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v3.9 update (#2213)

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Co-authored-by: yuzhai <yuzhai@nvidia.com>
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Yujia Zhai
2025-04-03 02:10:16 -04:00
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co-authored by yuzhai
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examples/83_blackwell_sparse_gemm/83_blackwell_sparse_gemm.cu
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/***************************************************************************************************
* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* 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.
*
* 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.
*
* 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
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief A FP16 sparse GEMM example for the NVIDIA Blackwell SM100 architecture using CUTLASS.
The Blackwell SM100 CUTLASS kernel uses of the following Blackwell SM100 features:
1. New series of Tensor Core MMA Instructions (tcgen05) introduced on the Blackwell architecture (sm100a)
which have 2x throughput compared to Hopper Tensor Core MMA instructions (WGMMA).
Note that Hopper WGMMA Tensor Core MMA instructions are not compatible on Blackwell (See https://docs.nvidia.com/cuda/parallel-thread-execution).
2. A new per-SM memory called Tensor Memory (TMEM) introduced on the Blackwell architecture (sm100a).
Blackwell SM100 Tensor Core MMA instructions store their accumulation results in TMEM instead of the
Register File. (Please refer to CUDA 12.8 docs on https://docs.nvidia.com/cuda/).
3. An extended flavor of the warp-specialized kernel design introduced in Hopper enabled by use of TMEM
which allows us to decouple the execution of MMA and epilogue into separate warps.
4. A new SW controlled dynamic scheduler based on cluster launch control (See https://docs.nvidia.com/cuda/parallel-thread-execution).
Usage:
$ ./examples/83_blackwell_sparse_gemm/83_blackwell_sparse_gemm --m=8192 --n=8192 --k=8192
*/
#include <iostream>
#include "cutlass/cutlass.h"
#include "cute/tensor.hpp"
#include "cutlass/tensor_ref.h"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/gemm/kernel/tile_scheduler_params.h"
#include "cutlass/util/command_line.h"
#include "cutlass/util/distribution.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/packed_stride.hpp"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/reference/device/gemm.h"
#include "cutlass/util/reference/device/tensor_compare.h"
#include "cutlass/util/reference/device/tensor_fill.h"
#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp"
#include "cutlass/transform/device/transform_universal_adapter.hpp"
#include "helper.h"
using namespace cute;
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM kernel configurations
/////////////////////////////////////////////////////////////////////////////////////////////////
// A matrix configuration
using ElementA = half_t; // Element type for A matrix operand
using LayoutTagA = cutlass::layout::RowMajor; // Layout type for A matrix operand
constexpr int AlignmentA = 2 * 128 / cutlass::sizeof_bits<ElementA>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes), 2x for compress along k
// E matrix config
using ElementE = cute::uint8_t;
// B matrix configuration
using ElementB = half_t; // Element type for B matrix operand
using LayoutTagB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
// C/D matrix configuration
using ElementD = float; // Element type for D matrix operand
using ElementC = float; // Element type for C matrix operand
using LayoutTagC = cutlass::layout::ColumnMajor; // Layout type for C matrix operand
using LayoutTagD = cutlass::layout::ColumnMajor; // Layout type for D matrix operand
constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value; // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
// Kernel functional config
using ElementAccumulator = float; // Element type for internal accumulation
