ops/vec_ops.cuh" -#include "gpu-utils/device_context.cuh" -#include "utils/mont.cuh" - -namespace vec_ops { - - namespace { - -#define MAX_THREADS_PER_BLOCK 256 - - template - __global__ void mul_kernel(const E* scalar_vec, const E* element_vec, int n, E* result) - { - int tid = blockDim.x * blockIdx.x + threadIdx.x; - if (tid < n) { result[tid] = scalar_vec[tid] * element_vec[tid]; } - } - - template - __global__ void mul_scalar_kernel(const E* element_vec, const S scalar, int n, E* result) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { result[tid] = element_vec[tid] * (scalar); } - } - - template - __global__ void div_element_wise_kernel(const E* element_vec1, const E* element_vec2, int n, E* result) - { - // TODO:implement better based on https://eprint.iacr.org/2008/199 - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { result[tid] = element_vec1[tid] * E::inverse(element_vec2[tid]); } - } - - template - __global__ void add_kernel(const E* element_vec1, const E* element_vec2, int n, E* result) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { result[tid] = element_vec1[tid] + element_vec2[tid]; } - } - - template - __global__ void sub_kernel(const E* element_vec1, const E* element_vec2, int n, E* result) - { - int tid = blockIdx.x * blockDim.x + threadIdx.x; - if (tid < n) { result[tid] = element_vec1[tid] - element_vec2[tid]; } - } - - template - __global__ void transpose_kernel(const E* in, E* out, uint32_t row_size, uint32_t column_size) - { - int tid = blockDim.x * blockIdx.x + threadIdx.x; - if (tid >= row_size * column_size) return; - out[(tid % row_size) * column_size + (tid / row_size)] = in[tid]; - } - - template - __global__ void bit_reverse_kernel(const E* input, uint64_t n, unsigned shift, E* output) - { - uint64_t tid = uint64_t(blockIdx.x) * blockDim.x + threadIdx.x; - // Handling arbitrary vector size - if (tid < n) { - int reversed_index = __brevll(tid) >> shift; - output[reversed_index] = input[tid]; - } - } - template - __global__ void bit_reverse_inplace_kernel(E* input, uint64_t n, unsigned shift) - { - uint64_t tid = uint64_t(blockIdx.x) * blockDim.x + threadIdx.x; - // Handling arbitrary vector size - if (tid < n) { - int reversed_index = __brevll(tid) >> shift; - if (reversed_index > tid) { - E temp = input[tid]; - input[tid] = input[reversed_index]; - input[reversed_index] = temp; - } - } - } - } // namespace - - template - cudaError_t vec_op(E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result) - { - CHK_INIT_IF_RETURN(); - - bool is_in_place = vec_a == result; - - // Set the grid and block dimensions - int num_threads = MAX_THREADS_PER_BLOCK; - int num_blocks = (n + num_threads - 1) / num_threads; - - E *d_result, *d_alloc_vec_a, *d_alloc_vec_b; - E* d_vec_a; - const E* d_vec_b; - if (!config.is_a_on_device) { - CHK_IF_RETURN(cudaMallocAsync(&d_alloc_vec_a, n * sizeof(E), config.ctx.stream)); - CHK_IF_RETURN(cudaMemcpyAsync(d_alloc_vec_a, vec_a, n * sizeof(E), cudaMemcpyHostToDevice, config.ctx.stream)); - d_vec_a = d_alloc_vec_a; - } else { - d_vec_a = vec_a; - } - - if (!config.is_b_on_device) { - CHK_IF_RETURN(cudaMallocAsync(&d_alloc_vec_b, n * sizeof(E), config.ctx.stream)); - CHK_IF_RETURN(cudaMemcpyAsync(d_alloc_vec_b, vec_b, n * sizeof(E), cudaMemcpyHostToDevice, config.ctx.stream)); - d_vec_b = d_alloc_vec_b; - } else { - d_vec_b = vec_b; - } - - if (!config.is_result_on_device) { - if (!is_in_place) { - CHK_IF_RETURN(cudaMallocAsync(&d_result, n * sizeof(E), config.ctx.stream)); - } else { - d_result = d_vec_a; - } - } else { - if (!is_in_place) { - d_result = result; - } else { - d_result = result = d_vec_a; - } - } - - // Call the kernel to perform element-wise operation - Kernel<<>>(d_vec_a, d_vec_b, n, d_result); - - if (!config.is_result_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync(result, d_result, n * sizeof(E), cudaMemcpyDeviceToHost, config.ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(d_result, config.ctx.stream)); - } - - if (!config.is_a_on_device && !is_in_place) { CHK_IF_RETURN(cudaFreeAsync(d_alloc_vec_a, config.ctx.stream)); } - if (!config.is_b_on_device) { CHK_IF_RETURN(cudaFreeAsync(d_alloc_vec_b, config.ctx.stream)); } - - if (!config.is_async) return CHK_STICKY(cudaStreamSynchronize(config.ctx.stream)); - - return CHK_LAST(); - } - - template - cudaError_t mul(E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result) - { - return vec_op(vec_a, vec_b, n, config, result); - } - - template - cudaError_t add(E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result) - { - return vec_op(vec_a, vec_b, n, config, result); - } - - template - cudaError_t sub(E* vec_a, const E* vec_b, int n, VecOpsConfig& config, E* result) - { - return vec_op(vec_a, vec_b, n, config, result); - } - - template - cudaError_t transpose_matrix( - const E* mat_in, - E* mat_out, - uint32_t row_size, - uint32_t column_size, - const device_context::DeviceContext& ctx, - bool on_device, - bool is_async) - { - int number_of_threads = MAX_THREADS_PER_BLOCK; - int number_of_blocks = (row_size * column_size + number_of_threads - 1) / number_of_threads; - cudaStream_t stream = ctx.stream; - - const E* d_mat_in; - E* d_allocated_input = nullptr; - E* d_mat_out; - if (!on_device) { - CHK_IF_RETURN(cudaMallocAsync(&d_allocated_input, row_size * column_size * sizeof(E), ctx.stream)); - CHK_IF_RETURN(cudaMemcpyAsync( - d_allocated_input, mat_in, row_size * column_size * sizeof(E), cudaMemcpyHostToDevice, ctx.stream)); - - CHK_IF_RETURN(cudaMallocAsync(&d_mat_out, row_size * column_size * sizeof(E), ctx.stream)); - d_mat_in = d_allocated_input; - } else { - d_mat_in = mat_in; - d_mat_out = mat_out; - } - - transpose_kernel<<>>(d_mat_in, d_mat_out, row_size, column_size); - - if (!on_device) { - CHK_IF_RETURN( - cudaMemcpyAsync(mat_out, d_mat_out, row_size * column_size * sizeof(E), cudaMemcpyDeviceToHost, ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(d_mat_out, ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(d_allocated_input, ctx.stream)); - } - if (!is_async) return CHK_STICKY(cudaStreamSynchronize(ctx.stream)); - - return CHK_LAST(); - } - - template - cudaError_t bit_reverse(const E* input, uint64_t size, BitReverseConfig& cfg, E* output) - { - if (size & (size - 1)) THROW_ICICLE_ERR(IcicleError_t::InvalidArgument, "bit_reverse: size must be a power of 2"); - if ((input == output) & (cfg.is_input_on_device != cfg.is_output_on_device)) - THROW_ICICLE_ERR( - IcicleError_t::InvalidArgument, "bit_reverse: equal devices should have same is_on_device parameters"); - - E* d_output; - if (cfg.is_output_on_device) { - d_output = output; - } else { - // allocate output on gpu - CHK_IF_RETURN(cudaMallocAsync(&d_output, sizeof(E) * size, cfg.ctx.stream)); - } - - uint64_t shift = __builtin_clzll(size) + 1; - uint64_t num_blocks = (size + MAX_THREADS_PER_BLOCK - 1) / MAX_THREADS_PER_BLOCK; - - if ((input != output) & cfg.is_input_on_device) { - bit_reverse_kernel<<>>(input, size, shift, d_output); - } else { - if (!cfg.is_input_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync(d_output, input, sizeof(E) * size, cudaMemcpyHostToDevice, cfg.ctx.stream)); - } - bit_reverse_inplace_kernel<<>>(d_output, size, shift); - } - if (!cfg.is_output_on_device) { - CHK_IF_RETURN(cudaMemcpyAsync(output, d_output, sizeof(E) * size, cudaMemcpyDeviceToHost, cfg.ctx.stream)); - CHK_IF_RETURN(cudaFreeAsync(d_output, cfg.ctx.stream)); - } - if (!cfg.is_async) CHK_IF_RETURN(cudaStreamSynchronize(cfg.ctx.stream)); - return CHK_LAST(); - } -} // namespace vec_ops diff --git a/icicle/tests/CMakeLists.txt b/icicle/tests/CMakeLists.txt index e904710a9..ddae9a7cb 100644 --- a/icicle/tests/CMakeLists.txt +++ b/icicle/tests/CMakeLists.txt @@ -5,28 +5,54 @@ FetchContent_Declare( googletest URL https://github.com/google/googletest/archive/refs/tags/v1.13.0.zip ) -# For Windows: Prevent overriding the parent project's compiler/linker settings +# Disable installation of gtest and gmock +set(INSTALL_GTEST OFF) +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) + +# For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) -add_executable(runner runner.cu) # TODO link to icicle rather than including source files -target_include_directories(runner PUBLIC ${CMAKE_SOURCE_DIR}/include/) -target_link_libraries(runner GTest::gtest_main) -set_target_properties(runner PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# polynomial test-bench -set(POLY_UNSUPPORTED_FIELD grumpkin;m31) -if (NOT FIELD IN_LIST POLY_UNSUPPORTED_FIELD) - add_executable(polynomial_tb polynomial_test.cu) - target_link_libraries(polynomial_tb GTest::gtest_main icicle_field pthread) - if (CURVE) - target_link_libraries(polynomial_tb icicle_curve) +# Ensure gtest and gmock are not built by default +set_target_properties(gtest PROPERTIES EXCLUDE_FROM_ALL TRUE) +set_target_properties(gtest_main PROPERTIES EXCLUDE_FROM_ALL TRUE) +set_target_properties(gmock PROPERTIES EXCLUDE_FROM_ALL TRUE) +set_target_properties(gmock_main PROPERTIES EXCLUDE_FROM_ALL TRUE) + +enable_testing() + +# device API test +add_executable(test_device_api test_device_api.cpp) +target_include_directories(test_device_api PRIVATE ${CMAKE_SOURCE_DIR}/include/) +target_link_libraries(test_device_api PRIVATE GTest::gtest_main icicle_device) +gtest_discover_tests(test_device_api) + +#field API test +if (FIELD) + add_executable(test_field_api test_field_api.cpp) + target_include_directories(test_field_api PRIVATE ${CMAKE_SOURCE_DIR}/include/) + target_link_libraries(test_field_api PRIVATE GTest::gtest_main icicle_device icicle_field) + gtest_discover_tests(test_field_api) + + if (NTT) + add_executable(test_polynomial_api test_polynomial_api.cpp) + target_include_directories(test_polynomial_api PRIVATE ${CMAKE_SOURCE_DIR}/include/) + target_link_libraries(test_polynomial_api PRIVATE GTest::gtest_main icicle_field) + gtest_discover_tests(test_polynomial_api) + endif() +endif() + +#curve API test +if (CURVE) + add_executable(test_curve_api test_curve_api.cpp) + target_include_directories(test_curve_api PRIVATE ${CMAKE_SOURCE_DIR}/include/) + target_link_libraries(test_curve_api PRIVATE GTest::gtest_main icicle_device icicle_field icicle_curve) + gtest_discover_tests(test_curve_api) + + if (NTT) + target_link_libraries(test_polynomial_api PRIVATE GTest::gtest_main icicle_curve) endif() - set_target_properties(polynomial_tb PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - gtest_discover_tests(polynomial_tb) endif() -enable_testing() -gtest_discover_tests(runner) diff --git a/icicle/tests/curve_test.cu b/icicle/tests/curve_test.cu deleted file mode 100644 index 087b3e4b0..000000000 --- a/icicle/tests/curve_test.cu +++ /dev/null @@ -1,194 +0,0 @@ -#include "utils/test_functions.cuh" -#include "curves/curve_config.cuh" -#include -#include -#include - -using namespace curve_config; - -template -class CurveTest : public ::testing::Test -{ -protected: - using TestP = P; - - static const unsigned n = 1 << 4; - - P* points1{}; - P* points2{}; - typename P::Scalar* scalars1{}; - typename P::Scalar* scalars2{}; - P* zero_points{}; - typename P::Scalar* one_scalars{}; - typename P::Aff* aff_points{}; - P* res_points1{}; - P* res_points2{}; - typename P::Scalar* res_scalars{}; - - CurveTest() - { - assert(!cudaDeviceReset()); - assert(!cudaMallocManaged(&points1, n * sizeof(P))); - assert(!cudaMallocManaged(&points2, n * sizeof(P))); - assert(!cudaMallocManaged(&scalars1, n * sizeof(typename P::Scalar))); - assert(!cudaMallocManaged(&scalars2, n * sizeof(typename P::Scalar))); - assert(!cudaMallocManaged(&zero_points, n * sizeof(P))); - assert(!cudaMallocManaged(&one_scalars, n * sizeof(typename P::Scalar))); - assert(!cudaMallocManaged(&aff_points, n * sizeof(typename P::Aff))); - assert(!cudaMallocManaged(&res_points1, n * sizeof(P))); - assert(!cudaMallocManaged(&res_points2, n * sizeof(P))); - assert(!cudaMallocManaged(&res_scalars, n * sizeof(typename P::Scalar))); - } - - ~CurveTest() override - { - cudaFree(points1); - cudaFree(points2); - cudaFree(scalars1); - cudaFree(scalars2); - cudaFree(zero_points); - cudaFree(one_scalars); - cudaFree(aff_points); - cudaFree(res_points1); - cudaFree(res_points2); - cudaFree(res_scalars); - - cudaDeviceReset(); - } - - void SetUp() override - { - ASSERT_EQ(device_populate_random

