add_parallel.cu

#include <iostream>
#include <math.h>
// Kernel function to add the elements of two arrays
// __global__ 变量声明符，作用是将add函数变成可以在GPU上运行的函数
// __global__ 函数被称为kernel，
// 在 GPU 上运行的代码通常称为设备代码（device code），而在 CPU 上运行的代码是主机代码（host code）。
__global__ 
void add(int n, float *x, float *y)
{
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  printf("index: %d, stride: %d\n", index, stride);
  for (int i = index; i < n; i+=stride)
    y[i] = x[i] + y[i];
}

int main(void)
{
  int N = 1<<20;
  float *x, *y;

  // Allocate Unified Memory – accessible from CPU or GPU
  // 内存分配，在GPU或者CPU上统一分配内存
  cudaMallocManaged(&x, N*sizeof(float));
  cudaMallocManaged(&y, N*sizeof(float));

  // initialize x and y arrays on the host
  for (int i = 0; i < N; i++) {
    x[i] = 1.0f;
    y[i] = 2.0f;
  }

  // Run kernel on 1M elements on the GPU
  // execution configuration, 执行配置
  int blockSize = 256;
  int numBlock = (N + blockSize - 1) / blockSize;
  add<<<numBlock, blockSize>>>(N, x, y);
  std::cout << "NumBlock: " << numBlock << ", blockSize: " << blockSize << std::endl;

  // Wait for GPU to finish before accessing on host
  // CPU需要等待cuda上的代码运行完毕，才能对数据进行读取
  cudaDeviceSynchronize();

  // Check for errors (all values should be 3.0f)
  float maxError = 0.0f;
  for (int i = 0; i < N; i++)
    maxError = fmax(maxError, fabs(y[i]-3.0f));
  std::cout << "Max error: " << maxError << std::endl;

  // Free memory
  cudaFree(x);
  cudaFree(y);
  
  return 0;
}