CUDA 事件 可以为主机代码和设备代码计时。
基本的语法
// 定义事件变量
cudaEvent_t start, stop;
// 初始化
cudaEventCreate(&start);
cudaEventCreate(&stop);
// 记录代表时间开始的事件,注意不是地址
cudaEventRecord(start);
// 在TCC的驱动下可以省略,在WDDM驱动模式下必须保留,所以默认保留
// 不可以使用错误检测函数,默认返回值是错误的
cudaEventQuery(start);
/**
* code
**/
// 记录代表时间结束的事件
cudaEventRecord(stop);
// 事件同步函数,等待事件记录结束
cudaEventSynchronize(stop);
// 计算时间差
float elapsed_time;
cudaEventElapsedTime(&elapsed_time, start, stop);
// 销毁变量
cudaEventDestroy(start);
cudaEventDestroy(stop);
使用示例:
计算两个数组的和
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <iostream>
#define NUM_REPEATS 10
static void CheckCudaErrorAux(const char*, unsigned, const char*, cudaError_t);
#define CUDA_CHECK_RETURN(value) CheckCudaErrorAux(__FILE__,__LINE__, #value, value)
// 设备函数
__device__ float add(const float x, const float y)
{
return x + y;
}
__global__ void addFromGPU(float* A, float* B, float* C, const int N)
{
int blockId = blockIdx.x;
int id = blockId * blockDim.x + threadIdx.x;
if (id >= N)
{
return;
}
C[id] = add(A[id], B[id]);
}
void initialData(float* addr, int nCount)
{
for (size_t i = 0; i < nCount; i++)
{
addr[i] = (float)(rand() & 0xFFF) / 100.f;
}
}
int main()
{
int iElemntCount = 4096*10;
size_t stBytesCount = iElemntCount * sizeof(float); // 字节数
// 分配主机内存和设备内存并初始化
float* fpHost_A = new float[iElemntCount];
float* fpHost_B = new float[iElemntCount];
float* fpHost_C = new float[iElemntCount];
memset(fpHost_A, 0, stBytesCount);
memset(fpHost_B, 0, stBytesCount);
memset(fpHost_C, 0, stBytesCount);
float* fpDevice_A, * fpDevice_B, * fpDevice_C;
CUDA_CHECK_RETURN(cudaMalloc((void**)&fpDevice_A, stBytesCount));
CUDA_CHECK_RETURN(cudaMalloc((void**)&fpDevice_B, stBytesCount));
CUDA_CHECK_RETURN(cudaMalloc((void**)&fpDevice_C, stBytesCount));
CUDA_CHECK_RETURN(cudaMemset(fpDevice_C, 0,stBytesCount));
srand(666);
initialData(fpHost_A, iElemntCount);
initialData(fpHost_B, iElemntCount);
CUDA_CHECK_RETURN(cudaMemcpy(fpDevice_A, fpHost_A, stBytesCount, cudaMemcpyHostToDevice));
CUDA_CHECK_RETURN(cudaMemcpy(fpDevice_B, fpHost_B, stBytesCount, cudaMemcpyHostToDevice));
dim3 block(32);
dim3 grid((iElemntCount + block.x - 1)/ block.x);
cudaEvent_t start, stop;
for (int i = 0; i < NUM_REPEATS; i++)
{
CUDA_CHECK_RETURN(cudaEventCreate(&start));
CUDA_CHECK_RETURN(cudaEventCreate(&stop));
CUDA_CHECK_RETURN(cudaEventRecord(start));
cudaEventQuery(start);
addFromGPU <<<grid, block >>> (fpDevice_A, fpDevice_B, fpDevice_C, iElemntCount);
CUDA_CHECK_RETURN(cudaEventRecord(stop));
CUDA_CHECK_RETURN(cudaEventSynchronize(stop));
float elapsed_time = 0.0f;
CUDA_CHECK_RETURN(cudaEventElapsedTime(&elapsed_time, start, stop));
CUDA_CHECK_RETURN(cudaEventDestroy(start));
CUDA_CHECK_RETURN(cudaEventDestroy(stop));
printf("%d \t elapsed_time = %.2f \n", i, elapsed_time);
//CUDA_CHECK_RETURN(cudaMemcpy(fpHost_C, fpDevice_C, stBytesCount, cudaMemcpyDeviceToHost));
//for (size_t j = 0; j < iElemntCount; j++)
//{
// printf("%.2f + %.2f = %.2f \n", fpHost_A[j], fpHost_B[j], fpHost_C[j]);
//}
}
delete[]fpHost_A;
delete[]fpHost_B;
delete[]fpHost_C;
fpHost_A = nullptr;
fpHost_B = nullptr;
fpHost_C = nullptr;
return 0;
}
static void CheckCudaErrorAux(const char* file, unsigned line, const char* statement, cudaError_t err)
{
if (err == cudaSuccess)
return;
std::cerr << statement << " returned: "<< cudaGetErrorName(err) << " \t : " << cudaGetErrorString(err) << "(" << err << ") at " << file << ":" << line << std::endl;
exit(1);
}
结果:
可以看出第一次调用的时候最费时