(points1, n), cudaSuccess); - ASSERT_EQ(device_populate_random

(points2, n), cudaSuccess); - ASSERT_EQ(device_populate_random(scalars1, n), cudaSuccess); - ASSERT_EQ(device_populate_random(scalars2, n), cudaSuccess); - ASSERT_EQ(device_set

(zero_points, P::zero(), n), cudaSuccess); - ASSERT_EQ(device_set(one_scalars, P::Scalar::one(), n), cudaSuccess); - ASSERT_EQ(cudaMemset(aff_points, 0, n * sizeof(typename P::Aff)), cudaSuccess); - ASSERT_EQ(cudaMemset(res_points1, 0, n * sizeof(P)), cudaSuccess); - ASSERT_EQ(cudaMemset(res_points2, 0, n * sizeof(P)), cudaSuccess); - ASSERT_EQ(cudaMemset(res_scalars, 0, n * sizeof(typename P::Scalar)), cudaSuccess); - } -}; - -#ifdef G2 -typedef testing::Types CTImplementations; -#else -typedef testing::Types CTImplementations; -#endif - -TYPED_TEST_SUITE(CurveTest, CTImplementations); - -TYPED_TEST(CurveTest, ECRandomPointsAreOnCurve) -{ - using TestP = typename TestFixture::TestP; - for (unsigned i = 0; i < this->n; i++) - ASSERT_PRED1(TestP::is_on_curve, this->points1[i]); -} - -TYPED_TEST(CurveTest, ECPointAdditionSubtractionCancel) -{ - ASSERT_EQ(vec_add(this->points1, this->points2, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_sub(this->res_points1, this->points2, this->res_points2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i], this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECPointZeroAddition) -{ - ASSERT_EQ(vec_add(this->points1, this->zero_points, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i], this->res_points1[i]); -} - -TYPED_TEST(CurveTest, ECPointAdditionHostDeviceEq) -{ - ASSERT_EQ(vec_add(this->points1, this->points2, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i] + this->points2[i], this->res_points1[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationHostDeviceEq) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->points1, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i] * this->points1[i], this->res_points1[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationByOne) -{ - ASSERT_EQ(vec_mul(this->one_scalars, this->points1, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i], this->res_points1[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationByMinusOne) -{ - ASSERT_EQ(vec_neg(this->one_scalars, this->res_scalars, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->res_scalars, this->points1, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_neg(this->points1, this->res_points2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_points1[i], this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationByTwo) -{ - ASSERT_EQ(vec_add(this->one_scalars, this->one_scalars, this->res_scalars, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->res_scalars, this->points1, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ((this->one_scalars[i] + this->one_scalars[i]) * this->points1[i], this->res_points1[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationInverseCancel) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->points1, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(field_vec_inv(this->scalars1, this->res_scalars, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->res_scalars, this->res_points1, this->res_points2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i], this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationIsDistributiveOverMultiplication) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->points1, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->scalars2, this->res_points1, this->res_points2, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->scalars1, this->scalars2, this->res_scalars, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->res_scalars, this->points1, this->res_points1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_points1[i], this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECScalarMultiplicationIsDistributiveOverAddition) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->points1, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->scalars2, this->points1, this->res_points2, this->n), cudaSuccess); - ASSERT_EQ(vec_add(this->scalars1, this->scalars2, this->res_scalars, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_scalars[i] * this->points1[i], this->res_points1[i] + this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECProjectiveToAffine) -{ - using TestP = typename TestFixture::TestP; - ASSERT_EQ(point_vec_to_affine(this->points1, this->aff_points, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->points1[i], TestP::from_affine(this->aff_points[i])); -} - -TYPED_TEST(CurveTest, ECMixedPointAddition) -{ - ASSERT_EQ(point_vec_to_affine(this->points2, this->aff_points, this->n), cudaSuccess); - ASSERT_EQ(vec_add(this->points1, this->aff_points, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_add(this->points1, this->points2, this->res_points2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_points1[i], this->res_points2[i]); -} - -TYPED_TEST(CurveTest, ECMixedAdditionOfNegatedPointEqSubtraction) -{ - ASSERT_EQ(point_vec_to_affine(this->points2, this->aff_points, this->n), cudaSuccess); - ASSERT_EQ(vec_sub(this->points1, this->aff_points, this->res_points1, this->n), cudaSuccess); - ASSERT_EQ(vec_neg(this->points2, this->res_points2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_points1[i], this->points1[i] + this->res_points2[i]); -} diff --git a/icicle/tests/device_error_test.cu b/icicle/tests/device_error_test.cu deleted file mode 100644 index 9071f8832..000000000 --- a/icicle/tests/device_error_test.cu +++ /dev/null @@ -1,92 +0,0 @@ -#include "gpu-utils/error_handler.cuh" // Include your error handling header file -#include - -__global__ void a_kernel_with_conditional_sticky_error(bool is_failing) -{ - int idx = blockIdx.x * blockDim.x + threadIdx.x; - - if (idx == 0) { - assert(cudaGetLastError() == cudaSuccess); - assert(cudaGetLastError() == cudaSuccess); - // Deliberately assert false - assert(!is_failing); - } -} - -// Test Fixture for CUDA tests -class CudaErrorTest : public ::testing::Test -{ -protected: - void SetUp() override - { - // Perform any setup needed before each test - cudaError_t err = cudaGetLastError(); // Clear any existing errors - } - - void TearDown() override - { - // Clean up after each test if necessary - } -}; - -// Test Case for Non-Sticky Error -TEST_F(CudaErrorTest, NonStickyErrorTest) -{ - cudaGetLastError(); - - // Deliberately cause a non-sticky CUDA error - cudaError_t err = cudaMalloc(nullptr, 0); // This should cause cudaErrorInvalidValue - cudaError_t err2; - - // Check if the macro correctly reports the error without throwing an exception - EXPECT_EQ(err, cudaErrorInvalidValue); - EXPECT_NO_THROW({ err2 = CHK_LAST(); }); - EXPECT_EQ(err2, err); - - // Optionally, clear the error if needed - EXPECT_EQ(cudaGetLastError(), cudaSuccess); -} - -// Test Case for Sticky Error -TEST_F(CudaErrorTest, StickyErrorTest) -{ - EXPECT_EQ(cudaGetLastError(), cudaSuccess); - - // Deliberately cause a sticky CUDA error - a_kernel_with_conditional_sticky_error<<<1, 1>>>(true); - - EXPECT_EQ(cudaGetLastError(), cudaSuccess); // no error until synchronization - - // Launch without error - a_kernel_with_conditional_sticky_error<<<1, 1>>>(false); - - EXPECT_EQ(cudaGetLastError(), cudaSuccess); - - cudaError_t sync_error = cudaDeviceSynchronize(); // only cudaDeviceSynchronize() can help - // determine sticky error reliably, - // returning same error as failed kernel - - EXPECT_NE(sync_error, cudaSuccess); - EXPECT_EQ(sync_error, cudaErrorAssert); - - EXPECT_EQ(cudaGetLastError(), cudaErrorAssert); // reports error after cudaDeviceSynchronize - EXPECT_EQ(cudaGetLastError(), cudaSuccess); // resets error, despite it's sticky - - // Check if the macro correctly throws an exception for a sticky error - EXPECT_THROW({ CHK_STICKY(cudaDeviceSynchronize()); }, IcicleError); -} - -// Test Case for Sticky Error -TEST_F(CudaErrorTest, StickyErrorTestNotThrowing) -{ - EXPECT_EQ(cudaGetLastError(), cudaSuccess); - - // Deliberately cause a sticky CUDA error - a_kernel_with_conditional_sticky_error<<<1, 1>>>(true); - - EXPECT_EQ(cudaDeviceSynchronize(), cudaErrorAssert); - - // Check if the macro correctly throws an exception for a sticky error - cudaError_t err = CHK_LAST_STICKY_NO_THROW(); - EXPECT_EQ(err, cudaErrorAssert); -} \ No newline at end of file diff --git a/icicle/tests/error_handler_test.cu b/icicle/tests/error_handler_test.cu deleted file mode 100644 index 64023694c..000000000 --- a/icicle/tests/error_handler_test.cu +++ /dev/null @@ -1,43 +0,0 @@ -#include "gpu-utils/error_handler.cuh" // Include your error handling header file -#include - -class IcicleErrorTest : public ::testing::Test -{ -protected: - // You can define helper functions or setup code here if needed -}; - -TEST_F(IcicleErrorTest, UndefinedErrorString) -{ - std::string expected = "Undefined error occurred"; - EXPECT_EQ(IcicleGetErrorString(IcicleError_t::UndefinedError), expected); -} - -TEST_F(IcicleErrorTest, UnknownErrorCodeString) -{ - // Using a made-up error code to test the default case - std::string expected = "Unknown error code"; - EXPECT_EQ(IcicleGetErrorString(static_cast(999)), expected); -} - -TEST_F(IcicleErrorTest, IcicleErrorWithCudaError) -{ - // Example test for IcicleError constructor with cudaError_t - std::string msg = "Test Message"; - cudaError_t cudaErr = cudaErrorInvalidDevice; - - IcicleError error(cudaErr, msg); - EXPECT_EQ(error.getErrorCode(), static_cast(cudaErr)); - EXPECT_NE(std::string(error.what()).find("CUDA Error:"), std::string::npos); - EXPECT_NE(std::string(error.what()).find(msg), std::string::npos); -} - -TEST_F(IcicleErrorTest, IcicleErrorWithIcicleErrorCode) -{ - // Example test for IcicleError constructor with IcicleError_t - std::string msg = "Custom Error"; - IcicleError error(IcicleError_t::UndefinedError, msg); - EXPECT_EQ(error.getErrorCode(), static_cast(IcicleError_t::UndefinedError)); - EXPECT_NE(std::string(error.what()).find("Icicle