我认为使用以下方法可能会实现最快的性能CUBLAS批量gemm函数 http://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-gemmbatched它是专门为此目的而设计的(执行大量“相对较小”的矩阵乘法运算)。
Even though you want to multiply your array of matrices (M[]
) by a single matrix (N
), the batch gemm function will require you to pass also an array of matrices for N
(i.e. N[]
), which will all be the same in your case.
EDIT:现在我已经完成了一个示例,对我来说很清楚,通过对下面的示例进行修改,我们可以传递一个N
矩阵并有GPU_Multi
函数只需发送单个N
矩阵到设备,同时传递一个指针数组N
, i.e. d_Narray
在下面的示例中,所有指针都指向同一个N
设备上的矩阵。
这是一个完整的批量 GEMM 示例:
#include <stdio.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <assert.h>
#define ROWM 4
#define COLM 3
#define COLN 5
#define cudaCheckErrors(msg) \
do { \
cudaError_t __err = cudaGetLastError(); \
if (__err != cudaSuccess) { \
fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
msg, cudaGetErrorString(__err), \
__FILE__, __LINE__); \
fprintf(stderr, "*** FAILED - ABORTING\n"); \
exit(1); \
} \
} while (0)
typedef float mytype;
// Pi = Mi x Ni
// pr = P rows = M rows
// pc = P cols = N cols
// mc = M cols = N rows
void GPU_Multi(mytype **M, mytype **N, mytype **P
, size_t pr, size_t pc, size_t mc
, size_t num_mat, mytype alpha, mytype beta)
{
mytype *devM[num_mat];
mytype *devN[num_mat];
mytype *devP[num_mat];
size_t p_size =sizeof(mytype) *pr*pc;
size_t m_size =sizeof(mytype) *pr*mc;
size_t n_size =sizeof(mytype) *mc*pc;
const mytype **d_Marray, **d_Narray;
mytype **d_Parray;
cublasHandle_t myhandle;
cublasStatus_t cublas_result;
for(int i = 0 ; i < num_mat; i ++ )
{
cudaMalloc((void**)&devM[ i ], m_size );
cudaMalloc((void**)&devN[ i ], n_size );
cudaMalloc((void**)&devP[ i ], p_size );
}
cudaMalloc((void**)&d_Marray, num_mat*sizeof(mytype *));
cudaMalloc((void**)&d_Narray, num_mat*sizeof(mytype *));
cudaMalloc((void**)&d_Parray, num_mat*sizeof(mytype *));
cudaCheckErrors("cudaMalloc fail");
for(int i = 0 ; i < num_mat; i ++ ) {
cudaMemcpy(devM[i], M[i], m_size , cudaMemcpyHostToDevice);
cudaMemcpy(devN[i], N[i], n_size , cudaMemcpyHostToDevice);
cudaMemcpy(devP[i], P[i], p_size , cudaMemcpyHostToDevice);
}
cudaMemcpy(d_Marray, devM, num_mat*sizeof(mytype *), cudaMemcpyHostToDevice);
cudaMemcpy(d_Narray, devN, num_mat*sizeof(mytype *), cudaMemcpyHostToDevice);
cudaMemcpy(d_Parray, devP, num_mat*sizeof(mytype *), cudaMemcpyHostToDevice);
cudaCheckErrors("cudaMemcpy H2D fail");
cublas_result = cublasCreate(&myhandle);
assert(cublas_result == CUBLAS_STATUS_SUCCESS);
// change to cublasDgemmBatched for double
cublas_result = cublasSgemmBatched(myhandle, CUBLAS_OP_N, CUBLAS_OP_N
, pr, pc, mc
, &alpha, d_Marray, pr, d_Narray, mc
, &beta, d_Parray, pr
, num_mat);
assert(cublas_result == CUBLAS_STATUS_SUCCESS);
for(int i = 0 ; i < num_mat ; i ++ )
{
cudaMemcpy(P[i], devP[i], p_size, cudaMemcpyDeviceToHost);
cudaFree(devM[i]);
cudaFree(devN[i]);
cudaFree(devP[i]);
}
cudaFree(d_Marray);
cudaFree(d_Narray);
cudaFree(d_Parray);
cudaCheckErrors("cudaMemcpy D2H fail");
}
int main(){
mytype h_M1[ROWM][COLM], h_M2[ROWM][COLM];
mytype h_N1[COLM][COLN], h_N2[COLM][COLN];
mytype h_P1[ROWM][COLN], h_P2[ROWM][COLN];
mytype *h_Marray[2], *h_Narray[2], *h_Parray[2];
for (int i = 0; i < ROWM; i++)
for (int j = 0; j < COLM; j++){
h_M1[i][j] = 1.0f; h_M2[i][j] = 2.0f;}
for (int i = 0; i < COLM; i++)
for (int j = 0; j < COLN; j++){
h_N1[i][j] = 1.0f; h_N2[i][j] = 1.0f;}
for (int i = 0; i < ROWM; i++)
for (int j = 0; j < COLN; j++){
h_P1[i][j] = 0.0f; h_P2[i][j] = 0.0f;}
h_Marray[0] = &(h_M1[0][0]);
h_Marray[1] = &(h_M2[0][0]);
h_Narray[0] = &(h_N1[0][0]);
h_Narray[1] = &(h_N2[0][0]);
h_Parray[0] = &(h_P1[0][0]);
h_Parray[1] = &(h_P2[0][0]);
GPU_Multi(h_Marray, h_Narray, h_Parray, ROWM, COLN, COLM, 2, 1.0f, 0.0f);
for (int i = 0; i < ROWM; i++)
for (int j = 0; j < COLN; j++){
if (h_P1[i][j] != COLM*1.0f)
{
printf("h_P1 mismatch at %d,%d was: %f should be: %f\n"
, i, j, h_P1[i][j], COLM*1.0f); return 1;
}
if (h_P2[i][j] != COLM*2.0f)
{
printf("h_P2 mismatch at %d,%d was: %f should be: %f\n"
, i, j, h_P2[i][j], COLM*2.0f); return 1;
}
}
printf("Success!\n");
return 0;
}