Rockcress

This is a fork of the gem5 simulator repository with a bunch of other stuff for the Rockcress architecture.

There are two ways to set up the infrastructure: an automated way inside a Docker container, or a manual way on your host machine.

The following instructions use top/ as an alias for the top level of this repository.

Environment Setup with Docker:

1. Install Docker.
2. cd top/docker && ./enter_docker.sh (This step will automatically fetch the Docker image from GitHub packages.)

That's all you need to get started. In the unlikely event you want to rebuild the Docker container from scratch, type this in the top/docker directory:

docker build -t ghcr.io/cucapra/gem5-mesh:latest .

Environment Setup, Natively (Docker-Free):

If you don't want to use Docker, you can install things manually. You'll need to install some packages and fetch some repositories.

1. Install build-essential gcc g++ m4 scons zlib1g zlib1g-dev libprotobuf-dev python-dev python autoconf automake autotools-dev curl python3 libmpc-dev libmpfr-dev libgmp-dev gawk bison flex texinfo gperf libtool patchutils bc libexpat-dev python-pip python3-pip
2. pip3 install regex colorlog
3. pip install numpy matplotlib scipy
4. git clone -b rvv-intrinsic https://github.com/riscv/riscv-gnu-toolchain
5. cd riscv-gnu-toolchain && ./configure --prefix=<local_install_path> --with-arch=rv64gv_zfh
6. make -j16 linux
7. export RV_CC10=<local_install_path>/bin/riscv64-unknown-linux-gnu-gcc or edit path directly in top/programs-spad/common/common.mk

Building:

Regardless of the route you choose, the first thing you'll need to do is build the simulator with this command:

scons -j16 ./build/RVSP/gem5.opt

Run this command from top, i.e., the repository root. (The Docker container should put you in this directory automatically.)

The build script may warn you about "missing the gem5 style or commit message hook." Just press enter to ignore this.

Building the simulator takes around 10 minutes.

Running Quickstart:

The benchmarks used in our system's evaluation are in the top/programs-spad directory. In each subdirectory there, you can type make run to execute an individual benchmark.

To collect the data for an entire batch of experiments, there is a collection of scripts available in top/scripts-phil/eval. We provide a top-level script to automate simulation, data collection, and plotting:

1. cd top/scripts-phil/eval
2. python artifact.py --experiment=[small,medium,large]

The artifact.py script invokes these other scripts:

1. run_sim.py: runs an experiment
2. extract_stats.py: extracts gem5 simulation data
3. organizer.py: plots graph of data

Each experiment size produces a part or all of the key data presented in the original paper. The time will vary depending on the number of cores available to parallelize the simulations. Each simulation takes 2-30 hours depending on the benchmark and configuration.

These are the available experiment sizes:

1. small: 10 simulations (5 benchmarks, 2 configs) -- recommended on 4-core systems.
2. medium: 30 simulations (15 benchmarks, 2 configs) -- recommended on 16-core systems.
3. large: 65 simulations (15 benchmarks, 4-5 configs) -- recommended on 32-core or more systems.

Plots will be generated in the same directory (top/scripts-phil/eval):

1. artifact_speedup.png: compares the execution time between configs.
2. artifact_icache.png: compares the icache accesses between configs.
3. artifact_energy.png: compares the energy consumption between configs.

Experimenting:

Aside from this recommended batch of experiments, you can also design and run your own custom experiments. Use the run_sim.py script and supply it with a different JSON file describing the data you want to collect:

python run_sim.py --sim-list=./experiments/test.json --results=./example

The ./experiments/full.json file gives an example of every experiment that can be run.

Then, you can extract and plot the data from these experiments using:

python extract_stats.py --cpu-sims=./example

If you just wish to plot previously extracted data (a pickle file is produced from the previous step), use:

python organizer.py

Benchmarks:

The benchmark suite for this evaluation is Polybench/GPU. The code in this repository consists of ports from the original C source code to use our architecture.

Key Source:

Some important source code directories to check out include:

• top/src/custom/ contains source for simulating vector groups
• top/src/mem/ruby/scratchpad/Scratchpad.cc contains source for "frames," which are the architecture's mechanism for decoupled access/execute (DAE) within vector groups

Compiler Pass:

Code for the architecture relies on a custom compiler pass found in top/trillium, which post-processes the assembly produced by GCC to produce runnable code.

Publication:

You can find more information about Rockcress in our MICRO 2021 paper.

Philip Bedoukian, Neil Adit, Edwin Peguero, and Adrian Sampson. 2021.
Software-Defined Vector Processing on Manycore Fabrics. In MICRO-54: 54th
Annual IEEE/ACM International Symposium on Microarchitecture (MICRO
’21), October 18–22, 2021, Virtual Event, Greece. ACM, New York, NY, USA,
15 pages. https://doi.org/10.1145/3466752.3480099

Acknowledgements

Thank you to Khalid Al-Hawaj and Tuan Ta for their contributions to the baseline models.