Error:"), std::string::npos); - EXPECT_NE(std::string(error.what()).find(msg), std::string::npos); -} \ No newline at end of file diff --git a/icicle/tests/field_test.cu b/icicle/tests/field_test.cu deleted file mode 100644 index 704c39868..000000000 --- a/icicle/tests/field_test.cu +++ /dev/null @@ -1,145 +0,0 @@ -#include "utils/test_functions.cuh" -#include "fields/field_config.cuh" -#include -#include -#include - -using namespace field_config; - -template -class FieldTest : public ::testing::Test -{ -protected: - static const unsigned n = 1 << 4; - - T* scalars1{}; - T* scalars2{}; - T* zero_scalars{}; - T* one_scalars{}; - T* res_scalars1{}; - T* res_scalars2{}; - - FieldTest() - { - assert(!cudaDeviceReset()); - assert(!cudaMallocManaged(&scalars1, n * sizeof(T))); - assert(!cudaMallocManaged(&scalars2, n * sizeof(T))); - assert(!cudaMallocManaged(&zero_scalars, n * sizeof(T))); - assert(!cudaMallocManaged(&one_scalars, n * sizeof(T))); - assert(!cudaMallocManaged(&res_scalars1, n * sizeof(T))); - assert(!cudaMallocManaged(&res_scalars2, n * sizeof(T))); - } - - ~FieldTest() override - { - cudaFree(scalars1); - cudaFree(scalars2); - cudaFree(zero_scalars); - cudaFree(one_scalars); - cudaFree(res_scalars1); - cudaFree(res_scalars2); - - cudaDeviceReset(); - } - - void SetUp() override - { - ASSERT_EQ(device_populate_random(scalars1, n), cudaSuccess); - ASSERT_EQ(device_populate_random(scalars2, n), cudaSuccess); - ASSERT_EQ(device_set(zero_scalars, T::zero(), n), cudaSuccess); - ASSERT_EQ(device_set(one_scalars, T::one(), n), cudaSuccess); - ASSERT_EQ(cudaMemset(res_scalars1, 0, n * sizeof(T)), cudaSuccess); - ASSERT_EQ(cudaMemset(res_scalars2, 0, n * sizeof(T)), cudaSuccess); - } -}; - -#ifdef EXT_FIELD -typedef testing::Types FTImplementations; -#else -typedef testing::Types FTImplementations; -#endif - -TYPED_TEST_SUITE(FieldTest, FTImplementations); - -TYPED_TEST(FieldTest, FieldAdditionSubtractionCancel) -{ - ASSERT_EQ(vec_add(this->scalars1, this->scalars2, this->res_scalars1, this->n), cudaSuccess); - ASSERT_EQ(vec_sub(this->res_scalars1, this->scalars2, this->res_scalars2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i], this->res_scalars2[i]); -} - -TYPED_TEST(FieldTest, FieldZeroAddition) -{ - ASSERT_EQ(vec_add(this->scalars1, this->zero_scalars, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldAdditionHostDeviceEq) -{ - ASSERT_EQ(vec_add(this->scalars1, this->scalars2, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i] + this->scalars2[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationByOne) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->one_scalars, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationByMinusOne) -{ - ASSERT_EQ(vec_neg(this->one_scalars, this->res_scalars1, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->scalars1, this->res_scalars1, this->res_scalars2, this->n), cudaSuccess); - ASSERT_EQ(vec_add(this->scalars1, this->res_scalars2, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_scalars1[i], this->zero_scalars[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationByZero) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->zero_scalars, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->zero_scalars[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationInverseCancel) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->scalars2, this->res_scalars1, this->n), cudaSuccess); - ASSERT_EQ(field_vec_inv(this->scalars2, this->res_scalars2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i], this->res_scalars1[i] * this->res_scalars2[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationHostDeviceEq) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->scalars2, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i] * this->scalars2[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationByTwoEqSum) -{ - ASSERT_EQ(vec_add(this->one_scalars, this->one_scalars, this->res_scalars1, this->n), cudaSuccess); - ASSERT_EQ(vec_mul(this->res_scalars1, this->scalars1, this->res_scalars2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_scalars2[i], this->scalars1[i] + this->scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldSqrHostDeviceEq) -{ - ASSERT_EQ(field_vec_sqr(this->scalars1, this->res_scalars1, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->scalars1[i] * this->scalars1[i], this->res_scalars1[i]); -} - -TYPED_TEST(FieldTest, FieldMultiplicationSqrEq) -{ - ASSERT_EQ(vec_mul(this->scalars1, this->scalars1, this->res_scalars1, this->n), cudaSuccess); - ASSERT_EQ(field_vec_sqr(this->scalars1, this->res_scalars2, this->n), cudaSuccess); - for (unsigned i = 0; i < this->n; i++) - ASSERT_EQ(this->res_scalars1[i], this->res_scalars2[i]); -} diff --git a/icicle/tests/runner.cu b/icicle/tests/runner.cu deleted file mode 100644 index 330b82c50..000000000 --- a/icicle/tests/runner.cu +++ /dev/null @@ -1,20 +0,0 @@ -#include -#include -#include - -// include list of test files -#include "field_test.cu" -#ifdef CURVE_ID -#include "curve_test.cu" -#endif -#include "error_handler_test.cu" - -// Ensure the device_error_test.cu is last to prevent aborting mid-test run -#include "device_error_test.cu" - -int main(int argc, char** argv) -{ - ::testing::InitGoogleTest(&argc, argv); - printf("running gtests...\n"); - return RUN_ALL_TESTS(); -} \ No newline at end of file diff --git a/icicle/tests/test_curve_api.cpp b/icicle/tests/test_curve_api.cpp new file mode 100644 index 000000000..db6a7e5a7 --- /dev/null +++ b/icicle/tests/test_curve_api.cpp @@ -0,0 +1,252 @@ + +#include +#include +#include +#include +#include "dlfcn.h" + +#include "icicle/runtime.h" +#include "icicle/ntt.h" +#include "icicle/msm.h" +#include "icicle/vec_ops.h" +#include "icicle/curves/montgomery_conversion.h" +#include "icicle/curves/curve_config.h" +#include "icicle/backend/msm_config.h" + +using namespace curve_config; +using namespace icicle; + +using FpMicroseconds = std::chrono::duration; +#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); +#define END_TIMER(timer, msg, enable) \ + if (enable) \ + printf( \ + "%s: %.3f ms\n", msg, FpMicroseconds(std::chrono::high_resolution_clock::now() - timer##_start).count() / 1000); + +static bool VERBOSE = true; +static inline std::string s_main_target; +static inline std::string s_ref_target; + +class CurveApiTest : public ::testing::Test +{ +public: + static inline std::list s_registered_devices; + + // SetUpTestSuite/TearDownTestSuite are called once for the entire test suite + static void SetUpTestSuite() + { +#ifdef BACKEND_BUILD_DIR + setenv("ICICLE_BACKEND_INSTALL_DIR", BACKEND_BUILD_DIR, 0 /*=replace*/); +#endif + icicle_load_backend_from_env_or_default(); + + const bool is_cuda_registered = is_device_registered("CUDA"); + if (!is_cuda_registered) { ICICLE_LOG_ERROR << "CUDA device not found. Testing CPU vs CPU"; } + s_main_target = is_cuda_registered ? "CUDA" : "CPU"; + s_ref_target = "CPU"; + } + static void TearDownTestSuite() + { + // make sure to fail in CI if only have one device + ICICLE_ASSERT(is_device_registered("CUDA")) << "missing CUDA backend"; + } + + // SetUp/TearDown are called before and after each test + void SetUp() override {} + void TearDown() override {} + + template + void random_scalars(T* arr, uint64_t count) + { + for (uint64_t i = 0; i < count; i++) + arr[i] = i < 1000 ? T::rand_host() : arr[i - 1000]; + } + + template + void MSM_test() + { + const int logn = 12; + const int batch = 2; + const int N = 1 << logn; + const int precompute_factor = 2; + const bool points_montgomery = false; + int c = std::max(logn, 8) - 1; + if (scalar_t::NBITS % c == 0) { c++; } + int n_threads = std::thread::hardware_concurrency(); + if (n_threads <= 0) { + ICICLE_LOG_WARNING << "Unable to detect number of hardware supported threads - fixing it to 1\n"; + n_threads = 1; + } + const int total_nof_elemets = batch * N; + auto scalars = std::make_unique(total_nof_elemets); + auto bases = std::make_unique(N); + + scalar_t::rand_host_many(scalars.get(), total_nof_elemets); + P::rand_host_many(bases.get(), N); + + auto result_main = std::make_unique(batch); + auto result_ref = std::make_unique(batch); + + auto run = [&](const std::string& dev_type, P* result, const char* msg, bool measure, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + auto config = default_msm_config(); + config.c = c; + config.batch_size = batch; + config.are_points_shared_in_batch = true; + config.precompute_factor = precompute_factor; + + config.are_results_on_device = true; + + ConfigExtension ext; + ext.set(CpuBackendConfig::CPU_NOF_THREADS, n_threads); + config.ext = &ext; + + // Note: allocating the precompute_bases on device since CUDA backend assumes that. + // TODO: fix CUDA backend to support host memory too. + A* precomp_bases = nullptr; + ICICLE_CHECK(icicle_malloc((void**)&precomp_bases, N * precompute_factor * sizeof(A))); + ICICLE_CHECK(msm_precompute_bases(bases.get(), N, config, precomp_bases)); + + config.are_points_on_device = true; + config.are_results_on_device = false; + + START_TIMER(MSM_sync) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(msm(scalars.get(), precomp_bases, N, config, result)); + } + END_TIMER(MSM_sync, oss.str().c_str(), measure); + ICICLE_CHECK(icicle_free(precomp_bases)); + }; + + run(s_main_target, result_main.get(), "msm", VERBOSE /*=measure*/, 1 /*=iters*/); + run(s_ref_target, result_ref.get(), "msm", VERBOSE /*=measure*/, 1 /*=iters*/); + for (int res_idx = 0; res_idx < batch; ++res_idx) { + ASSERT_EQ(true, P::is_on_curve(result_main[res_idx])); + ASSERT_EQ(true, P::is_on_curve(result_ref[res_idx])); + ASSERT_EQ(result_main[res_idx], result_ref[res_idx]); + } + } + + template + void mont_conversion_test() + { + const int N = 1 << 6; + auto elements = std::make_unique(N); + auto main_output = std::make_unique(N); + auto ref_output = std::make_unique(N); + P::rand_host_many(elements.get(), N); + + auto run = + [&](const std::string& dev_type, T* input, T* output, bool into_mont, bool measure, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + auto config = default_vec_ops_config(); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + START_TIMER(MONTGOMERY) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(convert_montgomery(input, N, into_mont, config, output)); + } + END_TIMER(MONTGOMERY, oss.str().c_str(), measure); + }; + + run(s_main_target, elements.get(), main_output.get(), true /*into*/, VERBOSE /*=measure*/, "to-montgomery", 1); + run(s_ref_target, elements.get(), ref_output.get(), true /*into*/, VERBOSE /*=measure*/, "to-montgomery", 1); + ASSERT_EQ(0, memcmp(main_output.get(), ref_output.get(), N * sizeof(T))); + + run(s_main_target, main_output.get(), main_output.get(), false /*from*/, VERBOSE /*=measure*/, "to-montgomery", 1); + run(s_ref_target, ref_output.get(), ref_output.get(), false /*from*/, VERBOSE /*=measure*/, "to-montgomery", 1); + ASSERT_EQ(0, memcmp(main_output.get(), ref_output.get(), N * sizeof(T))); + } +}; + +#ifdef MSM +TEST_F(CurveApiTest, msm) { MSM_test(); } +TEST_F(CurveApiTest, MontConversionAffine) { mont_conversion_test(); } +TEST_F(CurveApiTest, MontConversionProjective) { mont_conversion_test(); } + +#ifdef