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Changelog

CUTLASS 4.x

4.2.0 (2025-08-21)

CuTe DSL

  • We will likely be skipping 4.2.dev release and directly target 4.2.
  • CuTeDSL version remains at 4.1.0 till then.

CUTLASS C++

  • Add K major scale factor support for Hopper SM90 blockwise kernels.
  • Further enhance Blackwell SM100 Attention kernels in example 77.
    • Add fused reduction kernel support for cutlass MLA.
    • Fix an issue where get_unmasked_trip_count may return a negative value.
    • Fix an issue where mbarriers are initialized with a zero arrival count.
  • Add Blackwell SM120 blockwise gemm kernel example: example 87.
  • Support for Blackwell SM100 cpasync kernel.
  • Support for Blackwell SM121 kernels for DGX Spark GPUs.
    • Share the major codes with Blackwell SM120 kernels.
  • Support for Blackwell SM100 legacy mixed input GEMM kernels.
  • Support for Blackwell SM100 fp4 gemv kernels.
  • From CUDA 13.0, the Blackwell SM101 for Thor GPUs is renamed to SM110.
    • For CUDA toolkit version < 13.0, SM101 is still used for Thor GPUs.
    • For CUDA toolkit version >= 13.0, SM110 is used for Thor GPUs and SM101 is no longer valid.
  • CuTe changes:
    • Fix inaccurate GridDim calculation under CuTe tutorial.
    • Add movmatrix support.
    • Fix smallest MMA-N allowed for Blackwell fp8 and fp16 gemm kernels.
    • Support fp16 accmulator for sm89 fp8 mma.
    • Shorten nullspace implementation.
    • Isolate and comment on cosize hacks.
    • Important documentation correction: E<0,1> == 1@0@1.
  • Add support for heuristics-based kernel filtering and autotuning using nvidia-matmul-heuristics.
  • Rename legacy Python API package from cutlass to cutlass_cppgen.
  • Fix some profiler issues:
    • Modify default cluster callback values to none 0 to avoid profiler failure when these values are not set in command line.
    • Fix some no output and timeout issues.
  • Add following unit tests:
  • Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
  • Optimal code generation with CUDA toolkit versions 13.0.

4.1.0 (2025-07-16)

CuTe DSL

CUTLASS C++

  • Further enhance Blackwell SM100 Attention kernels in example 77.
    • Add variable sequence length support for FMHA Backward kernel.
    • Add varlen test support to Backward runner.
    • Codes support empty batch sequences.
  • Replace subbyte_iterator with cute::recast_ptr when constructing logical iterators/arrays.
  • CuTe changes:
    • Rewrite ArithTuple and ScaledBasis for robustness and clarity.
    • Remove buggy and kludgy get_layoutA|B|C_MN and friends from Atoms/TiledX.
    • Factor out print_latex and friends and rewrite.
    • Factor out print_svg and friends and rewrite.
  • Support Blackwell SM100 SIMT packed fp32x2 kernels.
  • Support residual add for implicit gemm kernels.
  • Various fixes for CUTLASS C++ Python interface's EVT tracer:
    • Add verifier for sm90 to report the invalid input.
    • When adding an edge to the graph, if the edge already exists, add an identity compute node to avoid having multiple parallel edges.
    • Register operations of tanh, sigmoid, exp, gelu to the python ast frontend.
    • Replace the NotImplemented Error by packing all nodes into a single topological visitor node as a fallback.
  • Fix profiler bugs in exhaustive perf search.
    • Fix incorrect cluster shape output issue when doing exhaustive search.
    • Fix a bug in profiler grouped GEMM for setting tile scheduler swizzles, cluster shapes, and raster orders.
  • Fix some profiler issues.
    • Complete the reference for Blackwell blockwise gemm kernels.
    • Fix incorrect regex logic for L1 test.
  • Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
  • Optimal code generation with CUDA toolkit versions 12.9.

