使用CUBLAS的一些小例子

1. 矩阵相乘再加 C = a*A*B + b*C

#include "cuda_runtime.h"
#include "cublas_v2.h"

#include 
#include 

using namespace std;

int const M = 6;
int const N = 10;

int main()
{
	cublasStatus_t status;

	//Host memory malloc
	float *h_A = (float*)malloc(N*M*sizeof(float));
	float *h_B = (float*)malloc(N*M*sizeof(float));
	float *h_C = (float*)malloc(M*M*sizeof(float));
	float *h_C_cpu = (float*)malloc(M*M*sizeof(float));
	memset(h_C_cpu,0,M*M*sizeof(float));

	//Initialize and print
	for (int i=0; i

2.更规范的一个例子，由SDK中的例子改编，与1类似，比较了CPU与GPU运算效率：

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/* 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 
#include 
#include 
#include 
#include "time.h"
/* Includes, cuda */
#include 
#include 
#include 
using namespace std;
/* Matrix size */
#define N  (600)

/* 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;


	clock_t cuStart,cuFinish;
	clock_t start,finish;

	
    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 = (float *)malloc(n2 * sizeof(h_A[0]));

    if (h_A == 0)
    {
        fprintf(stderr, "!!!! host memory allocation error (A)\n");
        return EXIT_FAILURE;
    }

    h_B = (float *)malloc(n2 * sizeof(h_B[0]));

    if (h_B == 0)
    {
        fprintf(stderr, "!!!! host memory allocation error (B)\n");
        return EXIT_FAILURE;
    }

    h_C = (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() / (float)RAND_MAX;
        h_B[i] = rand() / (float)RAND_MAX;
        h_C[i] = rand() / (float)RAND_MAX;
    }
	cuStart = clock();
    /* Allocate device memory for the matrices */
    if (cudaMalloc((void **)&d_A, n2 * sizeof(d_A[0])) != cudaSuccess)
    {
        fprintf(stderr, "!!!! device memory allocation error (allocate A)\n");
        return EXIT_FAILURE;
    }

    if (cudaMalloc((void **)&d_B, n2 * sizeof(d_B[0])) != cudaSuccess)
    {
        fprintf(stderr, "!!!! device memory allocation error (allocate B)\n");
        return EXIT_FAILURE;
    }

    if (cudaMalloc((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 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_ref = (float *)malloc(n2 * sizeof(h_C[0]));

    if (h_C_ref == 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_ref, 1);

    if (status != CUBLAS_STATUS_SUCCESS)
    {
        fprintf(stderr, "!!!! device access error (read C)\n");
        return EXIT_FAILURE;
    }


	cuFinish = clock();

	start = clock();
	/* Performs operation using plain C code */
	simple_sgemm(N, alpha, h_A, h_B, beta, h_C);
	finish = clock();

	cout<<"GPU TIME:"<<(double)(cuFinish - cuStart)/CLOCKS_PER_SEC<