G2 +TEST_F(CurveApiTest, msmG2) { MSM_test(); } +TEST_F(CurveApiTest, MontConversionG2Affine) { mont_conversion_test(); } +TEST_F(CurveApiTest, MontConversionG2Projective) { mont_conversion_test(); } +#endif // G2 +#endif // MSM + +#ifdef ECNTT +TEST_F(CurveApiTest, ecntt) +{ + const int logn = 5; + const int N = 1 << logn; + auto input = std::make_unique(N); + projective_t::rand_host_many(input.get(), N); + + auto out_main = std::make_unique(N); + auto out_ref = std::make_unique(N); + + auto run = [&](const std::string& dev_type, projective_t* out, const char* msg, bool measure, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + + auto init_domain_config = default_ntt_init_domain_config(); + ICICLE_CHECK(ntt_init_domain(scalar_t::omega(logn), init_domain_config)); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + auto config = default_ntt_config(); + + START_TIMER(NTT_sync) + for (int i = 0; i < iters; ++i) + ICICLE_CHECK(ntt(input.get(), N, NTTDir::kForward, config, out)); + END_TIMER(NTT_sync, oss.str().c_str(), measure); + + ntt_release_domain(); + }; + + run(s_main_target, out_main.get(), "ecntt", VERBOSE /*=measure*/, 1 /*=iters*/); + run(s_ref_target, out_ref.get(), "ecntt", VERBOSE /*=measure*/, 1 /*=iters*/); + // ASSERT_EQ(0, memcmp(out_main.get(), out_ref.get(), N * sizeof(projective_t))); // TODO ucomment when CPU is + // implemented +} +#endif // ECNTT + +template +class CurveSanity : public ::testing::Test +{ +}; + +#ifdef G2 +typedef testing::Types CTImplementations; +#else +typedef testing::Types CTImplementations; +#endif + +TYPED_TEST_SUITE(CurveSanity, CTImplementations); + +// Note: this is testing host arithmetic. Other tests against CPU backend should guarantee correct device arithmetic too +TYPED_TEST(CurveSanity, CurveSanityTest) +{ + auto a = TypeParam::rand_host(); + auto b = TypeParam::rand_host(); + ASSERT_EQ(true, TypeParam::is_on_curve(a) && TypeParam::is_on_curve(b)); // rand is on curve + ASSERT_EQ(a + TypeParam::zero(), a); // zero addition + ASSERT_EQ(a + b - a, b); // addition,subtraction cancel + ASSERT_EQ(a + TypeParam::neg(a), TypeParam::zero()); // addition with neg cancel + ASSERT_EQ(a + a + a, scalar_t::from(3) * a); // scalar multiplication + ASSERT_EQ(scalar_t::from(3) * (a + b), scalar_t::from(3) * a + scalar_t::from(3) * b); // distributive + ASSERT_EQ(a + b, a + TypeParam::to_affine(b)); // mixed addition projective+affine + ASSERT_EQ(a - b, a - TypeParam::to_affine(b)); // mixed subtraction projective-affine +} + +int main(int argc, char** argv) +{ + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} \ No newline at end of file diff --git a/icicle/tests/test_device_api.cpp b/icicle/tests/test_device_api.cpp new file mode 100644 index 000000000..98e2dfadb --- /dev/null +++ b/icicle/tests/test_device_api.cpp @@ -0,0 +1,207 @@ + +#include +#include + +#include "icicle/runtime.h" +#include "icicle/memory_tracker.h" +#include "dlfcn.h" + +using FpNanoseconds = std::chrono::duration; +#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); +#define END_TIMER(timer, msg, enable, iters) \ + if (enable) \ + printf( \ + "%s: %.3f ns\n", msg, FpNanoseconds(std::chrono::high_resolution_clock::now() - timer##_start).count() / iters); + +using namespace icicle; + +class DeviceApiTest : public ::testing::Test +{ +public: + static inline std::vector s_registered_devices; + // SetUpTestSuite/TearDownTestSuite are called once for the entire test suite + static void SetUpTestSuite() + { +#ifdef BACKEND_BUILD_DIR + setenv("ICICLE_BACKEND_INSTALL_DIR", BACKEND_BUILD_DIR, 0 /*=replace*/); +#endif + icicle_load_backend_from_env_or_default(); + s_registered_devices = get_registered_devices_list(); + ASSERT_GT(s_registered_devices.size(), 0); + } + static void TearDownTestSuite() {} + + // SetUp/TearDown are called before and after each test + void SetUp() override {} + void TearDown() override {} +}; + +TEST_F(DeviceApiTest, UnregisteredDeviceError) +{ + icicle::Device dev = {"INVALID_DEVICE", 2}; + EXPECT_ANY_THROW(get_deviceAPI(dev)); +} + +TEST_F(DeviceApiTest, MemoryCopySync) +{ + int input[2] = {1, 2}; + + for (const auto& device_type : s_registered_devices) { + int output[2] = {0, 0}; + + icicle::Device dev = {device_type, 0}; + icicle_set_device(dev); + + void* dev_mem = nullptr; + ICICLE_CHECK(icicle_malloc(&dev_mem, sizeof(input))); + ICICLE_CHECK(icicle_copy_to_device(dev_mem, input, sizeof(input))); + ICICLE_CHECK(icicle_copy_to_host(output, dev_mem, sizeof(input))); + ICICLE_CHECK(icicle_free(dev_mem)); + + ASSERT_EQ(0, memcmp(input, output, sizeof(input))); + } +} + +TEST_F(DeviceApiTest, MemoryCopyAsync) +{ + int input[2] = {1, 2}; + for (const auto& device_type : s_registered_devices) { + int output[2] = {0, 0}; + + icicle::Device dev = {device_type, 0}; + icicle_set_device(dev); + void* dev_mem = nullptr; + + icicleStreamHandle stream; + ICICLE_CHECK(icicle_create_stream(&stream)); + ICICLE_CHECK(icicle_malloc_async(&dev_mem, sizeof(input), stream)); + ICICLE_CHECK(icicle_copy_to_device_async(dev_mem, input, sizeof(input), stream)); + ICICLE_CHECK(icicle_copy_to_host_async(output, dev_mem, sizeof(input), stream)); + ICICLE_CHECK(icicle_free_async(dev_mem, stream)); + ICICLE_CHECK(icicle_stream_synchronize(stream)); + + ASSERT_EQ(0, memcmp(input, output, sizeof(input))); + } +} + +TEST_F(DeviceApiTest, CopyDeviceInference) +{ + int input[2] = {1, 2}; + for (const auto& device_type : s_registered_devices) { + int output[2] = {0, 0}; + + icicle::Device dev = {device_type, 0}; + icicle_set_device(dev); + void* dev_mem = nullptr; + + ICICLE_CHECK(icicle_malloc(&dev_mem, sizeof(input))); + ICICLE_CHECK(icicle_copy(dev_mem, input, sizeof(input))); // implicit host to device + ICICLE_CHECK(icicle_copy(output, dev_mem, sizeof(input))); // implicit device to host + + ASSERT_EQ(0, memcmp(input, output, sizeof(input))); + } +} + +TEST_F(DeviceApiTest, Memest) +{ + char expected[2] = {1, 1}; + for (const auto& device_type : s_registered_devices) { + char host_mem[2] = {0, 0}; + + // icicle::Device dev = {device_type, 0}; + icicle::Device dev = {"CPU", 0}; + icicle_set_device(dev); + void* dev_mem = nullptr; + + ICICLE_CHECK(icicle_malloc(&dev_mem, sizeof(host_mem))); + ICICLE_CHECK(icicle_memset(dev_mem, 1, sizeof(host_mem))); // implicit on device + ICICLE_CHECK(icicle_copy(host_mem, dev_mem, sizeof(host_mem))); // implicit device to host + + ASSERT_EQ(0, memcmp(expected, host_mem, sizeof(host_mem))); + } +} + +TEST_F(DeviceApiTest, ApiError) +{ + for (const auto& device_type : s_registered_devices) { + icicle::Device dev = {device_type, 0}; + icicle_set_device(dev); + void* dev_mem = nullptr; + EXPECT_ANY_THROW(ICICLE_CHECK(icicle_malloc(&dev_mem, -1))); + } +} + +TEST_F(DeviceApiTest, AvailableMemory) +{ + icicle::Device dev = {"CUDA", 0}; + const bool is_cuda_registered = eIcicleError::SUCCESS == icicle_is_device_available(dev); + if (!is_cuda_registered) { return; } // TODO implement for CPU too + + icicle_set_device(dev); + size_t total, free; + ASSERT_EQ(eIcicleError::SUCCESS, icicle_get_available_memory(total, free)); + + double total_GB = double(total) / (1 << 30); + double free_GB = double(free) / (1 << 30); + std::cout << std::fixed << std::setprecision(2) << "total=" << total_GB << "[GB], free=" << free_GB << "[GB]" + << std::endl; +} + +TEST_F(DeviceApiTest, InvalidDevice) +{ + for (const auto& device_type : s_registered_devices) { + icicle::Device dev = {device_type, 10}; // no such device-id thus expecting an error + ASSERT_EQ(eIcicleError::INVALID_DEVICE, icicle_set_device(dev)); + } +} + +TEST_F(DeviceApiTest, memoryTracker) +{ + const int NOF_ALLOCS = 1000; + const int ALLOC_SIZE = 1 << 20; + + MemoryTracker tracker{}; + Device device_cuda = {"CUDA", 0}; + icicle_set_device(device_cuda); + + std::vector allocated_addresses(NOF_ALLOCS, nullptr); + + START_TIMER(allocation); + for (auto& it : allocated_addresses) { + icicle_malloc(&it, ALLOC_SIZE); + } + END_TIMER(allocation, "memory-tracker: malloc average", true, NOF_ALLOCS); + + START_TIMER(insertion); + for (auto& it : allocated_addresses) { + tracker.add_allocation(it, ALLOC_SIZE, device_cuda); + } + END_TIMER(insertion, "memory-tracker: insert average", true, NOF_ALLOCS); + + START_TIMER(lookup); + for (auto& it : allocated_addresses) { + // identify addresses identified correctly (to active device) + const void* addr = (void*)((size_t)it + rand() % ALLOC_SIZE); + ICICLE_CHECK(icicle_is_active_device_memory(addr)); + } + END_TIMER(lookup, "memory-tracker: lookup (and compare) average", true, NOF_ALLOCS); + + // test host pointers are identified as host memory + auto host_mem = std::make_unique(); + ICICLE_CHECK(icicle_is_host_memory(host_mem.get())); + ASSERT_EQ(eIcicleError::INVALID_POINTER, icicle_is_active_device_memory(host_mem.get())); + + // test that we still identify correctly after switching device + icicle_set_device({"CPU", 0}); + const void* addr = (void*)((size_t)*allocated_addresses.begin() + rand() % ALLOC_SIZE); + ASSERT_EQ(eIcicleError::INVALID_POINTER, icicle_is_active_device_memory(addr)); + ASSERT_EQ(eIcicleError::INVALID_POINTER, icicle_is_active_device_memory(host_mem.get())); + auto dev = tracker.identify_device(addr); + ASSERT_EQ(**dev, device_cuda); +} + +int main(int argc, char** argv) +{ + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} \ No newline at end of file diff --git a/icicle/tests/test_field_api.cpp b/icicle/tests/test_field_api.cpp new file mode 100644 index 000000000..9743c6d2d --- /dev/null +++ b/icicle/tests/test_field_api.cpp @@ -0,0 +1,390 @@ +#include +#include +#include "dlfcn.h" +#include + +#include "icicle/runtime.h" +#include "icicle/vec_ops.h" +#include "icicle/ntt.h" + +#include "icicle/fields/field_config.h" +#include "icicle/utils/log.h" +#include "icicle/backend/ntt_config.h" + +using namespace field_config; +using namespace icicle; + +using FpMicroseconds = std::chrono::duration; +#define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); +#define END_TIMER(timer, msg, enable) \ + if (enable) \ + printf( \ + "%s: %.3f ms\n", msg, FpMicroseconds(std::chrono::high_resolution_clock::now() - timer##_start).count() / 1000); + +static bool VERBOSE = true; +static int ITERS = 16; +static inline std::string s_main_target; +static inline std::string s_reference_target; + +template +class FieldApiTest : public ::testing::Test +{ +public: + // SetUpTestSuite/TearDownTestSuite are called once for the entire test suite + static void SetUpTestSuite() + { +#ifdef BACKEND_BUILD_DIR + setenv("ICICLE_BACKEND_INSTALL_DIR", BACKEND_BUILD_DIR, 0 /*=replace*/); +#endif + icicle_load_backend_from_env_or_default(); + + const bool is_cuda_registered = is_device_registered("CUDA"); + if (!is_cuda_registered) { ICICLE_LOG_ERROR << "CUDA device not found. Testing CPU vs CPU"; } + s_main_target = is_cuda_registered ? "CUDA" : "CPU"; + s_reference_target = "CPU"; + } + static void TearDownTestSuite() + { + // make sure to fail in CI if only have one device + ICICLE_ASSERT(is_device_registered("CUDA")) << "missing CUDA backend"; + } + + // SetUp/TearDown are called before and after each test + void SetUp() override {} + void