cpu gpu做矩阵乘法效率比对，虽然如此，但是对需要自己做的算法是否能如此高效的提高还是未知

vs2010上创建cuda项目，kernel.cu，将如下代码拷贝进去，编译执行，能很清楚地看到GPU跑矩阵乘法和CPU的效率区别。在我的pc机上执行得到如下结果,开发环境VS2010+cuda4.1开发包+Tesla c2075显卡

//代码借用别人的，自己修改后测试通过

///////////////////////

#include <stdio.h>

#include <cstdlib>

#include <iostream>

#include <cutil.h>

//#include "cuda_class.h"

#include "cuda_runtime.h"

#include "device_launch_parameters.h"

using namespace std;

#if __DEVICE_EMULATION__

bool InitCUDA(void){return true;}

#else

bool InitCUDA(void)

{

int count = 0;

int i = 0;

cudaGetDeviceCount(&count);

if(count == 0) {

fprintf(stderr, "There is no device.\n");

return false;

}

for(i = 0; i < count; i++) {

cudaDeviceProp prop;

if(cudaGetDeviceProperties(&prop, i) == cudaSuccess) {

if(prop.major >= 1) {

break;

}

}

}

if(i == count) {

fprintf(stderr, "There is no device supporting CUDA.\n");

return false;

}

cudaSetDevice(i);

printf("CUDA initialized.\n");

cudaDeviceProp prop;

cudaGetDeviceProperties(&prop,i);

printf("Device : \" %s \" \n\n", prop.name);

return true;

}

#endif

#define aW 855

#define aH 511

#define bW 1013

#define blocknum 32//32

#define threadnum 256//256

typedef struct

{

int width;

int height;

int *element;

}Matrix;

Matrix InitMatrix(int w, int h)

{

Matrix t;

t.element=(int *)malloc(w * h * sizeof(int) );

for ( int i=0 ; i < w*h ; i ++)

t.element[i]= rand() % 10;

t.width=w;

t.height=h;

return t;

}

Matrix MM( Matrix a, Matrix b)

{

Matrix t;

t.element=(int *) malloc (a.height * b.width * sizeof(int));

t.width=b.width;

t.height=a.height;

int x;

int y;

for ( int i =0 ; i < t.width * t.height ; i ++ )

{

x=i / t.width * a.width;

y=i - i / t.width * t.width;

t.element[i]=0;

for ( int k = 0 ; k < a. width ; k ++ )

{

t.element[i] += a.element[x + k] * b.element [y +b.width * k];

}

}

return t;

}



__global__ static void MatrixMul(int *ma , int *mb , int *mc , int *mp)

{

int aw = mp[0];

int bw = mp[2];

int cw = mp[4];

int ch = mp[5];

const int bid = blockIdx.x;

const int tid = threadIdx.x;

int i , x , y ;



for ( i = bid * threadnum + tid ; i < cw * ch ; i += threadnum * blocknum )

{

x = i / cw * aw;

y = i - i / cw * cw;

mc[i] = 0;

for ( int k = 0 ; k < aw ; k ++ )

{

mc[i] += ma[ x + k ] * mb[ y + k * bw ];

}

}

}



int main(int argc, char* argv[])

{

if(!InitCUDA()) {

return 0;

}

//定义矩阵

//int matrixa

, matrixb

, matrixc

, gpuresult

, matrixd

;

Matrix matrixa=InitMatrix(aW,aH);

Matrix matrixb=InitMatrix(bW,aW);

Matrix matrixc;

Matrix gpuresult=InitMatrix(bW,aH);



int matrixprop[6];



//为CPU运算计时

unsigned int timer1 = 0;

//CPU矩阵相乘

int start = clock();

matrixc=MM(matrixa,matrixb);

int finish = clock();

printf("cpu time = %d\n", finish-start);

start = clock();

matrixprop[0] = matrixa.width;

matrixprop[1] = matrixa.height;

matrixprop[2] = matrixb.width;

matrixprop[3] = matrixb.height;

matrixprop[4] = matrixc.width;

matrixprop[5] = matrixc.height;



//申请显存

int *ma, *mb, *mc, *mp;

cudaMalloc( (void**)&ma , sizeof(int) * matrixa.width * matrixa.height );

cudaMalloc( (void**)&mb , sizeof(int) * matrixb.width * matrixb.height );

cudaMalloc( (void**)&mc , sizeof(int) * matrixc.width * matrixc.height );

cudaMalloc( (void**)&mp , sizeof(int) * 6 );

//将数据复制到显存内

cudaMemcpy(ma , matrixa.element , sizeof(int) * matrixa.width * matrixa.height ,cudaMemcpyHostToDevice);

cudaMemcpy(mb , matrixb.element , sizeof(int) * matrixb.width * matrixb.height ,cudaMemcpyHostToDevice);

cudaMemcpy(mp , matrixprop , sizeof(int) * 6 , cudaMemcpyHostToDevice);

unsigned int timer2 = 0;

//调用CUDA函数

MatrixMul <<< blocknum , threadnum , 0 >>>( ma , mb , mc , mp);

cudaThreadSynchronize();

//cutilCheckError( cutStopTimer( timer2));

//将数据从显存中复制出来

cudaMemcpy( gpuresult.element , mc , sizeof(int) * gpuresult.width * gpuresult.height , cudaMemcpyDeviceToHost );

finish = clock();

printf("gpu time = %d\n", finish-start);

for ( int i =0 ; i < gpuresult.width * gpuresult.height ; i ++ )

{

//printf("%d -- %d\n",matrixc.element[ i ],gpuresult.element[ i ]);

if ( matrixc.element[i] != gpuresult.element[i] )

{

printf("ERROR");

}

}

cudaFree(ma);

cudaFree(mb);

cudaFree(mc);

cudaFree(mp);

system("pause");

return 0;

}

测试的结果如下：仅供参考