CUDA计算实列：浅析n(n等于1或大于1)维数组计算方法与grid、block、thread之间关系(上篇)

// 一个维度的计算方法
__global__ void one_vector_add(float* a_device, float* b_device, float* c_device) {
    
    int tid = threadIdx.y+blockDim.x*threadIdx.x;
    
    c_device[tid] = a_device[tid] + b_device[tid];

}


// 一维数组相加

void OneDim_add() {

    const int length = 4;                                      // 数组长度为16
    float a[length], b[length], c[length];                      // host中的数组

    // 变量初始化赋值
    for (int i = 0; i < length; i++) {
        a[i] = i + 1;
        b[i] = a[i] * 10;

    }

    // 变量初始化打印
    cout << "a=" << ‘\t‘;
    for (int i = 0; i < length; i++) { cout << a[i] << ‘\t‘; }
    cout << ‘\n‘ << "b=" << ‘\t‘;
    for (int i = 0; i < length; i++) { cout << b[i] << ‘\t‘; }


    //构造cuda指针变量并分配内存
    float *a_device, *b_device, *c_device;                      // device中的数组
    cudaSetDevice(0);  // 设置cuda设备
    cudaMalloc((void**)&a_device, length * sizeof(float));
    cudaMalloc((void**)&b_device, length * sizeof(float));
    cudaMalloc((void**)&c_device, length * sizeof(float));
    // 将host数组的值拷贝给device数组
    cudaMemcpy(a_device, a, length * sizeof(float), cudaMemcpyHostToDevice);    
    cudaMemcpy(b_device, b, length * sizeof(float), cudaMemcpyHostToDevice);
    cout << ‘\n‘ << "length：" << length << "\t分配大小：" << length * sizeof(float) << endl; 
    // 配置grid,block参数
    dim3 grid(1, 1, 1), block(1,length , 1);                    // 设置grid block 参数
    // 重点：调用kernel函数
    
    one_vector_add <<<grid, block >>> (a_device, b_device, c_device);           // 启动kernel
    
    // 将gpu上得到结果值返回host变量                                                                                  
    cudaMemcpy(c, c_device, length * sizeof(float), cudaMemcpyDeviceToHost);    // 将结果拷贝到host
    cout << ‘\n‘ << "c=" << ‘\t‘;
    for (int i = 0; i < length; i++) { cout << c[i] << ‘\t‘; }  //打印cuda计算的值




}





//block-thread 1D-3D
__global__ void TwoBlock1Thread2Way1(float *c, const float *a, const float *b)
{
    int i = threadIdx.x+threadIdx.y*blockDim.x + threadIdx.z*blockDim.x*blockDim.y; //3d 也适用2D
    c[i] = b[i] + a[i];
}



//block-thread 2D-3D
__global__ void TwoBlock2Thread3Way1(float *c, const float *a, const float *b)
{
    int thread2d = threadIdx.x + threadIdx.y*blockDim.x;
    int block2d = blockIdx.x+blockIdx.y*gridDim.x; // block是2D形式，但同样适用block为1D形式(blockIdx.x)
    int i = thread2d + (blockDim.x*blockDim.y)*block2d;
    
    c[i] = b[i] + a[i];
}





void TwoDimway1_add() {
    //将二维矩阵化为一维数组,使用一维数组进行block分配
    const int row = 4;
    const int col = 6;
    const int length = row*col;                                      // 数组长度为16
    float a[length], b[length], c[length];                      // host中的数组

    // 变量初始化赋值
    for (int i = 0; i < length; i++) {
        a[i] = i + 1;
        b[i] = a[i] * 100;
    }

    // 变量初始化打印
    cout << "变量a=" <<endl;
    for (int i = 0; i < row; i++) { 
        for (int i = 0; i < col; i++) {
        cout<< ‘\t‘ << a[i] ; 
        }
        cout << endl;
    }
    
    cout << "变量b=" << endl;
    for (int i = 0; i < row; i++) {
        for (int i = 0; i < col; i++) {
            cout << ‘\t‘ << b[i];
        }
        cout << endl;
    }


    //构造cuda指针变量并分配内存
    float *a_device, *b_device, *c_device;                      // device中的数组
    cudaSetDevice(0);  // 设置cuda设备
    cudaMalloc((void**)&a_device, length * sizeof(float));
    cudaMalloc((void**)&b_device, length * sizeof(float));
    cudaMalloc((void**)&c_device, length * sizeof(float));
    // 将host数组的值拷贝给device数组
    cudaMemcpy(a_device, a, length * sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(b_device, b, length * sizeof(float), cudaMemcpyHostToDevice);
    
    // 配置grid,block参数
    //dim3 grid(1, 1, 1), block(row, col/2, col / 2);                    // 设置block为3D参数
    //TwoBlock1Thread2Way1 <<<grid, block >>> (c_device,a_device, b_device );  // 重点：调用kernel函数
    //dim3 grid(1, 1, 1), block(row, col , 1);                    // 设置block为2D参数                                           
    //TwoBlock1Thread2Way1 << <grid, block >> > (c_device, a_device, b_device);// 重点：调用kernel函数
    
    dim3 grid(2, 1, 1), block(row/2, col, 1);                    // 设置block为2D参数                                           
    TwoBlock2Thread3Way1 << <grid, block >> > (c_device, a_device, b_device);// 重点：调用kernel函数

    // 将gpu上得到结果值返回host变量                                                                                  
    cudaMemcpy(c, c_device, length * sizeof(float), cudaMemcpyDeviceToHost);    // 将结果拷贝到host
    
    
    cout << "计算结果 c=" << endl;
    for (int i = 0; i < row; i++) {
        for (int i = 0; i < col; i++) {
            cout << ‘\t‘ << c[i];
        }
        cout << endl;
    }
    


}




int main() {
    
    cout << "\n一维数组cuda计算结果：\n" << endl;
    OneDim_add();
    //TwoDim_add();
    
    cout << "\n\n二维数组一维方式cuda计算结果：\n\n" << endl;
    TwoDimway1_add();



    return 0;
}