using ArchTag = cutlass::arch::Sm100; // Tag indicating the minimum SM that supports the intended feature
using OperatorClass = cutlass::arch::OpClassSparseTensorOp; // Operator class tag
// MMA and Cluster Tile Shapes
// Shape of the tile computed by tcgen05 MMA, could be across 2 SMs if Cluster Shape %2 == 0
using MmaTileShape_MNK = Shape<_256,_128,_64>;
// Shape of the threadblocks in a cluster
using ClusterShape_MNK = Shape<_2,_1,_1>;
// Build the epilogue
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
ArchTag, OperatorClass,
MmaTileShape_MNK, ClusterShape_MNK,
cutlass::epilogue::collective::EpilogueTileAuto,
ElementAccumulator, ElementAccumulator,
ElementC, LayoutTagC, AlignmentC,
ElementD, LayoutTagD, AlignmentD,
cutlass::epilogue::TmaWarpSpecialized2Sm
>::CollectiveOp;
// Build the mainloop
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
ArchTag, OperatorClass,
ElementA, LayoutTagA, AlignmentA,
ElementB, LayoutTagB, AlignmentB,
ElementAccumulator,
MmaTileShape_MNK, ClusterShape_MNK,
cutlass::gemm::collective::StageCountAutoCarveoutEpi<CollectiveEpilogue>,
cutlass::gemm::KernelSparseTmaWarpSpecialized2SmSm100
>::CollectiveOp;
using ProblemShape = Shape<int,int,int,int>;
// Compose into a kernel
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
ProblemShape,
CollectiveMainloop,
CollectiveEpilogue,
void>; // Default to ClusterLaunchControl (CLC) based tile scheduler
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
// Reference device GEMM implementation type
using DeviceGemmReference = cutlass::reference::device::Gemm<
ElementA,
LayoutTagA,
ElementB,
LayoutTagB,
ElementC,
LayoutTagC,
ElementAccumulator,
ElementAccumulator>;
// Layouts
using LayoutA = typename Gemm::GemmKernel::CollectiveMainloop::LayoutA;
using LayoutE = typename Gemm::GemmKernel::CollectiveMainloop::LayoutE;
using StrideA = cutlass::gemm::TagToStrideA_t<LayoutTagA>;
using StrideE = StrideA;
using StrideB = typename Gemm::GemmKernel::StrideB;
using StrideC = typename Gemm::GemmKernel::StrideC;
using StrideD = typename Gemm::GemmKernel::StrideD;
//
// Compressor
//
using SparseConfig = typename Gemm::GemmKernel::CollectiveMainloop::SparseConfig;
using CompressorUtility = cutlass::transform::kernel::StructuredSparseCompressorUtility<
ProblemShape,
ElementA,
LayoutTagA,
SparseConfig>;
using CompressorKernel = cutlass::transform::kernel::StructuredSparseCompressor<
ProblemShape,
ElementA,
LayoutTagA,
SparseConfig,
ArchTag>;
using Compressor = cutlass::transform::device::TransformUniversalAdapter<CompressorKernel>;
//
// Data members
//
/// Initialization
LayoutA layout_A;
LayoutE layout_E;
StrideA stride_A;
StrideA stride_A_compressed;
StrideE stride_E;
StrideB stride_B;
StrideC stride_C;
StrideD stride_D;
uint64_t seed;
ProblemShape problem_shape;
cutlass::DeviceAllocation<typename Gemm::ElementA> block_A;
cutlass::DeviceAllocation<typename Gemm::ElementA> block_A_compressed;
cutlass::DeviceAllocation<typename Gemm::CollectiveMainloop::ElementE> block_E;
cutlass::DeviceAllocation<typename Gemm::ElementB> block_B;
cutlass::DeviceAllocation<typename Gemm::ElementC> block_C;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput> block_D;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput> block_ref_D;
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Testbed utility types
/////////////////////////////////////////////////////////////////////////////////////////////////
// Command line options parsing
struct Options {
bool help;
float alpha, beta;
int iterations;
int m, n, k, l;
Options():
help(false),
m(8192), n(8192), k(8192), l(1),
alpha(1.f), beta(0.f),
iterations(10)
{ }
// Parses the command line
void parse(int argc, char const **args) {
cutlass::CommandLine cmd(argc, args);
if (cmd.check_cmd_line_flag("help")) {
help = true;
return;
}
cmd.get_cmd_line_argument("m", m);
cmd.get_cmd_line_argument("n", n);
cmd.get_cmd_line_argument("k", k);
cmd.get_cmd_line_argument("l", l);
cmd.get_cmd_line_argument("alpha", alpha, 1.f);
cmd.get_cmd_line_argument("beta", beta, 0.f);
cmd.get_cmd_line_argument("iterations", iterations);
}
/// Prints the usage statement.
