Manish Gupta
@@ -1,8 +1,8 @@
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# CUTLASS 2.5
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# CUTLASS 2.6
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_CUTLASS 2.5 - February 2021_
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_CUTLASS 2.6 - July 2021_
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CUTLASS is a collection of CUDA C++ template abstractions for implementing
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high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
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@@ -34,12 +34,24 @@ See the [Quick Start Guide](/media/docs/quickstart.md) to get started quickly.
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See the [functionality listing](/media/docs/functionality.md) for the list of operations
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supported at each level of the execution model hierarchy.
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# What's New in CUTLASS 2.6
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CUTLASS 2.6 is a minor update to CUTLASS adding:
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- Fused [broadcast](test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu) and [reductions](/test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu) in the epilogues of GEMM and Convolution
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- [Quaternion-valued GEMM](/examples/21_quaternion_gemm/quaternion_gemm.cu) and [Convolution](/examples/22_quaternion_conv/quaternion_conv.cu) in single-precision
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- [New strided Dgrad](test/unit/gemm/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation offers up to 4x performance improvements over previous strided Dgrad
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- 64-bit strides for large tensor allocations
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- [General affine layouts](/examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) fp64 tensor core and simt GEMM
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- Enhanced functionality, boosted performance, and bug fixes in the epilogue.
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- Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
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- Adopt new L2 prefetch feature in [ptx instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#ptx-isa-version-7-4).
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- Numerous updates from the community (thanks!)
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- See the [CHANGELOG](CHANGELOG.md) for more details
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# What's New in CUTLASS 2.5
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CUTLASS 2.5 is a minor update to CUTLASS adding:
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- [Tensor reductions](/test/unit/reduction/device/tensor_reduce_contiguous.cu)
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- [Optimizations for 3-D convolution](include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h)
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- [Fused Convolution+Convolution example](/examples/13_two_tensor_op_fusion/README.md)
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- See the [CHANGELOG](CHANGELOG.md) for more details
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# What's New in CUTLASS 2.4
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CUTLASS 2.4 is a significant update to CUTLASS adding:
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@@ -52,7 +64,7 @@ CUTLASS 2.4 is a significant update to CUTLASS adding:
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CUTLASS 2.3 is a minor update to CUTLASS adding:
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- GEMMs targeting structured [Sparse Tensor Cores](test/unit/gemm/device/gemm_f16n_f16n_f32t_tensor_op_f32_sparse_sm80.cu) in NVIDIA Ampere Architecture GPUs
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- Fast SGEMM kernels targeting GeForce RTX 30-series CUDA Cores
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- Intended to be compiled with [CUDA 11.1 Toolkit](https://developer.nvidia.com/cuda-toolkit)
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- Intended to be compiled with [CUDA 11.1 Toolkit](https://developer.nvidia.com/cuda-toolkit) or later
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# What's New in CUTLASS 2.2
|
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@@ -62,7 +74,7 @@ CUTLASS 2.2 is a significant update to CUTLASS adding:
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- Tensor Core-accelerated GEMMs targeting Tensor Float 32, BFloat16, and double-precision data types
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- Deep software pipelines using asynchronous copy
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- Described in [GTC 2020 Webinar (SR 21745)](https://developer.nvidia.com/gtc/2020/video/s21745)
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- Intended to be compiled with [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit)
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- Intended to be compiled with [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit) or later
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# What's New in CUTLASS 2.1
|
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|
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@@ -95,8 +107,8 @@ using CUDA 11.0 Toolkit. Tensor Core operations are implemented using CUDA's
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# Compatibility
|
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CUTLASS requires a C++11 host compiler and
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performs best when built with the [CUDA 11.1 Toolkit](https://developer.nvidia.com/cuda-toolkit).
|
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It is compatible with CUDA 9.2, CUDA 10.0, CUDA 10.1, CUDA 10.2, and CUDA 11.0.
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performs best when built with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit).
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It is also compatible with CUDA 10.2, CUDA 11.0, CUDA 11.1, CUDA 11.2, and CUDA 11.3.
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We have tested the following environments.
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@@ -106,12 +118,16 @@ We have tested the following environments.
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| | Microsoft Visual Studio 2017|
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| Ubuntu 16.04 | GCC 5.4.0 |
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| Ubuntu 18.04 | GCC 7.5.0 |
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| Ubuntu 20.04 | GCC 10.2.0 |
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Additionally, CUTLASS may be built with clang.
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See [these instructions](media/docs/quickstart.md#clang) for more details.
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|
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CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on
|
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any Maxwell-, Pascal-, Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU.
|
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any Maxwell-, Pascal-, Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU.
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|
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For all GPUs, we recommend compiling with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
|
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for best performance.
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|
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|**GPU**|**CUDA Compute Capability**|**Minimum CUDA Toolkit**|**CUDA Toolkit Enabling Native Tensor Cores**|
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|---|---|---|---|
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@@ -511,6 +527,7 @@ CUTLASS is released by NVIDIA Corporation as Open Source software under the
|
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|
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The official list of CUTLASS developers and contributors is available here: [CONTRIBUTORS](CONTRIBUTORS.md).
|
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|
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|
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# Copyright
|
||||
|
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Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
|
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|
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