CUDA C编程权威指南：2.1-CUDA编程模型

  本文主要通过例子介绍了CUDA异构编程模型，需要说明的是Grid、Block和Thread都是逻辑结构，不是物理结构。实现例子代码参考文献[2]，只需要把相应章节对应的CMakeLists.txt文件拷贝到CMake项目根目录下面即可运行。

1.Grid、Block和Thread间的关系
  GPU中最重要的2种内存是全局内存和共享内存，前者类似于CPU系统内存，而后者类似于CPU缓存，然后GPU共享内存可由CUDA C内核直接控制。GPU简化的内存结构，如下所示：
[外链图片转存中…(img-zjlJfwmi-1696733256161)]
  由一个内核启动所产生的所有thread统称为一个grid，同一个grid中的所有thread共享相同的全局内存空间。一个grid由多个block构成，一个block包含一组thread，同一block内的thread通过同步、共享内存方式进行线程协作，不同block内的thread不能协作。由block和grid构成的2层的thread层次结构，如下所示：
[外链图片转存中…(img-eGUKo819-1696733256163)]
  CUDA可以组织3维的grid和block。blockIdx表示线程块在线程格内的索引，threadIdx表示块内的线程索引；blockDim表示每个线程块中的线程数，gridDim表示网格中的线程块数。这些变量允许开发人员在编写CUDA代码时，从逻辑上管理和组织线程块和网格的大小，从而优化并行执行的效率。如下所示：
[外链图片转存中…(img-wcgjwV8R-1696733256164)]

2.检查网格和块的索引和维度(checkDimension.cu)
  确定grid和block的方法为先确定block的大小，然后根据实际数据大小和block大小的基础上计算grid维度，如下所示：

// 检查网格和块的索引和维度
# include <cuda_runtime.h>
# include <stdio.h>__global__ void checkIndex(void) {// gridDim表示grid的维度，blockDim表示block的维度，grid维度表示grid中block的数量，block维度表示block中thread的数量printf("threadIdx:(%d, %d, %d) blockIdx:(%d, %d, %d) blockDim:(%d, %d, %d) ""gridDim:(%d, %d, %d)\n", threadIdx.x, threadIdx.y, threadIdx.z,blockIdx.x, blockIdx.y, blockIdx.z, blockDim.x, blockDim.y, blockDim.z,gridDim.x, gridDim.y, gridDim.z); // printf函数只支持Fermi及以上版本的GPU架构，因此编译的时候需要加上-arch=sm_20编译器选项
}int main(int argc, char** argv) {// 定义全部数据元素int nElem = 6;// 定义grid和block的结构dim3 block(3);  // 表示一个block中有3个线程dim3 grid((nElem + block.x - 1) / block.x);  // 表示grid中有2个block// 检查grid和block的维度(host端)printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);printf("block.x %d block.y %d block.z %d\n", block.x, block.y, block.z);// 检查grid和block的维度(device端)checkIndex<<<grid, block>>>();// 离开之前重置设备cudaDeviceReset();return 0;
}

  输出结果如下所示：

threadIdx:(0, 0, 0) blockIdx:(1, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
threadIdx:(1, 0, 0) blockIdx:(1, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
threadIdx:(2, 0, 0) blockIdx:(1, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
threadIdx:(0, 0, 0) blockIdx:(0, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
threadIdx:(1, 0, 0) blockIdx:(0, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
threadIdx:(2, 0, 0) blockIdx:(0, 0, 0) blockDim:(3, 1, 1) gridDim:(2, 1, 1)
grid.x 2 grid.y 1 grid.z 1
block.x 3 block.y 1 block.z 1

3.在主机上定义网格和块的大小(defineGridBlock.cu)
  接下来通过一个1维网格和1维块讲解当block大小变化时，gird的size也随之变化，如下所示：

#include <cuda_runtime.h>
#include <stdio.h>int main(int argc, char** argv) {// 定义全部数据元素int cElem = 1024;// 定义grid和block结构dim3 block(1024);dim3 grid((cElem + block.x - 1) / block.x);printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);// 重置blockblock.x = 512;grid.x = (cElem + block.x - 1) / block.x;printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);// 重置blockblock.x = 256;grid.x = (cElem + block.x - 1) / block.x;printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);// 重置blockblock.x = 128;grid.x = (cElem + block.x - 1) / block.x;printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);// 离开前重置devicecudaDeviceReset();return 0;
}

  输出结果，如下所示：

grid.x 1 grid.y 1 grid.z 1
grid.x 2 grid.y 1 grid.z 1
grid.x 4 grid.y 1 grid.z 1
grid.x 8 grid.y 1 grid.z 1

4.基于GPU的向量加法(sumArraysOnGPU-small-case.cu)