std::ostream & print_usage(std::ostream &out) const {
out << "83_blackwell_sparse_gemm\n\n"
<< " Blackwell FP16 Sparse GEMM example.\n\n"
<< "Options:\n\n"
<< " --help If specified, displays this usage statement\n\n"
<< " --m=<int> Sets the M extent of the GEMM\n"
<< " --n=<int> Sets the N extent of the GEMM\n"
<< " --k=<int> Sets the K extent of the GEMM\n"
<< " --l=<int> Sets the L extent of the GEMM\n"
<< " --alpha=<f32> Epilogue scalar alpha\n"
<< " --beta=<f32> Epilogue scalar beta\n\n"
<< " --iterations=<int> Number of profiling iterations to perform.\n\n";
out
<< "\n\nExamples:\n\n"
<< "$ " << "83_blackwell_sparse_gemm" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
return out;
}
/// Compute performance in GFLOP/s
double gflops(double runtime_s) const
{
// Two flops per multiply-add
uint64_t flop = uint64_t(2) * m * n * k;
double gflop = double(flop) / double(1.0e9);
return gflop / runtime_s;
}
};
/// Result structure
struct Result
{
double avg_runtime_ms;
double gflops;
cutlass::Status status;
cudaError_t error;
bool passed;
Result(
double avg_runtime_ms = 0,
double gflops = 0,
cutlass::Status status = cutlass::Status::kSuccess,
cudaError_t error = cudaSuccess)
:
avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
{}
};
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM setup and evaluation
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Helper to initialize a block of device data
template <class Element>
bool initialize_block(
cutlass::DeviceAllocation<Element>& block,
uint64_t seed=2023) {
Element scope_max, scope_min;
constexpr int bits_input = cutlass::sizeof_bits<Element>::value;
if constexpr (bits_input == 1) {
scope_max = Element(2);
scope_min = Element(0);
}
else if constexpr (bits_input <= 8) {
scope_max = Element(2);
scope_min = Element(-2);
}
else {
scope_max = Element(8);
scope_min = Element(-8);
}
cutlass::reference::device::BlockFillRandomUniform(
block.get(), block.size(), seed, scope_max, scope_min, 0);
return true;
}
/// Make A structured sparse by replacing elements with 0 and compress it
bool sparsify_and_compress()
{
auto [M, N, K, L] = problem_shape;
CompressorUtility compressor_utility(problem_shape, stride_A);
// TensorE
// In unit of ElementE (uint8_t), after alignment requirement
// M-dim: TensorEAtom_M alignment
// K-dim: TensorEAtom_K alignment
int KAlignedE = compressor_utility.get_metadata_k_physical();
int MAlignedE = compressor_utility.get_metadata_m_physical();
// TensorA Compressed
// In unit of ElementARaw, after alignment requirement
// M-dim: TMA alignment
// K-dim: TMA alignment
int KAlignedAC = compressor_utility.get_tensorA_k_physical();
int MAlignedAC = compressor_utility.get_tensorA_m_physical();
block_A_compressed.reset(M * KAlignedAC * L);
block_E.reset(MAlignedE * KAlignedE * L);
stride_A_compressed = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, KAlignedAC, L));
stride_E = cutlass::make_cute_packed_stride(StrideE{}, cute::make_shape(MAlignedE, KAlignedE, L));
// Random 50% fill zero is performed on host
std::vector<ElementA> block_A_host(block_A.size());
cutlass::device_memory::copy_to_host(block_A_host.data(), block_A.get(), block_A.size());
compressor_utility.structure_sparse_zero_mask_fill(block_A_host.data(), static_cast<int>(seed + 2024));
cutlass::device_memory::copy_to_device(block_A.get(), block_A_host.data(), block_A.size());
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = 0;
hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
typename Compressor::Arguments arguments {
problem_shape,
{ block_A.get(),
stride_A,
block_A_compressed.get(),
block_E.get() },
{hw_info} };
Compressor compressor_op;
size_t workspace_size = Compressor::get_workspace_size(arguments);
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
cutlass::Status status {cutlass::Status::kSuccess };
status = compressor_op.can_implement(arguments);
if (status != cutlass::Status::kSuccess) {
return false;
}
status = compressor_op.initialize(arguments, workspace.get());
if (status != cutlass::Status::kSuccess) {
return false;
}
status = compressor_op.run();
if (status != cutlass::Status::kSuccess) {
return false;
}
auto result = cudaDeviceSynchronize();
if (result != cudaSuccess) {
return false;
}
return true;
}
/// Initialize operands to be used in the GEMM and reference GEMM
bool initialize(const Options &options) {
stride_A = cutlass::make_cute_packed_stride(StrideA{}, {options.m, options.k, 1});
stride_B = cutlass::make_cute_packed_stride(StrideB{}, {options.n, options.k, 1});
stride_C = cutlass::make_cute_packed_stride(StrideC{}, {options.m, options.n, 1});
stride_D = cutlass::make_cute_packed_stride(StrideD{}, {options.m, options.n, 1});
block_A.reset(options.m * options.k);
block_B.reset(options.k * options.n);
block_C.reset(options.m * options.n);
block_D.reset(options.m * options.n);
block_ref_D.reset(options.m * options.n);
initialize_block(block_A, seed + 2023);
initialize_block(block_B, seed + 2022);
initialize_block(block_C, seed + 2021);
// Compress row A and get A_compress and E
problem_shape = make_tuple(options.m, options.n, options.k, options.l);
if (not sparsify_and_compress()) {
return false;
