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CUTLASS 2.9 (#468)
2022-04-24 03:02:38 +08:00
Update license year (#1306)
2024-01-17 03:37:22 +08:00
# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
CUTLASS 2.9 (#468)
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# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
Checkpointing CUTLASS 1.1 release.
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#
CUTLASS 2.9 (#468)
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# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
Checkpointing CUTLASS 1.1 release.
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#
CUTLASS 2.9 (#468)
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# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
Checkpointing CUTLASS 1.1 release.
2018-09-19 07:58:03 +08:00
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
CUTLASS 2.9 (#468)
2022-04-24 03:02:38 +08:00
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
set(CUTLASS_EXAMPLES_COMMON_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/common)
Cutlass 1.3 Release (#42) CUTLASS 1.3 Release - Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
2019-03-21 01:49:17 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
add_custom_target(cutlass_examples)
add_custom_target(test_examples)
CUTLASS 2.1 (#83) CUTLASS 2.1 contributes: - BLAS-style host-side API added to CUTLASS Library - Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores - Minor enhancements and bug fixes
2020-04-08 04:51:25 +08:00
function(cutlass_example_add_executable NAME)
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
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set(options)
CUTLASS 2.6 (#298) CUTLASS 2.6
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set(oneValueArgs DISABLE_TESTS)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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set(multiValueArgs DEPENDS DEPENDEES TEST_COMMAND_OPTIONS)
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
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cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
CUTLASS 2.6 (#298) CUTLASS 2.6
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if (NOT DEFINED __DISABLE_TESTS)
set(__DISABLE_TESTS OFF)
endif()
CUTLASS 3.4.0 (#1286) * CUTLASS 3.4.0 * Update CHANGELOG.md --------- Co-authored-by: Pradeep Ramani <prramani@nvidia.com>
2023-12-30 04:21:31 +08:00
cutlass_add_executable(${NAME} ${__UNPARSED_ARGUMENTS} BATCH_SOURCES OFF)
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
add_dependencies(cutlass_examples ${NAME})
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
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target_link_libraries(
${NAME}
PRIVATE
CUTLASS
cutlass_tools_util_includes
Updates to fused epilogue (#383) * Enhancements and fixes to fused GEMM and Convolution epilogue. * Need to explicitly list cudart as unit test library dependency.
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$<$<BOOL:${CUTLASS_ENABLE_CUBLAS}>:nvidia::cublas>
CUTLASS 3.0.0 (#786) * CUTLASS 3.0.0
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cuda
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
)
target_include_directories(
${NAME}
PRIVATE
${CUTLASS_EXAMPLES_COMMON_SOURCE_DIR}
CUTLASS 3.0.0 (#786) * CUTLASS 3.0.0
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${CUTLASS_EXAMPLES_UTILS_DIR}
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
install(
TARGETS ${NAME}
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>. (#100) - Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>. - Enhancement to CUTLASS Utility Library's HostTensorPlanarComplex template to support copy-in and copy-out - Added test_examples target to build and test all CUTLASS examples - Minor edits to documentation to point to GTC 2020 webinar
2020-06-16 01:47:01 +08:00
)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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cutlass_add_executable_tests(
test_examples_${NAME} ${NAME}
DEPENDS ${__DEPENDS}
DEPENDEES test_examples ${__DEPENDEES}
TEST_COMMAND_OPTIONS ${__TEST_COMMAND_OPTIONS}
DISABLE_EXECUTABLE_INSTALL_RULE
CUTLASS 2.6 (#298) CUTLASS 2.6
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DISABLE_TESTS ${__DISABLE_TESTS}
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
)
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
endfunction()
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
foreach(EXAMPLE
00_basic_gemm
01_cutlass_utilities
02_dump_reg_shmem
03_visualize_layout
04_tile_iterator
05_batched_gemm
06_splitK_gemm
07_volta_tensorop_gemm
CUTLASS 2.1 (#83) CUTLASS 2.1 contributes: - BLAS-style host-side API added to CUTLASS Library - Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores - Minor enhancements and bug fixes
2020-04-08 04:51:25 +08:00
08_turing_tensorop_gemm
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
09_turing_tensorop_conv2dfprop
CUTLASS 2.1 (#83) CUTLASS 2.1 contributes: - BLAS-style host-side API added to CUTLASS Library - Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores - Minor enhancements and bug fixes
2020-04-08 04:51:25 +08:00
10_planar_complex
11_planar_complex_array
CUTLASS 2.2 (#96) Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
2020-06-09 07:17:35 +08:00
12_gemm_bias_relu
