本代码实现计算矢量求和时,当矢量长度比较小,一个线程负责计算一个个维度的矢量求和,即数据平铺,当矢量长度较大,采取数据分块,一个线程可能负责多个维度的矢量求和。未使用优化手段,代码中包含对于计算正确性的检验、与CPU计算耗时的对比。
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <random>
#include "time.h"
#define N 130712
int numBlocks = (N + 512 - 1) / 512 > 512 ? 512 : (N + 512 - 1) / 512;
__global__ void addKernel(int* c, const int* a, const int* b)
{
int offset = blockDim.x * gridDim.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
while (idx < N)
{
c[idx] = a[idx] + b[idx];
idx += offset;
}
}
void addCPU(int* c, int* a, int* b) {
for (int i = 0; i < N; ++i)
c[i] = a[i] + b[i];
}
int main()
{
int* a = new int[N];
int* b = new int[N];
int* c1 = new int[N];
int* c2 = new int[N];
srand(0);
for (int i = 0; i < N; ++i) {
a[i] = rand() % 1000;
b[i] = rand() % 1000;
}
clock_t start, end;
double elapsedTime;
start = clock();
addCPU(c1, a, b);
end = clock();
elapsedTime = (double)(end - start);
printf("time to generate CPU:% 5.3f ms\n", elapsedTime);
int* dev_a, * dev_b, * dev_c;
cudaMalloc((void**)&dev_c, N * sizeof(int));
cudaMalloc((void**)&dev_a, N * sizeof(int));
cudaMalloc((void**)&dev_b, N * sizeof(int));
cudaMemcpy(dev_a, a, N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N * sizeof(int), cudaMemcpyHostToDevice);
cudaEvent_t start1, stop1;
cudaEventCreate(&start1);
cudaEventCreate(&stop1);
cudaEventRecord(start1, 0);
addKernel << <numBlocks, 512 >> > (dev_c, dev_a, dev_b);
cudaEventRecord(stop1, 0);
cudaEventSynchronize(stop1);
float elapsedTime1;
cudaEventElapsedTime(&elapsedTime1, start1, stop1);
printf("time to generate GPU:% 5.3f ms\n", elapsedTime1);
cudaEventDestroy(start1);
cudaEventDestroy(stop1);
cudaMemcpy(c2, dev_c, N * sizeof(int), cudaMemcpyDeviceToHost);
bool flag = true;
for (int i = 0; i < N; ++i) {
if (c1[i] != c2[i]) {
flag = false;
break;
}
}
if (flag) printf("Consistent!!!\n");
else printf("Not consistent!!!\n");
delete[] a;
delete[] b;
delete[] c1;
delete[] c2;
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);
return 0;
}
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