4.0.0 (2025-06-03)

CuTe DSL

CUTLASS C++

  • Support Family Specific Architecture Features which was introduced in CUDA 12.9
    • 100f, 101f, 120f were added to support Family Specific Architecture Features which allows running the same binary on different chips belonging to the same Family (e.g. sm100) without recompiling. Note 101a is supported since CUTLASS 3.9
  • Instruction shapes and redundant accumulation type have been removed from CUTLASS 3.x-style library kernel names to disambiguate kernels and shorten names.
    • For example:
      • (old) cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma
      • (new) cutlass3x_sm90_tensorop_gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma
    • If you are using the CUTLASS library kernel names directly (e.g. to compile a subset of the CUTLASS library with -DCUTLASS_LIBRARY_KERNELS, filter kernels in the CUTLASS profiler with --kernels), please update your uses accordingly, this is a breaking change.
  • Further improved Blockwise and Groupwise GEMMs on Hopper and Blackwell.
    • Added non-power-of-two tile sizes.
    • Improved performance for K-major scale factors.
    • The argument mma_promotion_interval has been removed from non-grouped GEMM to align with the grouped and Blackwell SM100 versions.
  • Enhance Blackwell SM100 Attention kernels in example 77.
    • Support LSE output in FMHA Forward kernel.
    • Enhance performance measurement: support of different warmup iterations; buffer rotation to keep L2 cold; separate testing of persistent and non-persistent.
    • Enhance testing of variable sequence length.
    • Disable B2B mode in MLA to simplify the sample.
    • Clarify that fmha_gen sample only supports head dim 128.
    • Fixes for split-kv output in MLA.
  • Improve Blackwell and Hopper grouped GEMM performance, functionality, and profiler support.
    • Enable runtime datatype for Blackwell SM100 grouped GEMM. Profiler support is also added.
    • Enable kernel parameter exploration for Blackwell SM100 grouped GEMM - raster_order, swizzle.
  • Add Blackwell SM100 implicit GEMM conv fprop/dgrad/wgrad unit tests.
  • Add dynamic and preferred cluster support for convolution Blackwell SM100 kernels.
  • Fix profiler issues which cause no output or not supported error for some kernels.
  • Optimizations for Blackwell SM100 and SM120 block scaled kernels.
  • Support for Blackwell SM120 blockwise dense gemm in CUTLASS library and profiler.
  • New Hopper SM90 FMHA example, similar in design to the existing Blackwell FMHA.
  • CuTe changes:
    • Rework cute::copy_if so that the predicate tensor is also a true CuTe Tensor rather than a lambda and introduces transform-tensors to avoid any extra register or load/store overhead in using bool-tensors.
    • New CuTe tutorial to show the usage of copy_if in tile copy.
    • Add CuTe C++ reduce op.
      • Add several unit tests for CuTe tensor algorithms.
  • Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
  • Optimal code generation with CUDA toolkit versions 12.9.

CUTLASS 3.x

3.9.2 (2025-05-03)

  • Fixed Blockwise and Groupwise GEMM hang issue when problem size K is 128.
  • Optimal code generation with CUDA toolkit versions 12.9.

3.9.1 (2025-04-30)

  • Fixed Group Gemm hang issue in CUTLASS 3.x
  • Improved Hopper Blockwise and Groupwise GEMM performance.

3.9.0 (2025-04-24)

3.8.0 (2025-01-25)

3.7.0 (2025-01-11)

