Event是CUDA中的事件，用于分析、检测CUDA程序中的错误。一般我们会定义一个宏：#pragma once
include
define CHECK(call) \
do \
{ \
const cudaError_t error_code = call; \
if (error_code != cudaSuccess) \
{ \
printf("CUDA Error:\n"); \
printf(" File: %s\n", __FILE__); \
printf(" Line: %d\n", __LINE__); \
printf(" Error code: %d\n", error_code); \
printf(" Error text: %s\n", \
cudaGetErrorString(error_code)); \
exit(1); \
} \} while (0)并在适当的位置使用这个宏来打印CUDA的错误日志。#pragma once, 不要放在源代码文件里，这个一般只放在头文件里的。（防止头文件被引入多次）Event的调用有以下内容：
具体的顺序如下：（1）声明Event（这里以计算核函数运行时间前后的start Event和stop Event为例）cudaEvent_t start, stop;（2）创建EventCHECK(cudaEventCreate(&start));
CHECK(cudaEventCreate(&stop));（3）添加Event（在合适的地方）cudaEventRecord(start);
cudaEventRecord(stop);（4）等待Event完成（a）非堵塞方式——可以用于一些不需要等待的处理cudaEventQuery(start);（b）堵塞方式——可以用于执行核函数后等待核函数执行完毕后的处理cudaEventSynchronize(stop);（5）计算两个Event间隔时间CHECK(cudaEventElapsedTime(&elapsed_time, start, stop));（6）销毁EventCHECK(cudaEventDestroy(start));
CHECK(cudaEventDestroy(stop));以上次介绍的矩阵乘为例，完整的代码如下：#pragma once
include
define CHECK(call) \
do \
{ \
const cudaError_t error_code = call; \
if (error_code != cudaSuccess) \
{ \
printf("CUDA Error:\n"); \
printf(" File: %s\n", __FILE__); \
printf(" Line: %d\n", __LINE__); \
printf(" Error code: %d\n", error_code); \
printf(" Error text: %s\n", \
cudaGetErrorString(error_code)); \
exit(1); \
} \} while (0)
include
include
include "error.cuh"
define BLOCK_SIZE 32
global void gpu_matrix_mult(int a,int b, int *c, int m, int n, int k)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int sum = 0;
if( col < k && row < m)
{
for(int i = 0; i < n; i++)
{
sum += a[row * n + i] * b[i * k + col];
}
c[row * k + col] = sum;
}}
void cpu_matrix_mult(int h_a, int h_b, int *h_result, int m, int n, int k) {
for (int i = 0; i < m; ++i)
{
for (int j = 0; j < k; ++j)
{
int tmp = 0.0;
for (int h = 0; h < n; ++h)
{
tmp += h_a[i * n + h] * h_b[h * k + j];
}
h_result[i * k + j] = tmp;
}
}}
int main(int argc, char const *argv[])
{
int m=100;
int n=100;
int k=100;
//声明Event
cudaEvent_t start, stop, stop2, stop3 , stop4 ;
//创建Event
CHECK(cudaEventCreate(&start));
CHECK(cudaEventCreate(&stop));
CHECK(cudaEventCreate(&stop2));
int *h_a, *h_b, *h_c, *h_cc;
CHECK(cudaMallocHost((void **) &h_a, sizeof(int)*m*n));
CHECK(cudaMallocHost((void **) &h_b, sizeof(int)*n*k));
CHECK(cudaMallocHost((void **) &h_c, sizeof(int)*m*k));
CHECK(cudaMallocHost((void **) &h_cc, sizeof(int)*m*k));
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
h_a[i * n + j] = rand() % 1024;
}
}
for (int i = 0; i < n; ++i) {
for (int j = 0; j < k; ++j) {
h_b[i * k + j] = rand() % 1024;
}
}
int *d_a, *d_b, *d_c;
CHECK(cudaMalloc((void **) &d_a, sizeof(int)*m*n));
CHECK(cudaMalloc((void **) &d_b, sizeof(int)*n*k));
CHECK(cudaMalloc((void **) &d_c, sizeof(int)*m*k));
// copy matrix A and B from host to device memory
CHECK(cudaMemcpy(d_a, h_a, sizeof(int)*m*n, cudaMemcpyHostToDevice));
CHECK(cudaMemcpy(d_b, h_b, sizeof(int)*n*k, cudaMemcpyHostToDevice));
unsigned int grid_rows = (m + BLOCK_SIZE - 1) / BLOCK_SIZE;
unsigned int grid_cols = (k + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid(grid_cols, grid_rows);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
//开始start Event
cudaEventRecord(start);
//非阻塞模式
cudaEventQuery(start);
//gpu_matrix_mult<<>>(d_a, d_b, d_c, m, n, k);
gpu_matrix_mult_shared<<>>(d_a, d_b, d_c, m, n, k);
//开始stop Event
cudaEventRecord(stop);
//由于要等待核函数执行完毕，所以选择阻塞模式
cudaEventSynchronize(stop);
//计算时间 stop-start
float elapsed_time;
CHECK(cudaEventElapsedTime(&elapsed_time, start, stop));
printf("start-》stop：Time = %g ms.\n", elapsed_time);
cudaMemcpy(h_c, d_c, (sizeof(int)*m*k), cudaMemcpyDeviceToHost);
//cudaThreadSynchronize();
//开始stop2 Event
CHECK(cudaEventRecord(stop2));
//非阻塞模式
//CHECK(cudaEventSynchronize(stop2));
cudaEventQuery(stop2);
//计算时间 stop-stop2
float elapsed_time2;
cudaEventElapsedTime(&elapsed_time2, stop, stop2);
printf("stop-》stop2：Time = %g ms.\n", elapsed_time2);
//销毁Event
CHECK(cudaEventDestroy(start));
CHECK(cudaEventDestroy(stop));
CHECK(cudaEventDestroy(stop2));
//CPU函数计算
cpu_matrix_mult(h_a, h_b, h_cc, m, n, k);
int ok = 1;
for (int i = 0; i < m; ++i)
{
for (int j = 0; j < k; ++j)
{
if(fabs(h_cc[i*k + j] - h_c[i*k + j])>(1.0e-10))
{
ok = 0;
}
}
}
if(ok)
{
printf("Pass!!!\n");
}
else
{
printf("Error!!!\n");
}
// free memory
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
cudaFreeHost(h_a);
cudaFreeHost(h_b);
cudaFreeHost(h_c);
return 0; }在Quardo P1000的GPU上执行：
这里以矩阵乘为例，打印了调用矩阵乘核函数的时间，以及后面 cudaMemcpy的时间。我们强行将 CHECK(cudaMemcpy(d_b, h_b, sizeof(int)nk, cudaMemcpyHostToDevice)); 改为 CHECK(cudaMemcpy(d_b, h_b, sizeof(int)nk*2, cudaMemcpyHostToDevice)); 故意让其出界。再重新编译，运行，看看效果：
系统会告诉你 这行有错：
这样就可以跟踪出CUDA调用中的错误。这里需要总结一下张小白在调试CHECK过程中发现的几个问题：（1）如果没有 CHECK(cudaEventCreate()) 就直接调用 cudaEventRecord() 或者执行后面的Event函数，会导致打印不了信息。张小白当时对于stop2这个event就犯了这个错，导致 stop->stop2的时间怎么都打不出来。（2）对于 cudaEventQuery() 是不能加 CHECK的，如果加了反而会报错：在上面的环境中，如果您这样写：CHECK(cudaEventQuery(stop2));编译执行就会出现以下错误：
cudaEventQuery的cudaErrorNotReady代表了事件还没发生（还没有被记录），不代表错误。