（5）cuda中的grid、block

概要

在CUDA中，host和device是两个重要的概念，我们用host指代CPU及其内存，而用device指代GPU及其内存。
一般的CUDA程序的执行流程如下：

1. 分配host内存，并进行数据初始化；
2. 分配device内存，并从host将数据拷贝到device上；
3. 调用CUDA的核函数在device上完成指定的运算；
4. 将device上的运算结果拷贝到host上；
5. 释放device和host上分配的内存。

整体架构流程

一般来说：
一个kernel对应一个grid
一个grid可以有多个block，一维~三维
一个block可以有多个thread，一维~三维
我们写的kernel function运行在block中的每个thread中。

https://cuda-programming.blogspot.com/2013/01/thread-and-block-heuristics-in-cuda.html

#include 
#include 

//核函数 打印线程索引
__global__ void print_idx(){
    printf("block idx: (%3d, %3d, %3d), thread idx: (%3d, %3d, %3d)\n",
         blockIdx.z, blockIdx.y, blockIdx.x,
         threadIdx.z, threadIdx.y, threadIdx.x);
}

void demo_print(){
    int inputSize = 8;
    int blockDim = 4;  // block的维度 即 block中的线程数量
    int gridDim = inputSize / blockDim; // 计算出需要2个block，所以grid的维度为2

    dim3 block(blockDim);
    dim3 grid(gridDim);

    print_idx<<<grid, block>>>();
	//cudaDeviceSynchroize()来强制性的让kernel函数的结果执行结
	//束之后host再执行下一步。
    cudaDeviceSynchronize();
}

int main() {
    demo_print();
    return 0;
}

打印grid和block的维度

__global__ void print_dim(){
    printf("grid dimension: (%3d, %3d, %3d), block dimension: (%3d, %3d, %3d)\n",
         gridDim.z, gridDim.y, gridDim.x,
         blockDim.z, blockDim.y, blockDim.x);
}

计算每个线程在block中的索引

__global__ void print_thread_idx_per_block(){
    int index = threadIdx.z * blockDim.x * blockDim.y + \
              threadIdx.y * blockDim.x + \
              threadIdx.x;

    printf("block idx: (%3d, %3d, %3d), thread idx: %3d\n",
         blockIdx.z, blockIdx.y, blockIdx.x,
         index);
}

计算每个线程在grid中的索引

__global__ void print_thread_idx_per_grid(){
    int block_Size  = blockDim.z * blockDim.y * blockDim.x;

    int block_Index = blockIdx.z * gridDim.x * gridDim.y + \
               blockIdx.y * gridDim.x + \
               blockIdx.x;

    int thread_Index = threadIdx.z * blockDim.x * blockDim.y + \
               threadIdx.y * blockDim.x + \
               threadIdx.x;

    int thread_index_in_grid  = block_Index * block_Size + thread_Index;

    printf("block idx: %3d, thread idx in block: %3d, thread index in grid: %3d\n", 
         block_Index, thread_Index, thread_index_in_grid);
}

完整代码与输出

#include 
#include 
#include 

//核函数 打印线程索引
__global__ void print_idx(){
    printf("block idx: (%3d, %3d, %3d), thread idx: (%3d, %3d, %3d)\n",
         blockIdx.z, blockIdx.y, blockIdx.x,
         threadIdx.z, threadIdx.y, threadIdx.x);
}
//核函数 打印grid和block的维度
__global__ void print_dim(){
    printf("grid dimension: (%3d, %3d, %3d), block dimension: (%3d, %3d, %3d)\n",
         gridDim.z, gridDim.y, gridDim.x,
         blockDim.z, blockDim.y, blockDim.x);
}
//核函数 计算每个线程在block中的索引。GPU遍历顺序为Z，Y，X，所以计算的如下：
__global__ void print_thread_idx_per_block(){
    int index = threadIdx.z * blockDim.x * blockDim.y + \
              threadIdx.y * blockDim.x + \
              threadIdx.x;

    printf("block idx: (%3d, %3d, %3d), thread idx: %3d\n",
         blockIdx.z, blockIdx.y, blockIdx.x,
         index);
}