};
// Build the compressed/metadata layouts
layout_A = SparseConfig::fill_layoutA(problem_shape);
layout_E = SparseConfig::fill_layoutE(problem_shape);
return true;
}
/// Populates a Gemm::Arguments structure from the given commandline options
typename Gemm::Arguments args_from_options(const Options &options)
{
typename Gemm::Arguments arguments {
cutlass::gemm::GemmUniversalMode::kGemm,
problem_shape,
{ block_A_compressed.get(), layout_A, block_B.get(), stride_B, block_E.get(), layout_E },
{{options.alpha, options.beta}, block_C.get(), stride_C, block_D.get(), stride_D}
};
return arguments;
}
bool verify(const Options &options) {
cutlass::TensorRef ref_A(block_A.get(), Gemm::LayoutA::packed({options.m, options.k}));
cutlass::TensorRef ref_B(block_B.get(), Gemm::LayoutB::packed({options.k, options.n}));
cutlass::TensorRef ref_C(block_C.get(), Gemm::LayoutC::packed({options.m, options.n}));
cutlass::TensorRef ref_D(block_ref_D.get(), Gemm::LayoutD::packed({options.m, options.n}));
//
// Compute reference output
//
// Create instantiation for device reference gemm kernel
DeviceGemmReference gemm_reference;
// Launch device reference gemm kernel
gemm_reference(
{options.m, options.n, options.k},
ElementAccumulator(options.alpha),
ref_A,
ref_B,
ElementAccumulator(options.beta),
ref_C,
ref_D);
// Wait for kernel to finish
CUDA_CHECK(cudaDeviceSynchronize());
// Check if output from CUTLASS kernel and reference kernel are equal or not
bool passed = cutlass::reference::device::BlockCompareEqual(block_ref_D.get(), block_D.get(), block_D.size());
return passed;
}
/// Execute a given example GEMM computation
template <typename Gemm>
int run(Options &options)
{
auto init_pass = initialize(options);
if (not init_pass) {
std::cout << "Initialization failure" << std::endl;
exit(EXIT_FAILURE);
}
// Instantiate CUTLASS kernel depending on templates
Gemm gemm;
// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
auto arguments = args_from_options(options);
// Using the arguments, query for extra workspace required for matrix multiplication computation
size_t workspace_size = Gemm::get_workspace_size(arguments);
// Allocate workspace memory
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
// Check if the problem size is supported or not
CUTLASS_CHECK(gemm.can_implement(arguments));
// Initialize CUTLASS kernel with arguments and workspace pointer
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
// Correctness / Warmup iteration
CUTLASS_CHECK(gemm.run());
cudaDeviceSynchronize();
// Check if output from CUTLASS kernel and reference kernel are equal or not
Result result;
result.passed = verify(options);
std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
if (not result.passed) {
exit(-1);
}
// Run profiling loop
if (options.iterations > 0)
{
GpuTimer timer;
timer.start();
for (int iter = 0; iter < options.iterations; ++iter) {
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
CUTLASS_CHECK(gemm.run());
}
timer.stop();
// Compute average runtime and GFLOPs.
float elapsed_ms = timer.elapsed_millis();
result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
std::cout << " Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << std::endl;
std::cout << " Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
std::cout << " GFLOPS: " << result.gflops << std::endl;
}
return 0;
}
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
///////////////////////////////////////////////////////////////////////////////////////////////////
int main(int argc, char const **args) {
// CUTLASS must be compiled with CUDA 12.8 or higher Toolkit to run this example
// and must have compute capability at least 100.
if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 8)) {
std::cerr << "This example requires CUDA 12.8 or newer." << std::endl;
// Returning zero so this test passes on older Toolkits. Its actions are no-op.
return 0;
}
cudaDeviceProp props;
int current_device_id;
CUDA_CHECK(cudaGetDevice(&current_device_id));
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
cudaError_t error = cudaGetDeviceProperties(&props, 0);
if (not (props.major == 10 && props.minor == 0)) {
std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100)." << std::endl;
return 0;
}
//
// Parse options
//
Options options;
options.parse(argc, args);
if (options.help) {
options.print_usage(std::cout) << std::endl;
return 0;
}
//
// Evaluate CUTLASS kernels
//
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
run<Gemm>(options);
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
return 0;
}
/////////////////////////////////////////////////////////////////////////////////////////////////
examples/83_blackwell_sparse_gemm/CMakeLists.txt
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# Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# 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.
#
# 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.
#
# 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
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
if (CUTLASS_NVCC_ARCHS MATCHES 100a)
cutlass_example_add_executable(
83_blackwell_sparse_gemm
83_blackwell_sparse_gemm.cu
)
endif()