Transposed conv2d and wgrad split k examples (#413) * add split k wgrad example * wgrad done * begin transposed conv2d example * update transposed conv2d example and add ref check * update doc for conv2d transpose example * add license * add wgrad doc * more clarification on GEMM output type * typo fix * clean up indent * address comments * rename example numbers to 34 and 35 * GEMM -> Implicit GEMM * Revert "rename example numbers to 34 and 35" This reverts commit 551a808c227216e9e38d4472ba8ff020557b8500. * transposed_conv2d is 34 * add compiler and device version check to exit gracefully Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
2022-03-24 02:52:54 +08:00
13_two_tensor_op_fusion
CUTLASS 2.3 initial commit (#134) CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
2020-09-24 05:00:58 +08:00
14_ampere_tf32_tensorop_gemm
15_ampere_sparse_tensorop_gemm
CUTLASS 2.5
2021-02-26 22:58:26 +08:00
16_ampere_tensorop_conv2dfprop
17_fprop_per_channel_bias
CUTLASS 2.6 (#298) CUTLASS 2.6
2021-07-23 12:40:53 +08:00
18_ampere_fp64_tensorop_affine2_gemm
19_tensorop_canonical
20_simt_canonical
21_quaternion_gemm
22_quaternion_conv
example 23 gemm operand reduction fusion (#325)
2021-09-21 04:34:47 +08:00
23_ampere_gemm_operand_reduction_fusion
CUTLASS 2.8 (#363) CUTLASS 2.8
2021-11-20 05:26:35 +08:00
24_gemm_grouped
25_ampere_fprop_mainloop_fusion
26_ampere_wgrad_mainloop_fusion
27_ampere_3xtf32_fast_accurate_tensorop_gemm
28_ampere_3xtf32_fast_accurate_tensorop_fprop
29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm
Transposed conv2d and wgrad split k examples (#413) * add split k wgrad example * wgrad done * begin transposed conv2d example * update transposed conv2d example and add ref check * update doc for conv2d transpose example * add license * add wgrad doc * more clarification on GEMM output type * typo fix * clean up indent * address comments * rename example numbers to 34 and 35 * GEMM -> Implicit GEMM * Revert "rename example numbers to 34 and 35" This reverts commit 551a808c227216e9e38d4472ba8ff020557b8500. * transposed_conv2d is 34 * add compiler and device version check to exit gracefully Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
2022-03-24 02:52:54 +08:00
30_wgrad_split_k
CUTLASS 2.9 (#468)
2022-04-24 03:02:38 +08:00
31_basic_syrk
32_basic_trmm
33_ampere_3xtf32_tensorop_symm
Transposed conv2d and wgrad split k examples (#413) * add split k wgrad example * wgrad done * begin transposed conv2d example * update transposed conv2d example and add ref check * update doc for conv2d transpose example * add license * add wgrad doc * more clarification on GEMM output type * typo fix * clean up indent * address comments * rename example numbers to 34 and 35 * GEMM -> Implicit GEMM * Revert "rename example numbers to 34 and 35" This reverts commit 551a808c227216e9e38d4472ba8ff020557b8500. * transposed_conv2d is 34 * add compiler and device version check to exit gracefully Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
2022-03-24 02:52:54 +08:00
34_transposed_conv2d
CUTLASS 2.9 (#468)
2022-04-24 03:02:38 +08:00
35_gemm_softmax
36_gather_scatter_fusion
CUTLASS 2.10 (#615) Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
2022-09-04 06:48:46 +08:00
37_gemm_layernorm_gemm_fusion
38_syr2k_grouped
CUTLASS 3.0.0 (#786) * CUTLASS 3.0.0
2023-01-24 09:55:28 +08:00
cute
CUTLASS 2.10 (#615) Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
2022-09-04 06:48:46 +08:00
39_gemm_permute
releaase 2.11 (#703)
2022-11-19 22:02:15 +08:00
41_fused_multi_head_attention
42_ampere_tensorop_group_conv
43_ell_block_sparse_gemm
45_dual_gemm
46_depthwise_simt_conv2dfprop
streamk example and performance tuning (#760) * streamk example and performance tuning * one missing file Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
2023-01-11 05:10:02 +08:00
47_ampere_gemm_universal_streamk
CUTLASS 3.0.0 (#786) * CUTLASS 3.0.0
2023-01-24 09:55:28 +08:00
48_hopper_warp_specialized_gemm
CUTLASS 3.1 (#915) Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
2023-04-15 11:19:34 +08:00
49_hopper_gemm_with_collective_builder
CUTLASS 3.0.0 (#786) * CUTLASS 3.0.0
2023-01-24 09:55:28 +08:00
50_hopper_gemm_with_epilogue_swizzle
CUTLASS 3.0 Hopper GEMMs are GETTs in disguise (#897)
2023-03-29 22:42:40 +08:00
51_hopper_gett
CUTLASS 3.2 (#1024) * CUTLASS 3.2
2023-08-08 08:50:32 +08:00
52_hopper_gather_scatter_fusion
53_hopper_gemm_permute
54_hopper_fp8_warp_specialized_gemm
CUTLASS 3.3.0 (#1167) * Release 3.3.0 Adds support for mixed precision GEMMs On Hopper and Ampere Adds support for < 16B aligned GEMMs on Hopper Enhancements to EVT Enhancements to Python interface Enhancements to Sub-byte type handling in CuTe Several other bug-fixes and performance improvements. * minor doc update
2023-11-02 23:09:05 +08:00
55_hopper_mixed_dtype_gemm
CUTLASS 3.4.0 (#1286) * CUTLASS 3.4.0 * Update CHANGELOG.md --------- Co-authored-by: Pradeep Ramani <prramani@nvidia.com>
2023-12-30 04:21:31 +08:00
56_hopper_ptr_array_batched_gemm
57_hopper_grouped_gemm
CUTLASS 3.5.0 (#1411)
2024-03-20 05:51:04 +08:00
58_ada_fp8_gemm
59_ampere_gather_scatter_conv
CUTLASS 3.6.0 (#1850) * v3.6 * update changelog * update readme * fix typo * fixing typos * hopper gemm with weight prefetch --------- Co-authored-by: yuzhai <yuzhai@nvidia.com> Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
2024-10-10 03:33:27 +08:00
61_hopper_gemm_with_topk_and_softmax
62_hopper_sparse_gemm
63_hopper_gemm_with_weight_prefetch
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
)
CUTLASS 2.0 (#62) CUTLASS 2.0 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: - Quick start guide - Documentation - Utilities - CUTLASS Profiler 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: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
add_subdirectory(${EXAMPLE})
endforeach()
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