用cuda实现向量点乘

//计算向量的内积程序  
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include
#include  
#define imin(a,b)    (a

//N为输入的向量的规模  
const int N = 33 * 1024;
const int threadsPerBlock = 256;
const int blocksPerGrid =
imin(32, (N + threadsPerBlock - 1) / threadsPerBlock);


__global__ void dot(float *a, float *b, float *c)
{
//每一个块上都有cache变量的拷贝，相互之间不影响  
__shared__ float cache[threadsPerBlock];
//tid为线程的偏移量  
int tid = threadIdx.x + blockIdx.x*blockDim.x;
int cacheIndex = threadIdx.x;
float temp = 0;
while (tid {
temp += a[tid] * b[tid];
//增加的下标量为进程总数  
tid += blockDim.x*gridDim.x;


}
cache[cacheIndex] = temp;
//同步化当前块上的线程  
__syncthreads();


int i = blockDim.x / 2;
//在块内计算部分和  
while (i != 0)
{
if (cacheIndex cache[cacheIndex] += cache[cacheIndex + i];
__syncthreads();
i /= 2;
}


if (cacheIndex == 0)
c[blockIdx.x] = cache[0];


}




int main(void)
{


float *a, *b, c, *partial_c;


float *dev_a, *dev_b, *dev_partial_c;


a = (float*)malloc(N*sizeof(float));
b = (float*)malloc(N*sizeof(float));
partial_c = (float*)malloc(blocksPerGrid*sizeof(float));


cudaMalloc((void**)&dev_a, N*sizeof(float));
cudaMalloc((void**)&dev_b, N*sizeof(float));
cudaMalloc((void**)&dev_partial_c, blocksPerGrid*sizeof(float));




for (int i = 0; i {


a[i] = i;
b[i] = i * 2;
}


cudaMemcpy(dev_a, a, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N*sizeof(float), cudaMemcpyHostToDevice);


dot << > >(dev_a, dev_b, dev_partial_c);


cudaMemcpy(partial_c, dev_partial_c, blocksPerGrid*sizeof(float), cudaMemcpyDeviceToHost);


c = 0;
for (int i = 0; i c += partial_c[i];
//此程序相当于计算0,1,...N-1的平方和  
    #define sum_squares(x)   (x*(x+1)*(2*x+1)/6)  
//测试向量内积的正确性  
printf("Does GPU value %.6g = %.6g\n", c,
2 * sum_squares((float)(N - 1)));




cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_partial_c);


free(a);
free(b);
free(partial_c);
system("pause");
}