  • Hopper blockwise scaling FP8 GEMM uses 2D scaling tensor, assigning one value per threadblock. This allows a finer-grained scaling to be applied for each output tile per gemm-k iteration. The operands and scaling tensors are loaded from global memory to shared memory using TMA and cp_async, respectively. The scaling is applied inside the mainloop. Details with figures are here.
  • Distributed GEMM is a new (experimental) API which can turn existing CUTLASS GEMM kernels into pipelined Tensor Parallel GEMMs that run efficiently on NVLink-based network of GPUs. Its pipelining schedules can hide most of the communication behind computation, and relies on point-to-point communication, which can simply use CUDA runtime's peer device access feature. It also utilizes remote TMA loads and memcopies with CUDA graphs to handle communication primarily through the Copy Engine, leaving all SMs free for Hopper's persistent kernels. For more details you can refer to the DistGEMM blog post.
  • Improved persistent grid launch for Hopper kernels with large cluster sizes (>= size of 4) using the new make_kernel_hardware_info API as shown in example 48.
  • Enabled high precision accumulation for Hopper FP8 Sparse GEMM.
  • Potential API breaking changes:
    • Fix cute::UniversalCopy for type safety.
    • No longer implicitly select cute::SM80_CP_ASYNC_* based on input tensors. This avoids implicit downstream synchronization requirements. To use SM80_CP_ASYNC, users must explicitly select the appropriate CopyAtom.
    • Fix cute::SM80_CP_ASYNC_CACHEALWAYS, cute::SM80_CP_ASYNC_CACHEGLOBAL, cute::SM80_CP_ASYNC_CACHEALWAYS_ZFILL, cute::SM80_CP_ASYNC_CACHEGLOBAL_ZFILL to avoid implicitly selecting ZFILL behavior on predication.
    • Remove cute::copy_vec<T> in favor of cute::copy_aligned and cute::copy(AutoVectorizingCopyWithAssumedAlignment<NumBits>,...).
    • A refactor of default epilogue struct DefaultEpilogue API to avoid reading non-void ElementC value for ElementC = void kernel.
  • New CUTLASS profiler flags: profiling-duration, min-iterations, and kernels-file documented in profiler.md.
  • Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
  • Optimal code generation with CUDA toolkit versions 12.6.

3.6.0 (2024-10-03)

3.5.1 (2024-07-25)

3.5.0 (2024-04-09)

3.4.1 (2024-02-14)

3.4.0 (2024-01-12)

3.3 (2023-10-31)

  • Mixed-input Hopper GEMMs support covering 16-bit x 8-bit input operand types.
  • Mixed-input Ampere GEMMs with support for canonical layouts (TN). The implementation supports upcast on operandB {fp16, bf16} x {s8, u8}, and upcast on operandA {s8, u8} x {fp16, bf16}.
  • Copy Async based Hopper GEMMs - which support lower than 16B aligned input tensors.
  • Kernel schedules and Builder support for mixed precision and Copy Async GEMMs with < 16B aligned input tensors.
  • Profiler support for lower-aligned Hopper GEMMs.
  • Performance Improvements to Scatter-Gather Hopper Example.
  • Sub-Byte type fixes and improvements.
  • EVT Support for RELU with Aux bitmap tensor store (used in dRELU). See SM90 EVT fusions for details.
  • Fusion support for backprop fusions including drelu, dgelu, and dbias.
  • Support for void-C kernels and SM80 mixed-input GEMMs in the CUTLASS Python interface

3.2.2 (2023-10-25)

  • Minor patch for issue/1138

3.2.1 (2023-09-22)

  • Python support SM90 Epilogue Visitor Tree (EVT) on top of the C++ support released in 3.2.0.
  • SM80 EVT support in C++ and Python.
  • Other SM90 epilogue improvements.
  • Splitting CUTLASS library into smaller units based on operation, arch and datatypes. See 1105 for details.
  • Making tools/library/scripts packageable - tools/library/scripts is now moving to python/cutlass_library. See the Python README for details.
  • SM90 TF32 kernel improvements for all layouts.
  • SM90 rasterization direction support in the CUTLASS profiler.
  • Improvement for CUTLASS profiler build times.
  • Remove Python-C++ bindings.

3.2.0 (2023-08-03)

  • New warp-specialized persistent FP8 GEMM kernel kernel schedules and mainloops targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters. An example showcasing Hopper warp-specialized FP8 GEMMs. FP8 GEMMs come with a fast accumulation mode. When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results will not periodically be promoted to a higher precision.
  • New Epilogue Visitor Tree (EVT) support for Hopper TMA epilogues. EVTs allows for user-defined customized epilogue fusion patterns without having to write a new epilogue.
  • Stream-K feature for Hopper. Note that this is only a functional implementation of stream-K, and should not be used for performance comparison. Optimizations are expected in a future release.
  • Improved CTA rasterization and support for CTA swizzling for Hopper kernels using the Tile Scheduler.
  • Improved performance for warp-specialized TensorFloat-32 (TF32) GEMM kernels targeting Hopper TMA.
  • Hopper GEMM+Permute, an example of fusing tensor reordering (permutation) with GEMM mainloop or epilogue.
  • New CUTLASS 2D Convolution Python interface. New example here.
  • Support for Windows (MSVC) builds. Tested with Visual Studio 2019 v16.11.27 on Windows 10.0.
  • Optimal performance using CUDA 12.2u1
  • Updates and bugfixes from the community (thanks!)