TearDown() override {} + + void random_samples(T* arr, uint64_t count) + { + for (uint64_t i = 0; i < count; i++) + arr[i] = i < 1000 ? T::rand_host() : arr[i - 1000]; + } +}; + +#ifdef EXT_FIELD +typedef testing::Types FTImplementations; +#else +typedef testing::Types FTImplementations; +#endif + +TYPED_TEST_SUITE(FieldApiTest, FTImplementations); + +// Note: this is testing host arithmetic. Other tests against CPU backend should guarantee correct device arithmetic too +TYPED_TEST(FieldApiTest, FieldSanityTest) +{ + auto a = TypeParam::rand_host(); + auto b = TypeParam::rand_host(); + auto b_inv = TypeParam::inverse(b); + auto a_neg = TypeParam::neg(a); + ASSERT_EQ(a + TypeParam::zero(), a); + ASSERT_EQ(a + b - a, b); + ASSERT_EQ(b * a * b_inv, a); + ASSERT_EQ(a + a_neg, TypeParam::zero()); + ASSERT_EQ(a * TypeParam::zero(), TypeParam::zero()); + ASSERT_EQ(b * b_inv, TypeParam::one()); + ASSERT_EQ(a * scalar_t::from(2), a + a); +} + +TYPED_TEST(FieldApiTest, vectorOps) +{ + const uint64_t N = 1 << 22; + auto in_a = std::make_unique(N); + auto in_b = std::make_unique(N); + FieldApiTest::random_samples(in_a.get(), N); + FieldApiTest::random_samples(in_b.get(), N); + + auto out_main = std::make_unique(N); + auto out_ref = std::make_unique(N); + + auto vector_accumulate_wrapper = + [](TypeParam* a, const TypeParam* b, uint64_t size, const VecOpsConfig& config, TypeParam* /*out*/) { + return vector_accumulate(a, b, size, config); + }; + + auto run = + [&](const std::string& dev_type, TypeParam* out, bool measure, auto vec_op_func, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + auto config = default_vec_ops_config(); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + START_TIMER(VECADD_sync) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(vec_op_func(in_a.get(), in_b.get(), N, config, out)); + } + END_TIMER(VECADD_sync, oss.str().c_str(), measure); + }; + + // warmup + // run(s_reference_target, out_ref.get(), false /*=measure*/, 16 /*=iters*/); + // run(s_main_target, out_main.get(), false /*=measure*/, 1 /*=iters*/); + + // accumulate + auto temp_result = std::make_unique(N); + auto initial_in_a = std::make_unique(N); + + std::memcpy(initial_in_a.get(), in_a.get(), N * sizeof(TypeParam)); + run(s_reference_target, nullptr, VERBOSE /*=measure*/, vector_accumulate_wrapper, "vector accumulate", ITERS); + std::memcpy(temp_result.get(), in_a.get(), N * sizeof(TypeParam)); + std::memcpy(in_a.get(), initial_in_a.get(), N * sizeof(TypeParam)); + run(s_main_target, nullptr, VERBOSE /*=measure*/, vector_accumulate_wrapper, "vector accumulate", ITERS); + ASSERT_EQ(0, memcmp(in_a.get(), temp_result.get(), N * sizeof(TypeParam))); + + // add + run(s_reference_target, out_ref.get(), VERBOSE /*=measure*/, vector_add, "vector add", ITERS); + run(s_main_target, out_main.get(), VERBOSE /*=measure*/, vector_add, "vector add", ITERS); + ASSERT_EQ(0, memcmp(out_main.get(), out_ref.get(), N * sizeof(TypeParam))); + + // sub + run(s_reference_target, out_ref.get(), VERBOSE /*=measure*/, vector_sub, "vector sub", ITERS); + run(s_main_target, out_main.get(), VERBOSE /*=measure*/, vector_sub, "vector sub", ITERS); + ASSERT_EQ(0, memcmp(out_main.get(), out_ref.get(), N * sizeof(TypeParam))); + + // mul + run(s_reference_target, out_ref.get(), VERBOSE /*=measure*/, vector_mul, "vector mul", ITERS); + run(s_main_target, out_main.get(), VERBOSE /*=measure*/, vector_mul, "vector mul", ITERS); + ASSERT_EQ(0, memcmp(out_main.get(), out_ref.get(), N * sizeof(TypeParam))); +} + +TYPED_TEST(FieldApiTest, matrixAPIsAsync) +{ + const int R = 1 << 10, C = 1 << 8; + auto h_in = std::make_unique(R * C); + FieldApiTest::random_samples(h_in.get(), R * C); + + auto h_out_main = std::make_unique(R * C); + auto h_out_ref = std::make_unique(R * C); + + auto run = [&](const std::string& dev_type, TypeParam* h_out, bool measure, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + + DeviceProperties device_props; + icicle_get_device_properties(device_props); + auto config = default_vec_ops_config(); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + // Note: if the device uses host memory, do not allocate device memory and copy + + TypeParam *d_in, *d_out; + if (!device_props.using_host_memory) { + icicle_create_stream(&config.stream); + icicle_malloc_async((void**)&d_in, R * C * sizeof(TypeParam), config.stream); + icicle_malloc_async((void**)&d_out, R * C * sizeof(TypeParam), config.stream); + icicle_copy_to_device_async(d_in, h_in.get(), R * C * sizeof(TypeParam), config.stream); + + config.is_a_on_device = true; + config.is_result_on_device = true; + config.is_async = false; + } + + TypeParam* in = device_props.using_host_memory ? h_in.get() : d_in; + TypeParam* out = device_props.using_host_memory ? h_out : d_out; + + START_TIMER(TRANSPOSE) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(matrix_transpose(in, R, C, config, out)); + } + END_TIMER(TRANSPOSE, oss.str().c_str(), measure); + + if (!device_props.using_host_memory) { + icicle_copy_to_host_async(h_out, d_out, R * C * sizeof(TypeParam), config.stream); + icicle_stream_synchronize(config.stream); + icicle_free_async(d_in, config.stream); + icicle_free_async(d_out, config.stream); + } + }; + + run(s_reference_target, h_out_ref.get(), VERBOSE /*=measure*/, "transpose", ITERS); + run(s_main_target, h_out_main.get(), VERBOSE /*=measure*/, "transpose", ITERS); + ASSERT_EQ(0, memcmp(h_out_main.get(), h_out_ref.get(), R * C * sizeof(TypeParam))); +} + +TYPED_TEST(FieldApiTest, montgomeryConversion) +{ + const uint64_t N = 1 << 18; + auto elements_main = std::make_unique(N); + auto elements_ref = std::make_unique(N); + FieldApiTest::random_samples(elements_main.get(), N); + memcpy(elements_ref.get(), elements_main.get(), N * sizeof(TypeParam)); + + auto run = [&](const std::string& dev_type, TypeParam* inout, bool measure, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + auto config = default_vec_ops_config(); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + START_TIMER(MONTGOMERY) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(convert_montgomery(inout, N, true /*into montgomery*/, config, inout)); + } + END_TIMER(MONTGOMERY, oss.str().c_str(), measure); + }; + + run(s_reference_target, elements_main.get(), VERBOSE /*=measure*/, "montgomery", 1); + run(s_main_target, elements_ref.get(), VERBOSE /*=measure*/, "montgomery", 1); + ASSERT_EQ(0, memcmp(elements_main.get(), elements_ref.get(), N * sizeof(TypeParam))); +} + +TYPED_TEST(FieldApiTest, bitReverse) +{ + const uint64_t N = 1 << 18; + auto elements_main = std::make_unique(N); + auto elements_ref = std::make_unique(N); + FieldApiTest::random_samples(elements_main.get(), N); + memcpy(elements_ref.get(), elements_main.get(), N * sizeof(TypeParam)); + + auto run = [&](const std::string& dev_type, TypeParam* inout, bool measure, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + auto config = default_vec_ops_config(); + + std::ostringstream oss; + oss << dev_type << " " << msg; + + START_TIMER(BIT_REVERSE) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(bit_reverse(inout, N, config, inout)); + } + END_TIMER(BIT_REVERSE, oss.str().c_str(), measure); + }; + + run(s_reference_target, elements_main.get(), VERBOSE /*=measure*/, "bit-reverse", 1); + run(s_main_target, elements_ref.get(), VERBOSE /*=measure*/, "bit-reverse", 1); + ASSERT_EQ(0, memcmp(elements_main.get(), elements_ref.get(), N * sizeof(TypeParam))); +} + +TYPED_TEST(FieldApiTest, Slice) +{ + const uint64_t N = 1 << 18; + const uint64_t offset = 2; + const uint64_t stride = 3; + const uint64_t size = 4; + + auto elements_main = std::make_unique(N); + auto elements_ref = std::make_unique(size); + auto elements_out = std::make_unique(size); + + FieldApiTest::random_samples(elements_main.get(), N); + + auto run = + [&](const std::string& dev_type, const TypeParam* in, TypeParam* out, bool measure, const char* msg, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + auto config = VecOpsConfig(); // Adjust configuration as needed + + std::ostringstream oss; + oss << dev_type << " " << msg; + + START_TIMER(SLICE) + for (int i = 0; i < iters; ++i) { + ICICLE_CHECK(slice(in, offset, stride, size, config, out)); + } + END_TIMER(SLICE, oss.str().c_str(), measure); + }; + + run(s_reference_target, elements_main.get(), elements_ref.get(), VERBOSE /*=measure*/, "slice", 1); + run(s_main_target, elements_main.get(), elements_out.get(), VERBOSE /*=measure*/, "slice", 1); + ASSERT_EQ(0, memcmp(elements_ref.get(), elements_out.get(), size * sizeof(TypeParam))); +} + +#ifdef NTT + +TYPED_TEST(FieldApiTest, ntt) +{ + // Randomize configuration + + int seed = time(0); + srand(seed); + const bool inplace = rand() % 2; + const int logn = rand() % 16 + 3; + const uint64_t N = 1 << logn; + const int log_ntt_domain_size = logn + 1; + const int log_batch_size = rand() % 3; + const int batch_size = 1 << log_batch_size; + const Ordering ordering = static_cast(rand() % 4); + bool columns_batch; + if (logn == 7 || logn < 4) { + columns_batch = false; // currently not supported (icicle_v3/backend/cuda/src/ntt/ntt.cuh line 578) + } else { + columns_batch = rand() % 2; + } + const NTTDir dir = static_cast(rand() % 2); // 0: forward, 1: inverse + const int log_coset_stride = rand() % 3; + scalar_t coset_gen; + if (log_coset_stride) { + coset_gen = scalar_t::omega(logn + log_coset_stride); + } else { + coset_gen = scalar_t::one(); + } + + const int total_size = N * batch_size; + auto scalars = std::make_unique(total_size); + FieldApiTest::random_samples(scalars.get(), total_size); + auto out_main = std::make_unique(total_size); + auto out_ref = std::make_unique(total_size); + auto run = [&](const std::string& dev_type, TypeParam* out, const char* msg, bool measure, int iters) { + Device dev = {dev_type, 0}; + icicle_set_device(dev); + icicleStreamHandle stream = nullptr; + ICICLE_CHECK(icicle_create_stream(&stream)); + auto init_domain_config = default_ntt_init_domain_config(); + init_domain_config.stream = stream; + init_domain_config.is_async = false; + ConfigExtension ext; + ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); + init_domain_config.ext = &ext; + auto config = default_ntt_config(); + config.stream = stream; + config.coset_gen = coset_gen; + config.batch_size = batch_size; // default: 1 + config.columns_batch = columns_batch; // default: false + config.ordering = ordering; // default: kNN + config.are_inputs_on_device = true; + config.are_outputs_on_device = true; + config.is_async = false; + ICICLE_CHECK(ntt_init_domain(scalar_t::omega(log_ntt_domain_size), init_domain_config)); + TypeParam *d_in, *d_out; + ICICLE_CHECK(icicle_malloc_async((void**)&d_in, total_size * sizeof(TypeParam), config.stream)); + ICICLE_CHECK(icicle_malloc_async((void**)&d_out, total_size * sizeof(TypeParam), config.stream)); + ICICLE_CHECK(icicle_copy_to_device_async(d_in, scalars.get(), total_size * sizeof(TypeParam), config.stream)); + std::ostringstream oss; + oss << dev_type << " " << msg; + START_TIMER(NTT_sync) + for (int i = 0; i < iters; ++i) { + if (inplace) { + ICICLE_CHECK(ntt(d_in, N, dir, config, d_in)); + } else { + ICICLE_CHECK(ntt(d_in, N, dir, config, d_out)); + } + } + END_TIMER(NTT_sync, oss.str().c_str(), measure); + + if (inplace) { + ICICLE_CHECK(icicle_copy_to_host_async(out, d_in, total_size * sizeof(TypeParam), config.stream)); + } else { + ICICLE_CHECK(icicle_copy_to_host_async(out, d_out, total_size * sizeof(TypeParam), config.stream)); + } + ICICLE_CHECK(icicle_free_async(d_in, config.stream)); + ICICLE_CHECK(icicle_free_async(d_out, config.stream)); + ICICLE_CHECK(icicle_stream_synchronize(config.stream)); + ICICLE_CHECK(icicle_destroy_stream(stream)); + ICICLE_CHECK(ntt_release_domain()); + }; + run(s_main_target, out_main.get(), "ntt", false /*=measure*/, 1 /*=iters*/); // warmup + run(s_reference_target, out_ref.get(), "ntt", VERBOSE /*=measure*/, 10 /*=iters*/); + run(s_main_target, out_main.get(), "ntt", VERBOSE /*=measure*/, 10 /*=iters*/); + ASSERT_EQ(0, memcmp(out_main.get(), out_ref.get(), total_size * sizeof(scalar_t))); +} +#endif // NTT + +int main(int argc, char** argv) +{ + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} \ No newline at end of file diff --git a/icicle/tests/polynomial_test.cu b/icicle/tests/test_polynomial_api.cpp similarity index 87% rename from icicle/tests/polynomial_test.cu rename to icicle/tests/test_polynomial_api.cpp index 9b433fa19..26034b8a9 100644 --- a/icicle/tests/polynomial_test.cu +++ b/icicle/tests/test_polynomial_api.cpp @@ -4,11 +4,13 @@ #include #include -#include "polynomials/polynomials.h" -#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" +#include "icicle/polynomials/polynomials.h" +#include "icicle/ntt.h" +#include "icicle/runtime.h" +#include "icicle/fields/field_config.h" -#include "ntt/ntt.cuh" -#include "gpu-utils/device_context.cuh" +using namespace field_config; +using namespace icicle; /*******************************************/ @@ -19,8 +21,6 @@ using FpMicroseconds = std::chrono::duration Polynomial_t; static uint64_t ceil_to_power_of_two(uint64_t x) { return 1ULL << uint64_t(ceil(log2(x))); } @@ -29,30 +29,37 @@ class PolynomialTest : public ::testing::Test { public: static inline const int MAX_NTT_LOG_SIZE = 24; - static inline const bool MEASURE = false; + static inline const bool MEASURE = true; + static inline std::vector s_registered_devices; // SetUpTestSuite/TearDownTestSuite are called once for the entire test suite static void SetUpTestSuite() { +#ifdef BACKEND_BUILD_DIR + setenv("ICICLE_BACKEND_INSTALL_DIR", BACKEND_BUILD_DIR, 0 /*=replace*/); +#endif + icicle_load_backend_from_env_or_default(); + s_registered_devices = get_registered_devices_list(); + // init NTT domain - auto ntt_config = ntt::default_ntt_config(); - const scalar_t basic_root = scalar_t::omega(MAX_NTT_LOG_SIZE); - ntt::init_domain(basic_root, ntt_config.ctx); - // initializing polynoimals factory for CUDA backend - Polynomial_t::initialize(std::make_unique>()); + for (const auto& dev_type : s_registered_devices) { + icicle_set_device({dev_type, 0}); + auto init_domain_config = default_ntt_init_domain_config(); + ntt_init_domain(scalar_t::omega(MAX_NTT_LOG_SIZE), init_domain_config); + } + + // choose random device for testing + srand(time(NULL)); + const int dev_idx = rand() % s_registered_devices.size(); + icicle_set_device(s_registered_devices.at(dev_idx)); + ICICLE_LOG_INFO << "setting device " << s_registered_devices.at(dev_idx) << " for polynomial tests"; } static void TearDownTestSuite() {} - void SetUp() override - { - // code that executes before each test - } - - void TearDown() override - { - // code that executes after each test - } + // SetUp/TearDown are called before and after each test + void SetUp() override {} + void TearDown() override {} static Polynomial_t randomize_polynomial(uint32_t size, bool random = true, bool from_evals = false) { @@ -108,10 +115,14 @@ class PolynomialTest : public ::testing::Test static Polynomial_t vanishing_polynomial(int degree) { - scalar_t coeffs_v[degree + 1] = {0}; + // scalar_t coeffs_v[degree + 1] = {0}; + auto coeffs_v = std::make_unique(degree + 1); + for (int i = 0; i < degree + 1; ++i) { + coeffs_v[i] = scalar_t::zero(); + } coeffs_v[0] = minus_one; // -1 coeffs_v[degree] = one; // +x^n - auto v = Polynomial_t::from_coefficients(coeffs_v, degree + 1); + auto v = Polynomial_t::from_coefficients(coeffs_v.get(), degree + 1); return v; } @@ -143,8 +154,8 @@ TEST_F(PolynomialTest, evaluationOnDomain) auto f = PolynomialTest::randomize_polynomial(size); size *= 2; // evaluating on a larger domain - auto default_device_context = device_context::get_default_device_context(); - const auto w = ntt::get_root_of_unity_from_domain((int)log2(size), default_device_context); + scalar_t w; + ICICLE_CHECK(get_root_of_unity_from_domain((int)log2(size), &w)); // construct domain as rou scalar_t x = one; @@ -175,8 +186,10 @@ TEST_F(PolynomialTest, evaluateOnRouDomain) // build domain auto test = [&](auto& p, int domain_logsize) { const int domain_size = 1 << domain_logsize; - device_context::DeviceContext ctx = device_context::get_default_device_context(); - scalar_t w = ntt::get_root_of_unity_from_domain(domain_logsize, ctx); + + scalar_t w; + ICICLE_CHECK(get_root_of_unity_from_domain(domain_logsize, &w)); + auto domain = std::make_unique(domain_size); domain[0] = scalar_t::one(); for (int i = 1; i < domain_size; ++i) { @@ -224,7 +237,7 @@ TEST_F(PolynomialTest, fromEvaluations) // evaluate f on roots of unity scalar_t omega = scalar_t::omega(log_size); - scalar_t evals[nof_evals] = {0}; + auto evals = std::make_unique(nof_evals); scalar_t x = one; for (int i = 0; i < nof_evals; ++i) { evals[i] = f(x); @@ -232,7 +245,7 @@ TEST_F(PolynomialTest, fromEvaluations) } // construct g from f's evaluations - auto g = Polynomial_t::from_rou_evaluations(evals, nof_evals); + auto g = Polynomial_t::from_rou_evaluations(evals.get(), nof_evals); // make sure they are equal, that is f-g=0 auto h = f - g; @@ -383,7 +396,7 @@ TEST_F(PolynomialTest, ReadCoeffsToHost) auto nof_coeffs = h.copy_coeffs(h_coeffs, 0, 2); // read the coefficients EXPECT_EQ(nof_coeffs, 3); // expecting 3 due to specified indices - scalar_t expected_h_coeffs[nof_coeffs] = {one, two, three}; + scalar_t expected_h_coeffs[3] = {one, two, three}; for (int i = 0; i < nof_coeffs; ++i) { EXPECT_EQ(expected_h_coeffs[i], h_coeffs[i]); } @@ -471,7 +484,7 @@ TEST_F(PolynomialTest, View) const int size = 1 << 6; auto f = randomize_polynomial(size); - auto [d_coeff, N, device_id] = f.get_coefficients_view(); + auto [d_coeff, N] = f.get_coefficients_view(); EXPECT_EQ(d_coeff.isValid(), true); auto g = f + f; @@ -515,36 +528,15 @@ TEST_F(PolynomialTest, slicing) body((1 << 10) - 1); // test odd size } -#ifdef CURVE -#include "msm/msm.cuh" -#include "curves/curve_config.cuh" +#ifdef MSM +#include "icicle/msm.h" +#include "icicle/curves/curve_config.h" using curve_config::affine_t; using curve_config::projective_t; -// using the MSM C-API directly since msm::MSM() symbol is hidden in icicle lib and I cannot understand why -namespace msm { - extern "C" cudaError_t CONCAT_EXPAND(CURVE, msm_cuda)( - const scalar_t* scalars, const affine_t* points, int msm_size, MSMConfig& config, projective_t* out); - cudaError_t _msm(const scalar_t* scalars, const affine_t* points, int msm_size, MSMConfig& config, projective_t* out) - { - return CONCAT_EXPAND(CURVE, msm_cuda)(scalars, points, msm_size, config, out); - } -} // namespace msm - #ifdef G2 using curve_config::g2_affine_t; using curve_config::g2_projective_t; - -namespace msm { - extern "C" cudaError_t CONCAT_EXPAND(CURVE, g2_msm_cuda)( - const scalar_t* scalars, const g2_affine_t* points, int msm_size, MSMConfig& config, g2_projective_t* out); - - cudaError_t - _g2_msm(const scalar_t* scalars, const g2_affine_t* points, int msm_size, MSMConfig& config, g2_projective_t* out) - { - return CONCAT_EXPAND(CURVE, g2_msm_cuda)(scalars, points, msm_size, config, out); - } -} // namespace msm #endif // G2 class dummy_g2_t : public scalar_t @@ -570,21 +562,22 @@ class dummy_g2_t : public scalar_t return dummy_g2_t{scalar_t::sub_modulus<1>(rs)}; } }; -namespace msm { - cudaError_t - _g2_msm(const scalar_t* scalars, const dummy_g2_t* points, int msm_size, MSMConfig& config, dummy_g2_t* out) + +namespace icicle { + template <> + eIcicleError + msm(const scalar_t* scalars, const dummy_g2_t* points, int msm_size, const MSMConfig& config, dummy_g2_t* out) { scalar_t* scalars_host = static_cast(malloc(msm_size * sizeof(scalar_t))); - cudaMemcpyAsync(scalars_host, scalars, msm_size * sizeof(scalar_t), cudaMemcpyDeviceToHost, config.ctx.stream); + ICICLE_CHECK(icicle_copy_to_host(scalars_host, scalars, msm_size * sizeof(scalar_t))); *out = dummy_g2_t::zero(); for (int i = 0; i < msm_size; i++) *out = *out + scalars_host[i] * points[i]; free(scalars_host); - return cudaSuccess; + return eIcicleError::SUCCESS; } - -} // namespace msm -#endif // CURVE +} // namespace icicle +#endif // MSM // Following examples are randomizing N private numbers and proving that I know N numbers such that their product is // equal to 'out'. @@ -690,7 +683,7 @@ class Groth16Example const int nof_constraints; std::vector L_QAP, R_QAP, O_QAP; -#ifdef CURVE +#ifdef MSM /******** SETUP *********/ // https://static.wixstatic.com/media/935a00_cd68860dafbb4ebe8f166de5cc8cc50c~mv2.png struct ToxicWaste { @@ -837,14 +830,14 @@ class Groth16Example const int vanishing_poly_deg = n; Polynomial_t h = (U * V - W).divide_by_vanishing_polynomial(vanishing_poly_deg); - auto msm_config = msm::default_msm_config(); + auto msm_config = default_msm_config(); msm_config.are_scalars_on_device = true; // compute [A]1 { G1P U_commited; - auto [U_coeff, N, device_id] = U.get_coefficients_view(); - CHK_STICKY(msm::_msm(U_coeff.get(), pk.g1.powers_of_tau.data(), n, msm_config, &U_commited)); + auto [U_coeff, N] = U.get_coefficients_view(); + ICICLE_CHECK(msm(U_coeff.get(), pk.g1.powers_of_tau.data(), n, msm_config, &U_commited)); proof.A = G1P::to_affine(U_commited + G1P::from_affine(pk.g1.alpha) + r * G1P::from_affine(pk.g1.delta)); } @@ -852,26 +845,26 @@ class Groth16Example G1P B1; { G2P V_commited_g2; - auto [V_coeff, N, device_id] = V.get_coefficients_view(); - CHK_STICKY(msm::_g2_msm(V_coeff.get(), pk.g2.powers_of_tau.data(), n, msm_config, &V_commited_g2)); + auto [V_coeff, N] = V.get_coefficients_view(); + ICICLE_CHECK(msm(V_coeff.get(), pk.g2.powers_of_tau.data(), n, msm_config, &V_commited_g2)); proof.B = G2P::to_affine(V_commited_g2 + pk.g2.beta + s * G2P::from_affine(pk.g2.delta)); G1P V_commited_g1; - CHK_STICKY(msm::_msm(V_coeff.get(), pk.g1.powers_of_tau.data(), n, msm_config, &V_commited_g1)); + ICICLE_CHECK(msm(V_coeff.get(), pk.g1.powers_of_tau.data(), n, msm_config, &V_commited_g1)); B1 = V_commited_g1 + pk.g1.beta + G1P::from_affine(pk.g1.delta) * s; } // compute [C]1 { - auto [H_coeff, N, device_id] = h.get_coefficients_view(); + auto [H_coeff, N] = h.get_coefficients_view(); G1P HT_commited; - CHK_STICKY(msm::_msm(H_coeff.get(), pk.g1.vanishing_poly_points.data(), n - 1, msm_config, &HT_commited)); + ICICLE_CHECK(msm(H_coeff.get(), pk.g1.vanishing_poly_points.data(), n - 1, msm_config, &HT_commited)); G1P private_inputs_commited; msm_config.are_scalars_on_device = false; - CHK_STICKY(msm::_msm( - witness.data() + l + 1, pk.g1.private_witness_points.data(), m - l, msm_config, &private_inputs_commited)); + ICICLE_CHECK( + msm(witness.data() + l + 1, pk.g1.private_witness_points.data(), m - l, msm_config, &private_inputs_commited)); proof.C = G1P::to_affine( private_inputs_commited + HT_commited + G1P::from_affine(proof.A) * s + B1 * r - @@ -901,7 +894,7 @@ class Groth16Example ProvingKey pk; VerifyingKey vk; -#endif // CURVE +#endif // MSM }; TEST_F(PolynomialTest, QAP) @@ -930,9 +923,9 @@ TEST_F(PolynomialTest, QAP) const int vanishing_poly_deg = ceil_to_power_of_two(nof_constraints); // (4) sanity check: verify AB=C at the evaluation points { - auto default_device_context = device_context::get_default_device_context(); - const auto w = - ntt::get_root_of_unity_from_domain((int)ceil(log2(nof_constraints)), default_device_context); + scalar_t w; + ICICLE_CHECK(get_root_of_unity_from_domain((int)ceil(log2(nof_constraints)), &w)); + auto x = scalar_t::one(); for (int i = 0; i < vanishing_poly_deg; ++i) { ASSERT_EQ(Lx(x) * Rx(x), Ox(x)); @@ -951,14 +944,14 @@ TEST_F(PolynomialTest, QAP) } } -#ifdef CURVE +#ifdef MSM TEST_F(PolynomialTest, commitMSM) { const int size = 1 << 6; auto f = randomize_polynomial(size); - auto [d_coeff, N, device_id] = f.get_coefficients_view(); - auto msm_config = msm::default_msm_config(); + auto [d_coeff, N] = f.get_coefficients_view(); + auto msm_config = default_msm_config(); msm_config.are_scalars_on_device = true; auto points = std::make_unique(size); @@ -969,7 +962,7 @@ TEST_F(PolynomialTest, commitMSM) compute_powers_of_tau(g, tau, points.get(), size); EXPECT_EQ(d_coeff.isValid(), true); - CHK_STICKY(msm::_msm(d_coeff.get(), points.get(), size, msm_config, &result)); + ICICLE_CHECK(msm(d_coeff.get(), points.get(), size, msm_config, &result)); EXPECT_EQ(result, f(tau) * g); @@ -1009,7 +1002,7 @@ TEST_F(PolynomialTest, Groth16) } #endif // G2 -#endif // CURVE +#endif // MSM int main(int argc, char** argv) { diff --git a/scripts/format_all.sh b/scripts/format_all.sh new file mode 100755 index 000000000..1b0ee08c3 --- /dev/null +++ b/scripts/format_all.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +# Check if directory is provided as an argument +if [ -z "$1" ]; then + echo "Usage: $0 " + exit 1 +fi + +DIRECTORY="$1" + +# Find and format all C, C++, header, and other relevant files +find "$DIRECTORY" -name '*.c' -o -name '*.cpp' -o -name '*.cc' -o -name '*.h' -o -name '*.hpp' -o -name '*.cu' -o -name '*.cuh' | while read -r file; do + echo "Formatting $file" + clang-format -i "$file" +done + +echo "All files formatted." diff --git a/scripts/gen_c_api.py b/scripts/gen_c_api.py index 4e7fae795..3c031a048 100755 --- a/scripts/gen_c_api.py +++ b/scripts/gen_c_api.py @@ -7,7 +7,7 @@ from pathlib import Path from string import Template -API_PATH = Path(__file__).resolve().parent.parent.joinpath("icicle").joinpath("include").joinpath("api") +API_PATH = Path(__file__).resolve().parent.parent.joinpath("icicle").joinpath("include").joinpath("icicle").joinpath("api") TEMPLATES_PATH = API_PATH.joinpath("templates") """ @@ -16,37 +16,37 @@ """ CURVES_CONFIG = { "bn254": [ - "field_ext.h", - "vec_ops_ext.h", - "ntt_ext.h", + "field_ext.template", + "vec_ops_ext.template", + "ntt_ext.template", ], "bls12_381": [ - "poseidon2.h", - "field_ext.h", - "vec_ops_ext.h", - "ntt_ext.h", + # "poseidon2.template", + "field_ext.template", + "vec_ops_ext.template", + "ntt_ext.template", ], "bls12_377": [ - "poseidon2.h", - "field_ext.h", - "vec_ops_ext.h", - "ntt_ext.h", + # "poseidon2.template", + "field_ext.template", + "vec_ops_ext.template", + "ntt_ext.template", ], "bw6_761": [ - "poseidon2.h", - "field_ext.h", - "vec_ops_ext.h", - "ntt_ext.h", + # "poseidon2.template", + "field_ext.template", + "vec_ops_ext.template", + "ntt_ext.template", ], "grumpkin": { - "poseidon2.h", - "curve_g2.h", - "msm_g2.h", - "ecntt.h", - "ntt.h", - "vec_ops_ext.h", - "field_ext.h", - "ntt_ext.h", + # "poseidon2.template", + "curve_g2.template", + "msm_g2.template", + "ecntt.template", + "ntt.template", + "vec_ops_ext.template", + "field_ext.template", + "ntt_ext.template", }, } @@ -56,30 +56,24 @@ """ FIELDS_CONFIG = { "babybear": { - "poseidon.h", + # "poseidon.template", }, "stark252": { - "poseidon.h", - "poseidon2.h", - "field_ext.h", - "vec_ops_ext.h", - "ntt_ext.h", + # "poseidon.template", + # "poseidon2.template", + "field_ext.template", + "vec_ops_ext.template", + "ntt_ext.template", }, - "m31": { - "ntt_ext.h", - "ntt.h", - "poseidon.h", - "poseidon2.h", - } + # "m31": { + # "ntt_ext.template", + # "ntt.template", + # "poseidon.template", + # "poseidon2.template", + # } } -# For cudaError_t and device_context -COMMON_INCLUDES = [ - '#include ', - '#include "gpu-utils/device_context.cuh"', - '#include "merkle-tree/merkle.cuh"', - '#include "matrix/matrix.cuh"' -] +COMMON_INCLUDES = [] WARN_TEXT = """\ // WARNING: This file is auto-generated by a script. @@ -91,8 +85,6 @@ INCLUDE_ONCE = """\ #pragma once -#ifndef {0}_API_H -#define {0}_API_H """ @@ -109,17 +101,17 @@ # Collect includes includes = COMMON_INCLUDES.copy() - includes.append(f'#include "curves/params/{curve}.cuh"') + includes.append(f'#include "icicle/curves/params/{curve}.h"') if any(header.name.startswith("ntt") for header in headers): - includes.append('#include "ntt/ntt.cuh"') + includes.append('#include "icicle/ntt.h"') if any(header.name.startswith("msm") for header in headers): - includes.append('#include "msm/msm.cuh"') + includes.append('#include "icicle/msm.h"') if any(header.name.startswith("vec_ops") for header in headers): - includes.append('#include "vec_ops/vec_ops.cuh"') + includes.append('#include "icicle/vec_ops.h"') if any(header.name.startswith("poseidon.h") for header in headers): - includes.append('#include "poseidon/poseidon.cuh"') + includes.append('#include "poseidon/poseidon.h"') if any(header.name.startswith("poseidon2.h") for header in headers): - includes.append('#include "poseidon2/poseidon2.cuh"') + includes.append('#include "poseidon2/poseidon2.h"') contents = WARN_TEXT + INCLUDE_ONCE.format(curve.upper()) + "\n".join(includes) + "\n\n" for header in headers: @@ -129,8 +121,7 @@ "CURVE": curve, "FIELD": curve, }) - contents += "\n\n" - contents += "#endif" + contents += "\n\n" with open(curve_api, "w") as f: f.write(contents) @@ -144,15 +135,15 @@ # Collect includes includes = COMMON_INCLUDES.copy() - includes.append(f'#include "fields/stark_fields/{field}.cuh"') + includes.append(f'#include "icicle/fields/stark_fields/{field}.h"') if any(header.name.startswith("ntt") for header in headers): - includes.append('#include "ntt/ntt.cuh"') + includes.append('#include "icicle/ntt.h"') if any(header.name.startswith("vec_ops") for header in headers): - includes.append('#include "vec_ops/vec_ops.cuh"') + includes.append('#include "icicle/vec_ops.h"') if any(header.name.startswith("poseidon.h") for header in headers): - includes.append('#include "poseidon/poseidon.cuh"') + includes.append('#include "icicle/poseidon/poseidon.h"') if any(header.name.startswith("poseidon2.h") for header in headers): - includes.append('#include "poseidon2/poseidon2.cuh"') + includes.append('#include "icicle/poseidon2/poseidon2.h"') contents = WARN_TEXT + INCLUDE_ONCE.format(field.upper()) + "\n".join(includes) + "\n\n" for header in headers: @@ -161,8 +152,7 @@ contents += template.safe_substitute({ "FIELD": field, }) - contents += "\n\n" - contents += "#endif" + contents += "\n\n" with open(field_api, "w") as f: f.write(contents) \ No newline at end of file diff --git a/scripts/pull_cuda_backend.sh b/scripts/pull_cuda_backend.sh new file mode 100755 index 000000000..a6b348423 --- /dev/null +++ b/scripts/pull_cuda_backend.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +CUDA_BACKEND=${1:-main} +BACKEND_DIR=${2:-backend/cuda} + +if [ "$CUDA_BACKEND" != "OFF" ]; then + # Get the absolute path of the backend directory + ABS_BACKEND_DIR=$(realpath ${BACKEND_DIR}) + + echo "Trying to pull CUDA backend commit '${CUDA_BACKEND}' to '${ABS_BACKEND_DIR}'" + + if [ -d "${BACKEND_DIR}" ] && [ "$(ls -A ${BACKEND_DIR})" ]; then + echo "Directory ${BACKEND_DIR} is not empty. Fetching and resetting..." + cd ${BACKEND_DIR} + git fetch origin + git reset --hard origin/${CUDA_BACKEND} + else + echo "Directory ${BACKEND_DIR} is empty or does not exist. Cloning..." + mkdir -p ${BACKEND_DIR} + cd ${BACKEND_DIR} + git clone git@github.com:ingonyama-zk/icicle-cuda-backend.git ${ABS_BACKEND_DIR} + git checkout ${CUDA_BACKEND} + fi +fi \ No newline at end of file diff --git a/scripts/release/Dockerfile.ubi8 b/scripts/release/Dockerfile.ubi8 new file mode 100644 index 000000000..88d434bee --- /dev/null +++ b/scripts/release/Dockerfile.ubi8 @@ -0,0 +1,5 @@ +# Use the official NVIDIA CUDA development image for ubi8 - rhel compatible +FROM nvidia/cuda:12.2.2-devel-ubi8 + +# install cmake +RUN dnf update -y && dnf install -y cmake diff --git a/scripts/release/Dockerfile.ubi9 b/scripts/release/Dockerfile.ubi9 new file mode 100644 index 000000000..163db6f58 --- /dev/null +++ b/scripts/release/Dockerfile.ubi9 @@ -0,0 +1,5 @@ +# Use the official NVIDIA CUDA development image for ubi9 - rhel compatible +FROM nvidia/cuda:12.2.2-devel-ubi9 + +# install cmake +RUN dnf update -y && dnf install -y cmake \ No newline at end of file diff --git a/scripts/release/Dockerfile.ubuntu20 b/scripts/release/Dockerfile.ubuntu20 new file mode 100644 index 000000000..e9e3d57d7 --- /dev/null +++ b/scripts/release/Dockerfile.ubuntu20 @@ -0,0 +1,29 @@ +# Use the official NVIDIA CUDA development image for Ubuntu 20.04 +FROM nvidia/cuda:12.2.2-devel-ubuntu20.04 + +# Prevent interactive prompts during package installation +ENV DEBIAN_FRONTEND=noninteractive + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + build-essential \ + wget \ + tar \ + libssl-dev \ + libcurl4-openssl-dev \ + libarchive-dev \ + zlib1g-dev + +# Install the latest stable version of CMake from source +RUN CMAKE_VERSION=3.27.4 \ + && wget https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz \ + && tar -zxvf cmake-${CMAKE_VERSION}.tar.gz \ + && cd cmake-${CMAKE_VERSION} \ + && ./bootstrap \ + && make -j$(nproc) \ + && make install \ + && cd .. \ + && rm -rf cmake-${CMAKE_VERSION} cmake-${CMAKE_VERSION}.tar.gz + +# Set CMake as the default version +RUN ln -sf /usr/local/bin/cmake /usr/bin/cmake \ No newline at end of file diff --git a/scripts/release/Dockerfile.ubuntu22 b/scripts/release/Dockerfile.ubuntu22 new file mode 100644 index 000000000..e64ba489e --- /dev/null +++ b/scripts/release/Dockerfile.ubuntu22 @@ -0,0 +1,10 @@ +# Use the official NVIDIA development runtime image for Ubuntu 22.04 +FROM nvidia/cuda:12.2.2-devel-ubuntu22.04 + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + build-essential \ + cmake \ + tar + +RUN apt install cargo -y diff --git a/scripts/release/README.md b/scripts/release/README.md new file mode 100644 index 000000000..8caa8d420 --- /dev/null +++ b/scripts/release/README.md @@ -0,0 +1,46 @@ +# Build the release + +This section is describing how a release is generated, given the release sources.
