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https://github.com/NVIDIA/cuda-samples.git
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b7c5481c55
This is the release of the CUDA 13.3 samples, which include additions for CUDA Tile C++, and updated CCCL and Python samples.
97 lines
3.9 KiB
C++
97 lines
3.9 KiB
C++
/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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* This file provides matrix multiplication benchmark helpers shared by
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* tileMatmul and tileMatmulAutotuner.
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*/
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#ifndef CUDA_TILE_MATMUL_BENCHMARK_H
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#define CUDA_TILE_MATMUL_BENCHMARK_H
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#include "benchmark.h"
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#include <cuda_fp16.h>
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#include <cmath>
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inline void fill_matmul_metrics(BenchmarkResult& result, int M, int N, int K) {
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// FLOPs: 2 * M * N * K (multiply + add for each output element).
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double flops = 2.0 * M * N * K;
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result.gflops = flops / (result.time_ms * 1e6);
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// Bandwidth: read A, read B, and write C.
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size_t bytes = ((size_t)M * K + (size_t)K * N) * sizeof(__half) +
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(size_t)M * N * sizeof(float);
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result.bandwidth_gb_s = (bytes / 1e9) / (result.time_ms / 1000.0);
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}
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// CPU reference implementation (FP16 -> FP32).
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inline void matmul_cpu(float* C, const __half* A, const __half* B, int M, int N, int K) {
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for (int i = 0; i < M; i++) {
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for (int j = 0; j < N; j++) {
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float sum = 0.0f;
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for (int k = 0; k < K; k++) {
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sum += __half2float(A[i * K + k]) * __half2float(B[k * N + j]);
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}
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C[i * N + j] = sum;
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}
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}
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}
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inline bool verify_matmul_result(const char* name,
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const float* h_result,
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const float* h_expected,
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int M, int N) {
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for (int i = 0; i < M * N; i++) {
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float abs_err = std::abs(h_result[i] - h_expected[i]);
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float rel_err = abs_err / (std::abs(h_expected[i]) + 1e-6f);
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// FP16 has less precision, so allow a larger tolerance.
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if (abs_err > 1e-2f && rel_err > 0.1f) {
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printf("%s verification failed at %d: got %f, expected %f\n",
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name, i, h_result[i], h_expected[i]);
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return false;
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}
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}
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return true;
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}
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template<typename KernelFunc, typename ValidateFunc>
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inline BenchmarkResult run_benchmark(const char* name,
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KernelFunc kernel_launch,
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ValidateFunc validate_result,
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int M, int N, int K) {
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BenchmarkResult result;
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result.name = name;
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result.time_ms = time_kernel(kernel_launch);
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fill_matmul_metrics(result, M, N, K);
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result.correct = !use_validation() || validate_result();
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return result;
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}
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#endif // CUDA_TILE_MATMUL_BENCHMARK_H
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