CUDA Events and Streams

Explanation Video Link on Youtube

B站中文解说视频链接

!apt-get --purge remove cuda nvidia* libnvidia-*
!dpkg -l | grep cuda- | awk '{print $2}' | xargs -n1 dpkg --purge
!apt-get remove cuda-*
!apt autoremove
!apt-get update

!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
!apt-get update
!apt-get install cuda-9.2

!pip install git+git://github.com/andreinechaev/nvcc4jupyter.git
%load_ext nvcc_plugin

Use CUDA Events to profiling GPU run-time performance:

%%cu
#include <stdio.h>
#define N (1024)
#define FULL_DATA_SIZE (N*500)
__global__ void add( int *a, int *b, int *c ) {
    int tid = threadIdx.x + blockIdx.x * blockDim.x;
    c[tid] = a[tid] + b[tid];
}
int main(void) {
  cudaEvent_t start, stop;
  float elapsedTime;
  cudaEventCreate(&start);
  cudaEventCreate(&stop);
  cudaEventRecord(start, 0);
  int host_a[FULL_DATA_SIZE], host_b[FULL_DATA_SIZE], host_c[FULL_DATA_SIZE];
  int *dev_a, *dev_b, *dev_c;
  cudaMalloc((void**)&dev_a, N * sizeof(int));
  cudaMalloc((void**)&dev_b, N * sizeof(int));
  cudaMalloc((void**)&dev_c, N * sizeof(int));

  for (int i = 0; i < FULL_DATA_SIZE; i++) {
    host_a[i] = rand();
    host_b[i] = rand();
  }

  for (int i = 0; i < FULL_DATA_SIZE; i+=N) {
    cudaMemcpy(dev_a, host_a + i, N * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(dev_b, host_b + i, N * sizeof(int), cudaMemcpyHostToDevice);
    add<<<256,256>>>(dev_a, dev_b, dev_c);
    cudaMemcpy(host_c + i, dev_c, N * sizeof(int), cudaMemcpyDeviceToHost);
  }

  cudaEventRecord(stop, 0);
  cudaEventSynchronize(stop);
  cudaEventElapsedTime(&elapsedTime, start, stop);
  printf("Time taken: %3.1f ms\n", elapsedTime);
  cudaFree(dev_a);
  cudaFree(dev_b);
  cudaFree(dev_c);
  return 0;
}

Use two CUDA Streams to improve above application:

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