3.1.0 (2023-04-14)

  • New CUTLASS Python interface that aims to provide an ease-of-use interface for instantiating, emitting, compiling, and running CUTLASS kernels via Python. More details here and new examples.
  • New efficient epilogues using TMA for Hopper.
  • Support for fused epilogues, such Bias, ReLU and GELU, using the new efficient epilogues.
  • New warp-specialized TensorFloat-32 (TF32) GEMM kernels targeting Hopper TMA.
  • New warp-specialized persistent cooperative kernel design that allows for larger tile sizes and improves performance on Hopper.
  • An example showcasing GEMM-Like Tensor-Tensor Contraction (GETT) capability on Hopper.
  • Epilogue builders. Similar to mainloop builders (see example 49), epilogue builders aim to generate the best-possible epilogue while exposing incremental opt-ins for greater customization.
  • Profiler support for overriding kernel and epilogue builder auto schedules for 3.x API kernels, allowing specific policies to be run in the CUTLASS profiler.
  • Performance optimizations for the warp-specialized persistent ping-pong kernel.
  • Changes to the GEMM API 3.x, involving the host-facing arguments and the underlying Params structs.
  • FMHA Backward Pass from Meta xFormers.
  • Streamk GEMM with Broadcast enables epilogue broadcast with StreamK GEMM.
  • Batched B2B GEMM now can run multiple Back-to-Back GEMM with the same problem size in parallel.
  • Batched Strided GEMV support both row major and column major input matrix.
  • Permute + GEMM fusion can fuse Permute with following GEMM now. Before, we only support fusing GEMM with Permute in the epilogue.
  • Row Broadcast can be fused in the epilogue.
  • The GitHub branch is renamed from master to main in this release.
  • Optimal performance using CUDA 12.1
  • Updates and bugfixes from the community (thanks!)

3.0.0 (2023-01-23)

CUTLASS 2.x

2.11.0 (2022-11-19)

  • Stream-K, which is a new general way to do split-K. It can not only improve performance, but can also significantly reduce the number of tile sizes that need to be profiled to find the best one.

  • Fused multi-head attention Kernel. It has two variants: one uses batched GEMM for the fixed sequence length, and the other one uses group GEMM for the variable sequence length. Both versions just need one kernel.

  • Dual GEMM, which can fuse A x B and A x C into one kernel. Two GEMMs has no producer-consumer dependency.

  • Hopper improves double precision matrix multiplication by 2x compared to Ampere at iso-clocks. It is supported since CUDA 11.8.

  • BLAS3 functions with Hoppers new double precision matrix multiplication instructions.

  • ELL Block Sparse GEMM, which uses an ELL matrix to describe the sparsity of A matrix. B and output matrices are still dense. The block size can be arbitary.

  • Optimized Group Conv for SingleGroup mode, which requires that the output channel per group is a multiple of Threadblock tile N.

  • Optimized DepthWise Conv. Two new modes are added

    • kOptimized - use direct conv to compute instead of implicit GEMM.
      • The restrictions are: 1) input ,output channel and group number should be multiple of (128 / sizeof(input element)). 2) The input filter size should be the same as the template parameter configuration.
    • kFixedStrideDilation - which puts stride and dilation into templates to further improve the performance. In this mode, kernel persistents some inputs into register to squeeze more performance, so large filter/stride/dilation is not recommanded.
      • The restrictions are: 1) input, output channel and group number should be multiple of (128 / sizeof(input element)). 2) input filter size, stride, dilation should same as the template parameter configuration.

  • Scripts to fuse multiple back-to-back GEMM. Its implementation was discussed in a GTC'22 Spring talk.

