mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-28 15:49:17 +08:00
256 lines
7.2 KiB
C++
256 lines
7.2 KiB
C++
/* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * 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.
|
|
* * Neither the name of NVIDIA CORPORATION 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 ``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 OWNER 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.
|
|
*/
|
|
|
|
/* This example demonstrates how to use the CUBLAS library
|
|
* by scaling an array of floating-point values on the device
|
|
* and comparing the result to the same operation performed
|
|
* on the host.
|
|
*/
|
|
|
|
/* Includes, system */
|
|
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
|
|
/* Includes, cuda */
|
|
#include <cublas_v2.h>
|
|
#include <cuda_runtime.h>
|
|
#include <helper_cuda.h>
|
|
|
|
/* Matrix size */
|
|
#define N (275)
|
|
|
|
/* Host implementation of a simple version of sgemm */
|
|
static void simple_sgemm(int n, float alpha, const float *A, const float *B,
|
|
float beta, float *C) {
|
|
int i;
|
|
int j;
|
|
int k;
|
|
|
|
for (i = 0; i < n; ++i) {
|
|
for (j = 0; j < n; ++j) {
|
|
float prod = 0;
|
|
|
|
for (k = 0; k < n; ++k) {
|
|
prod += A[k * n + i] * B[j * n + k];
|
|
}
|
|
|
|
C[j * n + i] = alpha * prod + beta * C[j * n + i];
|
|
}
|
|
}
|
|
}
|
|
|
|
/* Main */
|
|
int main(int argc, char **argv) {
|
|
cublasStatus_t status;
|
|
float *h_A;
|
|
float *h_B;
|
|
float *h_C;
|
|
float *h_C_ref;
|
|
float *d_A = 0;
|
|
float *d_B = 0;
|
|
float *d_C = 0;
|
|
float alpha = 1.0f;
|
|
float beta = 0.0f;
|
|
int n2 = N * N;
|
|
int i;
|
|
float error_norm;
|
|
float ref_norm;
|
|
float diff;
|
|
cublasHandle_t handle;
|
|
|
|
int dev = findCudaDevice(argc, (const char **)argv);
|
|
|
|
if (dev == -1) {
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Initialize CUBLAS */
|
|
printf("simpleCUBLAS test running..\n");
|
|
|
|
status = cublasCreate(&handle);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! CUBLAS initialization error\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Allocate host memory for the matrices */
|
|
h_A = reinterpret_cast<float *>(malloc(n2 * sizeof(h_A[0])));
|
|
|
|
if (h_A == 0) {
|
|
fprintf(stderr, "!!!! host memory allocation error (A)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
h_B = reinterpret_cast<float *>(malloc(n2 * sizeof(h_B[0])));
|
|
|
|
if (h_B == 0) {
|
|
fprintf(stderr, "!!!! host memory allocation error (B)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
h_C = reinterpret_cast<float *>(malloc(n2 * sizeof(h_C[0])));
|
|
|
|
if (h_C == 0) {
|
|
fprintf(stderr, "!!!! host memory allocation error (C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Fill the matrices with test data */
|
|
for (i = 0; i < n2; i++) {
|
|
h_A[i] = rand() / static_cast<float>(RAND_MAX);
|
|
h_B[i] = rand() / static_cast<float>(RAND_MAX);
|
|
h_C[i] = rand() / static_cast<float>(RAND_MAX);
|
|
}
|
|
|
|
/* Allocate device memory for the matrices */
|
|
if (cudaMalloc(reinterpret_cast<void **>(&d_A), n2 * sizeof(d_A[0])) !=
|
|
cudaSuccess) {
|
|
fprintf(stderr, "!!!! device memory allocation error (allocate A)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
if (cudaMalloc(reinterpret_cast<void **>(&d_B), n2 * sizeof(d_B[0])) !=
|
|
cudaSuccess) {
|
|
fprintf(stderr, "!!!! device memory allocation error (allocate B)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
if (cudaMalloc(reinterpret_cast<void **>(&d_C), n2 * sizeof(d_C[0])) !=
|
|
cudaSuccess) {
|
|
fprintf(stderr, "!!!! device memory allocation error (allocate C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Initialize the device matrices with the host matrices */
|
|
status = cublasSetVector(n2, sizeof(h_A[0]), h_A, 1, d_A, 1);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! device access error (write A)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
status = cublasSetVector(n2, sizeof(h_B[0]), h_B, 1, d_B, 1);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! device access error (write B)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
status = cublasSetVector(n2, sizeof(h_C[0]), h_C, 1, d_C, 1);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! device access error (write C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Performs operation using plain C code */
|
|
simple_sgemm(N, alpha, h_A, h_B, beta, h_C);
|
|
h_C_ref = h_C;
|
|
|
|
/* Performs operation using cublas */
|
|
status = cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, N, N, N, &alpha, d_A,
|
|
N, d_B, N, &beta, d_C, N);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! kernel execution error.\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Allocate host memory for reading back the result from device memory */
|
|
h_C = reinterpret_cast<float *>(malloc(n2 * sizeof(h_C[0])));
|
|
|
|
if (h_C == 0) {
|
|
fprintf(stderr, "!!!! host memory allocation error (C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Read the result back */
|
|
status = cublasGetVector(n2, sizeof(h_C[0]), d_C, 1, h_C, 1);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! device access error (read C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Check result against reference */
|
|
error_norm = 0;
|
|
ref_norm = 0;
|
|
|
|
for (i = 0; i < n2; ++i) {
|
|
diff = h_C_ref[i] - h_C[i];
|
|
error_norm += diff * diff;
|
|
ref_norm += h_C_ref[i] * h_C_ref[i];
|
|
}
|
|
|
|
error_norm = static_cast<float>(sqrt(static_cast<double>(error_norm)));
|
|
ref_norm = static_cast<float>(sqrt(static_cast<double>(ref_norm)));
|
|
|
|
if (fabs(ref_norm) < 1e-7) {
|
|
fprintf(stderr, "!!!! reference norm is 0\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Memory clean up */
|
|
free(h_A);
|
|
free(h_B);
|
|
free(h_C);
|
|
free(h_C_ref);
|
|
|
|
if (cudaFree(d_A) != cudaSuccess) {
|
|
fprintf(stderr, "!!!! memory free error (A)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
if (cudaFree(d_B) != cudaSuccess) {
|
|
fprintf(stderr, "!!!! memory free error (B)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
if (cudaFree(d_C) != cudaSuccess) {
|
|
fprintf(stderr, "!!!! memory free error (C)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
/* Shutdown */
|
|
status = cublasDestroy(handle);
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
fprintf(stderr, "!!!! shutdown error (A)\n");
|
|
return EXIT_FAILURE;
|
|
}
|
|
|
|
if (error_norm / ref_norm < 1e-6f) {
|
|
printf("simpleCUBLAS test passed.\n");
|
|
exit(EXIT_SUCCESS);
|
|
} else {
|
|
printf("simpleCUBLAS test failed.\n");
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
}
|