284804ac53
Sources: siboehm/SGEMM_CUDA → upstream_ref/sgemm_siboehm/ (25 files) wangzyon/NVIDIA_SGEMM_PRACTICE → upstream_ref/nvidia_sgemm_practice/ (23 files, filled gaps) edtallison/sgemm-cuda → upstream_ref/sgemm_edtallison/ (41 files) All files cat'd one by one from git clone (no --depth). These are the 3 public SGEMM repos that can compile on CUDA 10.2 + CoreX ivcore10. Key files for BI-V100 porting: kernel 10 (warp tiling) — already proven on device with WARPSIZE=64 kernel 11/12 (double buffering) — next optimization target sgemm.cu + runner.cu — complete build+benchmark harness CMakeLists.txt — build system reference
108 lines
2.5 KiB
Plaintext
108 lines
2.5 KiB
Plaintext
#include <cstdio>
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#include <cublas_v2.h>
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#include <cuda_runtime.h>
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/*
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* A stand-alone script to invoke & benchmark standard cuBLAS SGEMM performance
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*/
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int main(int argc, char *argv[]) {
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int m = 2;
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int k = 3;
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int n = 4;
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int print = 1;
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cudaError_t cudaStat; // cudaMalloc status
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cublasStatus_t stat; // cuBLAS functions status
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cublasHandle_t handle; // cuBLAS context
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int i, j;
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float *a, *b, *c;
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// malloc for a,b,c...
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a = (float *)malloc(m * k * sizeof(float));
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b = (float *)malloc(k * n * sizeof(float));
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c = (float *)malloc(m * n * sizeof(float));
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int ind = 11;
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for (j = 0; j < m * k; j++) {
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a[j] = (float)ind++;
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}
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ind = 11;
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for (j = 0; j < k * n; j++) {
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b[j] = (float)ind++;
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}
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ind = 11;
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for (j = 0; j < m * n; j++) {
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c[j] = (float)ind++;
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}
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// DEVICE
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float *d_a, *d_b, *d_c;
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// cudaMalloc for d_a, d_b, d_c...
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cudaMalloc((void **)&d_a, m * k * sizeof(float));
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cudaMalloc((void **)&d_b, k * n * sizeof(float));
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cudaMalloc((void **)&d_c, m * n * sizeof(float));
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stat = cublasCreate(&handle); // initialize CUBLAS context
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cudaMemcpy(d_a, a, m * k * sizeof(float), cudaMemcpyHostToDevice);
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cudaMemcpy(d_b, b, k * n * sizeof(float), cudaMemcpyHostToDevice);
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cudaMemcpy(d_c, c, m * n * sizeof(float), cudaMemcpyHostToDevice);
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float alpha = 1.0f;
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float beta = 0.5f;
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if (print == 1) {
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printf("alpha = %4.0f, beta = %4.0f\n", alpha, beta);
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printf("A = (mxk: %d x %d)\n", m, k);
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for (i = 0; i < m; i++) {
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for (j = 0; j < k; j++) {
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printf("%4.1f ", a[i * m + j]);
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}
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printf("\n");
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}
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printf("B = (kxn: %d x %d)\n", k, n);
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for (i = 0; i < k; i++) {
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for (j = 0; j < n; j++) {
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printf("%4.1f ", b[i * n + j]);
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}
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printf("\n");
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}
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printf("C = (mxn: %d x %d)\n", m, n);
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for (i = 0; i < m; i++) {
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for (j = 0; j < n; j++) {
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printf("%4.1f ", c[i * n + j]);
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}
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printf("\n");
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}
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}
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stat = cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, n, m, k, &alpha, d_b, n,
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d_a, k, &beta, d_c, n);
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cudaMemcpy(c, d_c, m * n * sizeof(float), cudaMemcpyDeviceToHost);
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if (print == 1) {
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printf("\nC after SGEMM = \n");
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for (i = 0; i < m; i++) {
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for (j = 0; j < n; j++) {
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printf("%4.1f ", c[i * n + j]);
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}
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printf("\n");
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}
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}
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cudaFree(d_a);
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cudaFree(d_b);
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cudaFree(d_c);
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cublasDestroy(handle); // destroy CUBLAS context
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free(a);
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free(b);
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free(c);
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return EXIT_SUCCESS;
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} |