  • FP8 data type definition and conversion routines.

  • Updates and bugfixes from the community (thanks!). Big shout out to Meta's xFormers.

  • Deprecation announcement: CUTLASS plans to deprecate the following:

    • Maxwell and Pascal GPU architectures
    • Ubuntu 16.04
    • CUDA 10.2

2.10.0 (2022-08-23)

  • CUTLASS Python now supports GEMM, CONV, Group GEMM for different data types as well as different epilogue flavours.
  • Optimizations for CUTLASS's Grouped GEMM kernel. Threadblock scheduling part is improved. Some computation can be moved to the host side if applicable. Grouped Syr2k kernels are added, too.
  • Optimizations for GEMM+Softmax. All the reduction computation is fused into the previous GEMM. More template arguments are provided to fine tune the performance.
  • Grouped GEMM for Multihead Attention. This general group gemm based MHA does not require the sequence length of all GEMMs to be the same which makes it most useful for natural language processing.
  • GEMM + Layer norm fusion for Ampere splits the layernorm into two parts and both of them can be fused into the GEMMs before and after separately. In addition to use square sum to compute variance of layernorm, Shift-K is provided if square sum raise numerical issues.
  • GEMM Epilogue Permutation Fusion can apply user provided permutation layout mapping in the GEMM epilogue.
  • Grouped convolution targeting implicit GEMM introduces the first group convolution implementation to CUTLASS. It is an Analytical implementation, not an Optimized. The restrictions are: 1) input and output channel number should be multiple of group number. 2) split-K is not supported. The implementation has 2 modes:
    • kSingleGroup: output channel per group is multiple of Threadblock tile N.
    • kMultipleGroup: Threadblock tile N is multiple of output channel per group.
  • Depthwise separable convolution introduces the first depthwise convolution which is also Analytical for now. The restrictions are: 1) SIMT only 2) No split-K 3) input channel equals to output channel equals to group number.
  • Standalone Layernorm and Pooling kernels.
  • Back-to-back GEMM/CONV relaxes the requirement that the first GEMM K dimension needs to be the multiple of Threadblock Tile K dimension.
  • Optimal performance using CUDA 11.6u2
  • Updates and bugfixes from the community (thanks!)

2.9.0 (2022-04-21)

  • First layer Convolution kernels specialized for small channel counts and reduced alignment
  • BLAS3 operators accelerated by Tensor Cores
  • CUTLASS Python demonstrating JIT compilation of CUTLASS kernels and a Python-based runtime using CUDA Python
  • GEMM + Softmax example
  • Gather and Scatter Fusion with GEMM can gather inputs and scatters outputs based on indices vectors in the same GEMM kernel.
    • It can select random rows in a row major matrix.
    • It can select random columns in a column major matrix.
  • Back-to-back GEMM/CONV fully supports buffering the first GEMM/CONV results in the shared memory for the latter one to use. It can eliminate register spill when the tile size is big. Additionally, bias vector add is supported in the first GEMM/CONV.
    • Supported kernels: GEMM and CONV.
    • Supported types: fp16 and int8.
    • Supported architectures: Turing and Ampere.
  • Transposed Convolution (a.k.a Deconvolution) support which reuses Dgrad implementation.
  • Utility functions that can pad NHWC and convert between NCHW and NHWC.
  • Small alignment implicit gemm support for Fprop/Dgrad/Wgrad so that padding is no longer mandated to use tensor cores in these kernels.
  • Epilogue enhancement:
    • Eliminate bank conflicts in int8 tensor core kernels.
    • Half2 usage if epilogue compute type is fp16.
    • More activation functions: Silu, Hardswish, Leaky Relu.
    • New elementwise fusion pattern for residual block.
  • Group GEMM thread block number calculation fix which helps to launch the intended number of threadblocks to fully occupy the GPUs.
  • Parallel GEMM splitk support in the CUTLASS profiler.
  • Optimal performance using CUDA 11.6u2
  • Updates and bugfixes from the community (thanks!)

2.8.0 (2021-11-19)

2.7.0 (2021-09-24)

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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