//核函数 计算每个线程在grid中的索引。GPU遍历顺序为Z，Y，X：
__global__ void print_thread_idx_per_grid(){
    int block_Size  = blockDim.z * blockDim.y * blockDim.x;

    int block_Index = blockIdx.z * gridDim.x * gridDim.y + \
               blockIdx.y * gridDim.x + \
               blockIdx.x;

    int thread_Index = threadIdx.z * blockDim.x * blockDim.y + \
               threadIdx.y * blockDim.x + \
               threadIdx.x;

    int thread_index_in_grid  = block_Index * block_Size + thread_Index;

    printf("block idx: %3d, thread idx in block: %3d, thread index in grid: %3d\n", 
         block_Index, thread_Index, thread_index_in_grid);
}



void demo_print(){
    int inputSize = 8;
    int blockDim = 4;  // block的维度 即 block中的线程数量
    int gridDim = inputSize / blockDim; // 计算出需要2个block，所以grid的维度为2

    dim3 block(blockDim);
    dim3 grid(gridDim);

    print_idx<<<grid, block>>>();
    //cudaDeviceSynchroize()来强制性的让kernel函数的结果执行结
	//束之后host再执行下一步。
    cudaDeviceSynchronize();
    std::cout << "---------------分割线---------------------------" << std::endl;
    print_dim<<<grid, block>>>();
    cudaDeviceSynchronize();
    std::cout << "---------------分割线---------------------------" << std::endl;
    print_thread_idx_per_block<<<grid, block>>>();
    cudaDeviceSynchronize();
    std::cout << "---------------分割线---------------------------" << std::endl;
    print_thread_idx_per_grid<<<grid, block>>>();
    cudaDeviceSynchronize();

}

int main() {
    demo_print();
    return 0;
}

cmake_minimum_required(VERSION 3.10)

project(test CUDA)
set(CMAKE_CUDA_STANDARD 20)

add_executable(test1 print_index_demo1.cu)

输出gpu信息

#include 
#include 
#include 
#include 


int main(){
    int count;
    int index = 0;
    cudaGetDeviceCount(&count);
    while (index < count) {
        cudaSetDevice(index);
        cudaDeviceProp prop;
        cudaGetDeviceProperties(&prop, index);
        std::cout<<"*********************Architecture related**********************"<<std::endl;
        std::cout<<"Device id: " << index<<std::endl;
        std::cout<<"Device name: " << prop.name<<std::endl;
        std::cout<<"Device compute capability: "<<prop.major + (float)prop.minor / 10<<std::endl;
        std::cout<<"GPU global meory size: "<<(float)prop.totalGlobalMem / (1<<30) << "GB"<<std::endl;;
        std::cout<<"L2 cache size: "<<(float)prop.l2CacheSize / (1<<20) << "MB"<<std::endl;;
        std::cout<<"Shared memory per block: "<<(float)prop.sharedMemPerBlock / (1<<10) << "KB"<<std::endl;;
        std::cout<<"Shared memory per SM: "<<(float)prop.sharedMemPerMultiprocessor / (1<<10)<< "KB"<<std::endl;;
        std::cout<<"Device clock rate: "<<prop.clockRate*1E-6<< "GHz"<<std::endl;;
        std::cout<<"Device memory clock rate: "<<prop.memoryClockRate*1E-6<< "Ghz"<<std::endl;;
        std::cout<<"Number of SM: "<<prop.multiProcessorCount<<std::endl;
        std::cout<<"Warp size: "<<prop.warpSize<<std::endl;

        std::cout<<"*********************Parameter related************************"<<std::endl;;
        std::cout<<"Max block numbers: "<< prop.maxBlocksPerMultiProcessor<<std::endl;
        std::cout<<"Max threads per block: "<<prop.maxThreadsPerBlock<<std::endl;
        std::cout<<"Max block dimension size:"<<prop.maxThreadsDim[0]<<" "<< prop.maxThreadsDim[1]<<" "<< prop.maxThreadsDim[2]<<std::endl;
        std::cout<<"Max grid dimension size: "<<prop.maxGridSize[0]<<" "<< prop.maxGridSize[1]<<" "<< prop.maxGridSize[2]<<std::endl;
        index ++;
        printf("\n");
    }
    return 0;
}