#include <cuda_runtime.h>
#include <stdio.h>#define CHECK(call)
//{
//    const cudaError_t error = call;
//    if (error != cudaSuccess)
//    {
//        printf("Error: %s:%d, ", __FILE__, __LINE__);
//        printf("code:%d, reason: %s\n", error, cudaGetErrorString(error));
//        exit(1);
//    }
//}void checkResult(float *hostRef, float *gpuRef, const int N)
{double epsilon = 1.0E-8;bool match = 1;for (int i = 0; i < N; i++){if (abs(hostRef[i] - gpuRef[i]) > epsilon){match = 0;printf("Arrays do not match!\n");printf("host %5.2f gpu %5.2f at current %d\n", hostRef[i], gpuRef[i], i);break;}}if (match) printf("Arrays match.\n\n");
}void initialData(float *ip, int size)
{// generate different seed for random numbertime_t t;srand((unsigned int) time(&t));for (int i = 0; i < size; i++){ip[i] = (float) (rand() & 0xFF) / 10.0f;}
}void sumArraysOnHost(float *A, float *B, float *C, const int N)
{for (int idx = 0; idx < N; idx++){C[idx] = A[idx] + B[idx];}
}__global__ void sumArraysOnGPU(float *A, float *B, float *C)
{// int i = threadIdx.x;  // 获取线程索引int i = blockIdx.x * blockDim.x + threadIdx.x;  // 获取线程索引printf("threadIdx.x: %d, blockIdx.x: %d, blockDim.x: %d\n", threadIdx.x, blockIdx.x, blockDim.x);C[i] = A[i] + B[i];  // 计算
}int main(int argc, char** argv) {printf("%s Starting...\n", argv[0]);// 设置设备int dev = 0;cudaSetDevice(dev);// 设置vectors数据大小int nElem = 32;printf("Vector size %d\n", nElem);// 分配主机内存size_t nBytes = nElem * sizeof(float);float *h_A, *h_B, *hostRef, *gpuRef;  // 定义主机内存指针h_A = (float *) malloc(nBytes);  // 分配主机内存h_B = (float *) malloc(nBytes);  // 分配主机内存hostRef = (float *) malloc(nBytes);  // 分配主机内存，用于存储host端计算结果gpuRef = (float *) malloc(nBytes);  // 分配主机内存，用于存储device端计算结果// 初始化主机数据initialData(h_A, nElem);initialData(h_B, nElem);memset(hostRef, 0, nBytes);  // 将hostRef清零memset(gpuRef, 0, nBytes);  // 将gpuRef清零// 分配设备全局内存float *d_A, *d_B, *d_C;  // 定义设备内存指针cudaMalloc((float **) &d_A, nBytes);  // 分配设备内存cudaMalloc((float **) &d_B, nBytes);  // 分配设备内存cudaMalloc((float **) &d_C, nBytes);  // 分配设备内存// 从主机内存拷贝数据到设备内存cudaMemcpy(d_A, h_A, nBytes, cudaMemcpyHostToDevice);  // d_A表示目标地址，h_A表示源地址，nBytes表示拷贝字节数，cudaMemcpyHostToDevice表示拷贝方向cudaMemcpy(d_B, h_B, nBytes, cudaMemcpyHostToDevice);  // d_B表示目标地址，h_B表示源地址，nBytes表示拷贝字节数，cudaMemcpyHostToDevice表示拷贝方向// 在host端调用kerneldim3 block(nElem);  // 定义block维度dim3 grid(nElem / block.x);  // 定义grid维度sumArraysOnGPU<<<grid, block>>>(d_A, d_B, d_C);  // 调用kernel，<<<grid, block>>>表示执行配置，d_A, d_B, d_C表示kernel参数printf("Execution configuration <<<%d, %d>>>\n", grid.x, block.x);  // 打印执行配置// 拷贝device结果到host内存cudaMemcpy(gpuRef, d_C, nBytes, cudaMemcpyDeviceToHost);  // gpuRef表示目标地址，d_C表示源地址，nBytes表示拷贝字节数，cudaMemcpyDeviceToHost表示拷贝方向// 在host端计算结果sumArraysOnHost(h_A, h_B, hostRef, nElem);// 检查device结果checkResult(hostRef, gpuRef, nElem);// 释放设备内存cudaFree(d_A);cudaFree(d_B);cudaFree(d_C);// 释放主机内存free(h_A);free(h_B);free(hostRef);free(gpuRef);return 0;
}

  输出结果如下所示：
[外链图片转存中…(img-yLMkSnjk-1696733256164)]

threadIdx.x: 0, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 1, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 2, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 3, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 4, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 5, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 6, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 7, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 8, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 9, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 10, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 11, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 12, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 13, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 14, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 15, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 16, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 17, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 18, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 19, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 20, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 21, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 22, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 23, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 24, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 25, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 26, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 27, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 28, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 29, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 30, blockIdx.x: 0, blockDim.x: 32
threadIdx.x: 31, blockIdx.x: 0, blockDim.x: 32
L:\20200706_C++\C++Program\20231003_ClionProgram\cmake-build-debug\20231003_ClionProgram.exe Starting...
Vector size 32
Execution configuration <<<1, 32>>>
Arrays match.

5.其它知识点
（1）host和device同步
  核函数的调用和主机线程是异步的，即核函数调用结束后，控制权立即返回给主机端，可以调用cudaDeviceSynchronize(void)函数来强制主机端程序等待所有的核函数执行结束。当使用cudaMemcpy函数在host和device间拷贝数据时，host端隐式同步，即host端程序必须等待数据拷贝完成后才能继续执行程序。需要说明的是，所有CUDA核函数的启动都是异步的，当CUDA内核调用完成后，控制权立即返回给CPU。
（2）函数类型限定符
  函数类型限定符指定一个函数在host上执行还是在device上执行，以及可被host调用还是被device调用，函数类型限定符如下所示：
[外链图片转存中…(img-dj0ukz7N-1696733256165)]
  说明：__device__和__host__限定符可以一起使用，这样可同时在host和device端进行编译。

参考文献：
[1]《CUDA C编程权威指南》
[2]2.1-CUDA编程模型概述：https://github.com/ai408/nlp-engineering/tree/main/20230917_NLP工程化/20231004_高性能计算/20231003_CUDA编程/20231003_CUDA_C编程权威指南/2-CUDA编程模型/2.1-CUDA编程模型概述