+We use docker to represent the target environment for the release. Each Docker image is tailored to a specific distribution and CUDA version. You first build the Docker image, which sets up the environment, and then use this Docker image to build the release tar file. This ensures that the build process is consistent and reproducible across different environments. + +## Build full release + +To build all tars: +```bash +# from icicle root dir +mkdir -p release_output && rm -rf release_output/* # output dir where tars will be placed +./scripts/release/build_all.sh release_output # release_output is the output dir where tar files will be generated to +``` + +### Build Docker Image + +The Docker images represent the target environment for the release. + +To build the Docker images for each distribution and CUDA version, use the following commands: + +```bash +cd ./scripts/release +# Ubuntu 22.04, CUDA 12.2.2 +docker build -t icicle-release-ubuntu22-cuda122 -f Dockerfile.ubuntu22 . +``` + +## Build Libraries Inside the Docker + +To build the Icicle libraries inside a Docker container and output the tar file to the `release_output` directory: + +```bash +# from icicle root dir +mkdir -p release_output +# ubuntu 22 +docker run --rm --gpus all \ + -v ./icicle:/icicle \ + -v ./release_output:/output \ + -v ./scripts:/scripts \ + icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh icicle30 ubuntu22 cuda122 +``` + +This command executes the `build_release_and_tar.sh` script inside the Docker container, which provides the build environment. It maps the source code and output directory to the container, ensuring the generated tar file is available on the host system. + +You can replace `icicle-release-ubuntu22-cuda122` with another Docker image tag to build in the corresponding environment. +Make sure to pass corresponding OS and CUDA version in the params `icicle30 ubuntu22 cuda122`. For example for ubi9 it would be `icicle30 ubi9 cuda122`. +See `build_all.sh` script for reference. diff --git a/scripts/release/build_all.sh b/scripts/release/build_all.sh new file mode 100755 index 000000000..1d5c4089b --- /dev/null +++ b/scripts/release/build_all.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +set -e + +# Use provided release_output directory or default to "release_output" +output_dir="${1:-/release_output}" + +# Check if both directories exist in the current working directory +if [[ ! -d "./icicle" || ! -d "./scripts" ]]; then + echo "Usage: The current directory must contain both 'icicle' and 'scripts' directories. Retry from icicle root dir." + exit 1 +fi + +# Build Docker images + +# Ubuntu 22.04, CUDA 12.2.2 +docker build -t icicle-release-ubuntu22-cuda122 -f ./scripts/release/Dockerfile.ubuntu22 . +# Ubuntu 20.04, CUDA 12.2.2 +docker build -t icicle-release-ubuntu20-cuda122 -f ./scripts/release/Dockerfile.ubuntu20 . +# ubi8 (rhel compatible), CUDA 12.2.2 +docker build -t icicle-release-ubi8-cuda122 -f ./scripts/release/Dockerfile.ubi8 . +# ubi9 (rhel compatible), CUDA 12.2.2 +docker build -t icicle-release-ubi9-cuda122 -f ./scripts/release/Dockerfile.ubi9 . + +# Compile and tar release in each + +# Create the output directory if it doesn't exist, and clean it +mkdir -p "$output_dir" && rm -rf "$output_dir/*" + +# ubuntu 22 +docker run --rm --gpus all \ + -v ./icicle:/icicle \ + -v "$output_dir:/output" \ + -v ./scripts:/scripts \ + icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh icicle30 ubuntu22 cuda122 + +# ubuntu 20 +docker run --rm --gpus all \ + -v ./icicle:/icicle \ + -v "$output_dir:/output" \ + -v ./scripts:/scripts \ + icicle-release-ubuntu20-cuda122 bash /scripts/release/build_release_and_tar.sh icicle30 ubuntu20 cuda122 + +# ubi 8 (rhel compatible) +docker run --rm --gpus all \ + -v ./icicle:/icicle \ + -v "$output_dir:/output" \ + -v ./scripts:/scripts \ + icicle-release-ubi8-cuda122 bash /scripts/release/build_release_and_tar.sh icicle30 ubi8 cuda122 + +# ubi 9 (rhel compatible) +docker run --rm --gpus all \ + -v ./icicle:/icicle \ + -v "$output_dir:/output" \ + -v ./scripts:/scripts \ + icicle-release-ubi9-cuda122 bash /scripts/release/build_release_and_tar.sh icicle30 ubi9 cuda122 diff --git a/scripts/release/build_release_and_tar.sh b/scripts/release/build_release_and_tar.sh new file mode 100755 index 000000000..c786c3298 --- /dev/null +++ b/scripts/release/build_release_and_tar.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +set -e + +# Accept ICICLE_VERSION, ICICLE_OS, and ICICLE_CUDA_VERSION as inputs or use defaults +ICICLE_VERSION=${1:-icicle30} # Default to "icicle30" if not set +ICICLE_OS=${2:-unknown_os} # Default to "unknown_os" if not set +ICICLE_CUDA_VERSION=${3:-cuda_unknown} # Default to "cuda_unknown" if not set + +# List of fields and curves +fields=("babybear" "stark252" "m31") +curves=("bn254" "bls12_381" "bls12_377" "bw6_761" "grumpkin") + +cd / +mkdir -p install_dir/icicle # output dir that is tarred + +# Iterate over fields +for field in "${fields[@]}"; do + echo "Building for field: $field" + + mkdir -p build && rm -rf build/* + # Configure, build, and install + cmake -S icicle -B build -DFIELD=$field -DCUDA_BACKEND=local -DCMAKE_INSTALL_PREFIX=install_dir/icicle -DCMAKE_BUILD_TYPE=Release + cmake --build build -j # build + cmake --install build # install +done + +# Iterate over curves +for curve in "${curves[@]}"; do + echo "Building for curve: $curve" + + mkdir -p build && rm -rf build/* + # Configure, build, and install + cmake -S icicle -B build -DCURVE=$curve -DCUDA_BACKEND=local -DCMAKE_INSTALL_PREFIX=install_dir/icicle -DCMAKE_BUILD_TYPE=Release + cmake --build build -j # build + cmake --install build # install +done + +# Split CUDA binaries to a separate directory to tar them separately +mkdir -p install_dir_cuda_only/icicle/lib/backend +mv install_dir/icicle/lib/backend/* install_dir_cuda_only/icicle/lib/backend + +# Copy headers +cp -r ./icicle/include install_dir/icicle + +# Create the tarball for frontend libraries +cd install_dir +tar -czvf /output/${ICICLE_VERSION}-${ICICLE_OS}.tar.gz icicle # tar the install dir + +# Create tarball for CUDA backend +cd ../install_dir_cuda_only +tar -czvf /output/${ICICLE_VERSION}-${ICICLE_OS}-${ICICLE_CUDA_VERSION}.tar.gz icicle # tar the install dir \ No newline at end of file diff --git a/wrappers/golang/README.md b/wrappers/golang/README.md index dbd50cb6d..c78ab60c2 100644 --- a/wrappers/golang/README.md +++ b/wrappers/golang/README.md @@ -4,26 +4,29 @@ In order to build the underlying ICICLE libraries you should run the build scrip Build script USAGE -```bash -./build.sh [-curve= | -field=] [-cuda_version=] [-g2] [-ecntt] [-devmode] - -curve - The name of the curve to build or "all" to build all curves -field - The name of the field to build or "all" to build all fields --g2 - Optional - build with G2 enabled --ecntt - Optional - build with ECNTT enabled --devmode - Optional - build in devmode +```sh +./build.sh [-curve=] [-field=] [-hash=] [-cuda_version=] [-skip_msm] [-skip_ntt] [-skip_g2] [-skip_ecntt] [-skip_fieldext] + +curve - The name of the curve to build or "all" to build all supported curves +field - The name of the field to build or "all" to build all supported fields +-skip_msm - Optional - build with MSM disabled +-skip_ntt - Optional - build with NTT disabled +-skip_g2 - Optional - build with G2 disabled +-skip_ecntt - Optional - build with ECNTT disabled +-skip_fieldext - Optional - build without field extension +-help - Optional - Displays usage information ``` To build ICICLE libraries for all supported curves with G2 and ECNTT enabled. ```sh -./build.sh -curve=all -g2 -ecntt +./build.sh -curve=all ``` If you wish to build for a specific curve, for example bn254, without G2 or ECNTT enabled. ```sh -./build.sh -curve=bn254 +./build.sh -curve=bn254 -skip_g2 -skip_ecntt ``` ## Supported curves, fields and operations @@ -53,7 +56,7 @@ If you wish to build for a specific curve, for example bn254, without G2 or ECNT To run the tests for curve bn254. ```sh -go test ./wrappers/golang/curves/bn254/tests -count=1 -v +go test ./wrappers/golang_v3/curves/bn254/tests -count=1 -v ``` To run all the tests in the golang bindings @@ -64,15 +67,15 @@ go test ./... -count=1 -v ## How do Golang bindings work? -The libraries produced from the CUDA code compilation are used to bind Golang to ICICLE's CUDA code. +The libraries produced from the code compilation are used to bind Golang to ICICLE's code. -1. These libraries (named `libingo_curve_.a` and `libingo_field_.a`) can be imported in your Go project to leverage the GPU accelerated functionalities provided by ICICLE. +1. These libraries (named `libicicle_curve_.so` and `libicicle_field_.so`) can be imported in your Go project to leverage the accelerated functionalities provided by ICICLE. 2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: ```go /* -#cgo LDFLAGS: -L$/path/to/shared/libs -lingo_curve_bn254 -L$/path/to/shared/libs -lingo_field_bn254 -lstdc++ -lm +#cgo LDFLAGS: -L/path/to/shared/libs -licicle_device -lstdc++ -lm -Wl,-rpath=/path/to/shared/libs #include "icicle.h" // make sure you use the correct header file(s) */ import "C" @@ -84,38 +87,3 @@ func main() { ``` Replace `/path/to/shared/libs` with the actual path where the shared libraries are located on your system. - -## Common issues - -### Cannot find shared library - -In some cases you may encounter the following error, despite exporting the correct `LD_LIBRARY_PATH`. - -```sh -/usr/local/go/pkg/tool/linux_amd64/link: running gcc failed: exit status 1 -/usr/bin/ld: cannot find -lbn254: No such file or directory -/usr/bin/ld: cannot find -lbn254: No such file or directory -/usr/bin/ld: cannot find -lbn254: No such file or directory -/usr/bin/ld: cannot find -lbn254: No such file or directory -/usr/bin/ld: cannot find -lbn254: No such file or directory -collect2: error: ld returned 1 exit status -``` - -This is normally fixed by exporting the path to the shared library location in the following way: `export CGO_LDFLAGS="-L//"` - -### cuda_runtime.h: No such file or directory - -```sh -# github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bls12381 -In file included from wrappers/golang/curves/bls12381/curve.go:5: -wrappers/golang/curves/bls12381/include/curve.h:1:10: fatal error: cuda_runtime.h: No such file or directory - 1 | #include - | ^~~~~~~~~~~~~~~~ -compilation terminated. -``` - -Our golang bindings rely on cuda headers and require that they can be found as system headers. Make sure to add the `cuda/include` of your cuda installation to your CPATH - -```sh -export CPATH=$CPATH: -``` diff --git a/wrappers/golang/build.ps1 b/wrappers/golang/build.ps1 deleted file mode 100644 index d64e54bf6..000000000 --- a/wrappers/golang/build.ps1 +++ /dev/null @@ -1,23 +0,0 @@ -$G2_DEFINED = "OFF" - -if ($args.Count -gt 1) { - $G2_DEFINED = "ON" -} - -$BUILD_DIR = (Get-Location).Path + "\..\icicle\build" -$SUPPORTED_CURVES = @("bn254", "bls12_377", "bls12_381", "bw6_761") - -if ($args[0] -eq "all") { - $BUILD_CURVES = $SUPPORTED_CURVES -} else { - $BUILD_CURVES = @($args[0]) -} - -Set-Location "../../icicle" - -New-Item -ItemType Directory -Path "build" -Force - -foreach ($CURVE in $BUILD_CURVES) { - cmake -DCURVE:STRING=$CURVE -DG2_DEFINED:STRING=$G2_DEFINED -DCMAKE_BUILD_TYPE:STRING=Release -S . -B build - cmake --build build -} diff --git a/wrappers/golang/build.sh b/wrappers/golang/build.sh index caef755ec..2698dd3a2 100755 --- a/wrappers/golang/build.sh +++ b/wrappers/golang/build.sh @@ -1,17 +1,22 @@ #!/bin/bash -G2_DEFINED=OFF -ECNTT_DEFINED=OFF +MSM_DEFINED=ON +NTT_DEFINED=ON +G2_DEFINED=ON +ECNTT_DEFINED=ON +EXT_FIELD=ON + CUDA_COMPILER_PATH=/usr/local/cuda/bin/nvcc + DEVMODE=OFF -EXT_FIELD=OFF BUILD_CURVES=( ) BUILD_FIELDS=( ) -BUILD_HASHES=( ) SUPPORTED_CURVES=("bn254" "bls12_377" "bls12_381" "bw6_761", "grumpkin") SUPPORTED_FIELDS=("babybear") -SUPPORTED_HASHES=("keccak") +CUDA_BACKEND=OFF + +BUILD_DIR="${ICICLE_BUILD_DIR:-$(realpath "$PWD/../../icicle/build")}" if [[ $1 == "-help" ]]; then echo "Build script for building ICICLE cpp libraries" @@ -21,15 +26,27 @@ if [[ $1 == "-help" ]]; then echo "USAGE: ./build.sh [OPTION...]" echo "" echo "OPTIONS:" - echo " -curve= The curve that should be built. If \"all\" is supplied," - echo " all curves will be built with any other supplied curve options" - echo " -g2 Builds the curve lib with G2 enabled" - echo " -ecntt Builds the curve lib with ECNTT enabled" - echo " -field= The field that should be built. If \"all\" is supplied," - echo " all fields will be built with any other supplied field options" - echo " -field-ext Builds the field lib with the extension field enabled" - echo " -devmode Enables devmode debugging and fast build times" - echo " -cuda_version= The version of cuda to use for compiling" + echo " -curve= Specifies the curve to be built. If \"all\" is supplied," + echo " all curves will be built with any additional curve options." + echo "" + echo " -skip_msm Builds the curve library with MSM (multi-scalar multiplication) disabled." + echo "" + echo " -skip_ntt Builds the curve/field library with NTT (number theoretic transform) disabled." + echo "" + echo " -skip_g2 Builds the curve library with G2 (a secondary group) disabled." + echo "" + echo " -skip_ecntt Builds the curve library with ECNTT (elliptic curve NTT) disabled." + echo "" + echo " -field= Specifies the field to be built. If \"all\" is supplied," + echo " all fields will be built with any additional field options." + echo "" + echo " -skip_fieldext Builds the field library with the extension field disabled." + echo "" + echo " -cuda_backend=