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NVIDIA CUTLASS Changelog

CUTLASS 2.x

2.6.1 (2021-09-03)

  • Arbitrary padding and striding for CUTLASS Strided DGRAD Convolution operator (Analytic Iterators)
  • Tuning for GEMMs fused with partial reductions
  • Corrections and bug fixes reported by the CUTLASS community
    • Thank you for filing these issues!

2.6.0 (2021-07-22)

  • Optimal performance when compiled with the CUDA 11.4 Toolkit
  • Fused operators with GEMM and Convolution
  • 64b tensor strides and leading dimensions support for GEMMs
  • Affine rank=2 matrix layouts
  • Batched GEMV preview implementation
  • New strided Dgrad implementation
    • Accelerates over previous implementation by cutting down redundant math by 4x
    • Support using new Dy and w analytic iterators and existing cutlass::conv::device::ImplicitGemmConvolution interface
  • Quaternion-valued GEMM and Convolution in single- and double-precision (targeting CUDA Cores)
  • Many improvements to the epilogue.
    • Provide an option to not fully unroll the epilogue to reduce the code size and improve the performance when using complicated elementwise operations
    • Performance improvement for FP16 tensor core kernels
    • Bug fixes
  • Enhanced Clang support and the combination of Clang 13 and CUDA 11.4 can build and run kernels from Pascal and Ampere.
  • Updated minimum CUDA Toolkit requirement to 10.2
  • Corrections and bug fixes reported by the CUTLASS community
    • Thank you for filing these issues!

2.5.0 (2021-02-26)

  • Tensor reductions
    • m-to-n reductions of tensors with affine layout
    • Specializations for reductions including contiguous dimension
    • Specializations for reductions excluding contiguous dimension
    • Custom reduction functors such as cutlass::logical_and
    • Large tensor support, up to 2^63 elements (however, each dimension is limited to an extent of 2^31)
  • Optimizations for 3-D convolution
  • Fused Convolution+Convolution example
  • Corrections and bug fixes reported by the CUTLASS community
    • Thank you for filing these issues!

2.4.0 (2020-11-19)

  • Implicit GEMM convolution kernels supporting CUDA and Tensor Cores on NVIDIA GPUs
    • Operators: forward (Fprop), backward data gradient (Dgrad), and backward weight gradient (Wgrad) convolution
    • Data type: FP32, complex, Tensor Float 32 (TF32), BFloat16 (BF16), Float16, Int4, Int8, Int32
    • Spatial dimensions: 1-D, 2-D, and 3-D
    • Layout: NHWC, NCxHWx
  • Implicit GEMM convolution components:
    • Global memory iterators supporting Fprop, Dgrad, and Wgrad
    • MmaMultistage for implicit GEMM convolution for NVIDIA Ampere architecture
    • MmaPipeline for implicit GEMM convolution for NVIDIA Volta and Turing architectures
    • Documentation describing Implicit GEMM Convolution algorithm and implementation

2.3.0 (2020-09-23)

2.2.0 (2020-06-08)

  • NVIDIA Ampere Architecture features
    • Fast Tensor Core operations:
    • Maximum performance via mma.sync
    • Tensor Float 32, BFloat16, and double-precision data types
    • Mixed integer data types (int8, int4, bin1)
    • Asynchronous copy for deep software pipelines via cp.async
    • Described in GTC 2020 Webinar (SR 21745) (free registration required)
  • Features:
    • SDK examples showing GEMM fused with bias+relu and fused GEMM+GEMM
    • Complex-valued GEMMs targeting NVIDIA Ampere Tensor Cores in double-precision and Tensor Float 32
    • Gaussian complex GEMMs using 3m complex multiply algorithm
    • Universal GEMM kernel supporting two batch modes and two algorithms for parallel reductions
  • Policy updates:
    • CUDA 11 Toolkit needed to enable NVIDIA Ampere Architecture features
    • Disabled F16C by default for compatibility - enable on cmake command line with -DCUTLASS_ENABLE_F16C=ON

2.1.0 (2020-04-06)

  • BLAS-style host-side API added to CUTLASS Library
    • API to launch compiled kernel instances for GEMM and planar complex GEMM
  • Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores
  • Minor enhancements and bug fixes

2.0.0 (2019-11-19)

  • Substantially refactored for
    • Better performance, particularly for native Turing Tensor Cores
    • Robust and durable templates spanning the design space
    • Encapsulated functionality embodying modern C++11 programming techniques
    • Optimized containers and data types for efficient, generic, portable device code
  • Updates to:
  • Native Turing Tensor Cores
    • Efficient GEMM kernels targeting Turing Tensor Cores
    • Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands
  • Coverage of existing CUTLASS functionality
    • GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs
    • Volta Tensor Cores through native mma.sync and through WMMA API
    • Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions
    • Batched GEMM operations
    • Complex-valued GEMMs
  • Note: a host compiler supporting C++11 or greater is required.

CUTLASS 1.x

1.3.2 (2019-07-09)

  • Performance improvement for Volta Tensor Cores TN and TT layouts.

1.3.1 (2019-04-09)

  • Corrected NVRTC unit tests.

1.3.0 (2019-03-20)

  • Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.

1.2.0 (2018-10-26)

  • Parallelized reductions across threadblocks ("Split-K")
    • Improved IGEMM performance
  • Batched strided WMMA GEMMs

1.1.0 (2018-09-19)

  • Turing Features
    • WMMA GEMM targeting TensorCores - INT8, INT4, 1-bit
  • Batched Strided GEMM
  • Threadblock rasterization strategies
    • Improved performance for adverse problem sizes and data layouts
  • Extended CUTLASS Core comonents
    • Tensor views support arbitrary matrix and tensor layouts
    • Zip iterators for structuring multiple data streams
  • Enhanced CUTLASS utilities
    • Reference code for tensor operations in host and device code
    • Added HostMatrix<> for simplified matrix creation
  • Examples
    • Basic GEMM, tensor views, CUTLASS utilities, batched GEMM, WMMA GEMM

1.0.1 (2018-06-11)

  • Intra-threadblock reduction added for small threadblock tile sizes
    • sgemm_64x128x16, sgemm_128x128x16, sgemm_128x64x16, sgemm_128x32x16, sgemm_64x64x16, sgemm_64x32x16
    • igemm_32x32x128
  • GEMM K residue handled during prologue prior to mainloop
  • Replaced Google Test copy with submodule. Use git submodule init --recursive --update

1.0.0 (2018-05-16)

  • Substantial rewrite to accommodate new architecture
  • Kernels: SGEMM, DGEMM, IGEMM, HGEMM, WMMA GEMM
  • Unit and performance tests

0.0.1 (2017-12-04)

  • Initial release

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