CUDA编程首先呢是分配thread以及block
#include
#include
#include //cuda运行时间接口
#define Thread_Num 256 //每一block包含的线程数
#define Matrix_Size 10
const int block_num=(Matrix_Size+Thread_Num-1)/Thread_Num;
然后是两个基本的函数:
//打印设备信息
void printDeviceProp(const cudaDeviceProp &prop)
{
printf("Device Name : %s.\n", prop.name);
printf("totalGlobalMem : %d.\n", prop.totalGlobalMem);
printf("sharedMemPerBlock : %d.\n", prop.sharedMemPerBlock);
printf("regsPerBlock : %d.\n", prop.regsPerBlock);
printf("warpSize : %d.\n", prop.warpSize);
printf("memPitch : %d.\n", prop.memPitch);
printf("maxThreadsPerBlock : %d.\n", prop.maxThreadsPerBlock);
printf("maxThreadsDim[0 - 2] : %d %d %d.\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf("maxGridSize[0 - 2] : %d %d %d.\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf("totalConstMem : %d.\n", prop.totalConstMem);
printf("major.minor : %d.%d.\n", prop.major, prop.minor);
printf("clockRate : %d.\n", prop.clockRate);
printf("textureAlignment : %d.\n", prop.textureAlignment);
printf("deviceOverlap : %d.\n", prop.deviceOverlap);
printf("multiProcessorCount : %d.\n", prop.multiProcessorCount);
}
//初始化cuda
bool InitCUDA()
{
int count;
cudaGetDeviceCount(&count);
if(count==0){
fprintf(stderr,"three is no device.\n");
return false;
}
int i;
for(i=0;i=1){break;}
}
}
if (i == count) {
fprintf(stderr, "There is no device supporting CUDA 1.x.\n");
return false;
}
cudaSetDevice(i);
return true;
}
//接着随机生成两个矩阵
void matGen(float* a, int n)
{
int i,j;
for(i=0;i
//并行矩阵乘法函数,最主要的一部分
__global__ static void matMultCuda(const float* a,const float* b,float* c,int n,clock_t* time)
{
const int tid=threadIdx.x;
const int bid=blockIdx.x;
//从 bid 和 tid 计算出这个 thread 应该计算的 row 和 column
const int idx = bid * Thread_Num + tid;
const int row = idx / n;
const int column = idx % n;
int i;
//clock_t start;
//每个block开始时记录
if(tid==0) time[bid]=clock();
//计算矩阵乘法
if(row < n && column < n)
{
float t=0;
for(i=0;i
//运算完后打印出矩阵
void printMatrix(const float *A, const int n) {
for(int i = 0; i < n; i++){
for(int j = 0; j < n; j++){
printf("%.2f" ,A[i*n+j]);
printf(" ");
}
printf("\n");
}
printf("\n");
}
//最后我们来看一下主函数
int main()
{
if(!InitCUDA()) return 0;
float *a,*b,*c;
int n=Matrix_Size;
//分配内存
a=(float*)malloc(sizeof(float)*n*n);
b=(float*)malloc(sizeof(float)* n*n);
c=(float*)malloc(sizeof(float)* n*n);
//设置随机种子
srand(0);
//随机生成两个矩阵
matGen(a,n);
matGen(b,n);
float *cuda_a,*cuda_b,*cuda_c;
clock_t* time;
//cudaMalloc 获取一块显卡内存
cudaMalloc((void**)&cuda_a, sizeof(float)* n*n);
cudaMalloc((void**)&cuda_b, sizeof(float)* n*n);
cudaMalloc((void**)&cuda_c, sizeof(float)* n*n);
cudaMalloc((void**)&time, sizeof(clock_t)* block_num*2);
//cudaMemcpy 将产生的矩阵复制到显卡内存中
//cudaMemcpyHostToDevice - 从内存复制到显卡内存
//cudaMemcpyDeviceToHost - 从显卡内存复制到内存
cudaMemcpy(a,cuda_a,sizeof(float)* n*n,cudaMemcpyHostToDevice);
cudaMemcpy(b,cuda_b,sizeof(float)* n*n,cudaMemcpyHostToDevice);
//printMatrix(cuda_a, n);
//printMatrix(cuda_b, n);
// 在CUDA 中执行函数 语法:函数名称<<>>(参数...);
matMultCuda<<>>(cuda_a,cuda_b,cuda_c,n,time);
//把结果复制回内存中
clock_t time_use[block_num*2];
cudaMemcpy(c,cuda_c,sizeof(float)* n*n,cudaMemcpyDeviceToHost);
cudaMemcpy(&time_use, time, sizeof(clock_t)* block_num * 2, cudaMemcpyDeviceToHost);
printMatrix(a, n);
printMatrix(b, n);
printMatrix(c, n);
//释放资源
cudaFree(cuda_a);
cudaFree(cuda_b);
cudaFree(cuda_c);
cudaFree(time);
//把每个 block 最早的开始时间,和最晚的结束时间相减,取得总运行时间
clock_t min_start, max_end;
min_start = time_use[0];
max_end = time_use[block_num];
for (int i = 1; i < block_num; i++)
{
if (min_start > time_use[i]) min_start = time_use[i];
if (max_end < time_use[i + block_num]) max_end = time_use[i + block_num];
}
//核函数运行时间
clock_t final_time = max_end - min_start;
printf("gputime: %d\n", final_time);
return 0;
}