Windows11+VS2019+CUDA11.8配置过程

最近要处理大规模点云数据， 用CPU跑感觉有点慢，想通过GPU加速点云处理过程，于是想要学习CUDA编程。

很多教程提到在安装CUDA之前，需要打开cmd，输入nvidia-smi，查看显卡支持的CUDA版本。这个步骤我在安装CUDA之前没有做，我是直接安装了，但是建议查一下。事后，还是查了一下，发现直接写的是11.8

CUDA下载安装

CUDA官方安装教程：https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html

根据官方教程可以发现，VS2019和Win11是可以跟CUDA11.8配置的。

CUDA下载链接：https://developer.nvidia.com/cuda-downloads

安装过程一路默认，默认路径一般在C盘。

安装完毕之后，打开cmd，输入nvcc -V试一试看看能否查到CUDA版本，可以的话应该是没问题。

VS2019与CUDA配置

可以打开一个现有的VS项目，或者新建一个空项目。右键点击源文件，添加新建项就可以创建一个CUDA文件，后缀是.cu。若是创建CUDA头文件，后缀就是.cuh。debug为x64

然后，右键点击这个cu文件，选择属性，将项类型改成CUDA C++。

选择项目，点击右键–>生成依赖项–>自定义生成–>选择CUDA11.8

右键项目，找到CUDA C/C++ ——>Common，输入C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8

当然，也还是要配置包含目录和库目录，这个和VS配置其他库一样
包含目录：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include
库目录：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64

链接器——>输入——>附加依赖项

cublas.lib
cublasLt.lib
cuda.lib
cudadevrt.lib
cudart.lib
cudart_static.lib
cufft.lib
cufftw.lib
cufilt.lib
curand.lib
cusolver.lib
cusolverMg.lib
cusparse.lib
nppc.lib
nppial.lib
nppicc.lib
nppidei.lib
nppif.lib
nppig.lib
nppim.lib
nppist.lib
nppisu.lib
nppitc.lib
npps.lib
nvblas.lib
nvjpeg.lib
nvml.lib
nvptxcompiler_static.lib
nvrtc.lib
nvrtc_static.lib
nvrtc-builtins_static.lib
OpenCL.lib

完成配置之后，通常要先运行一个例程试一试看看效果。但是报错

未定义标识符“__syncthreads”

这篇博客中解释到这个情况加什么头文件都不行，我试了也发现确实是不行。但是好像可以编译运行。

另一个博主也是这么说，传送门

应输入表达式

这个好像也是可以忽略的，也是没找到什么办法。

测试代码

error.cuh文件

#pragma once
#include 

#define CHECK(call)                                   \
do                                                    \
{                                                     \
    const cudaError_t error_code = call;              \
    if (error_code != cudaSuccess)                    \
    {                                                 \
        printf("CUDA Error:\n");                      \
        printf("    File:       %s\n", __FILE__);     \
        printf("    Line:       %d\n", __LINE__);     \
        printf("    Error code: %d\n", error_code);   \
        printf("    Error text: %s\n",                \
            cudaGetErrorString(error_code));          \
        exit(1);                                      \
    }                                                 \
} while (0)

XXX.cu代码

#include 
#include 
#include "error.cuh"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "device_functions.h"
#include 



#define TILE_DIM 32   //Don't ask me why I don't set these two values to one
#define BLOCK_SIZE 32
#define N 3001 // for huanhuan, you know that!

__managed__ int input_M[N * N];      //input matrix & GPU result
int cpu_result[N * N];   //CPU result


//in-place matrix transpose
__global__ void ip_transpose(int* data)
{
    __shared__ int tile_s[TILE_DIM][TILE_DIM + 1];
    __shared__ int tile_d[TILE_DIM][TILE_DIM + 1];

    int x = blockIdx.x * TILE_DIM + threadIdx.x;
    int y = blockIdx.y * TILE_DIM + threadIdx.y;

    //Threads in the triangle below
    if (blockIdx.y > blockIdx.x) {
        int dx = blockIdx.y * TILE_DIM + threadIdx.x;
        int dy = blockIdx.x * TILE_DIM + threadIdx.y;
        if (x < N && y < N)
        {
            tile_s[threadIdx.y][threadIdx.x] = data[(y)*N + x];
        }
        if (dx < N && dy < N)
        {
            tile_d[threadIdx.y][threadIdx.x] = data[(dy)*N + dx];
        }

        __syncthreads();
        if (dx < N && dy < N)
        {
            data[(dy)*N + dx] = tile_s[threadIdx.x][threadIdx.y];
        }
        if (x < N && y < N)
        {
            data[(y)*N + x] = tile_d[threadIdx.x][threadIdx.y];
        }
    }
    else if (blockIdx.y == blockIdx.x)//Threads on the diagonal
    {
        if (x < N && y < N)
        {
            tile_s[threadIdx.y][threadIdx.x] = data[(y)*N + x];
        }
        __syncthreads();
        if (x < N && y < N)
        {
            data[(y)*N + x] = tile_s[threadIdx.x][threadIdx.y];
        }
    }
}

void cpu_transpose(int* A, int* B)
{
    for (int j = 0; j < N; j++)
    {
        for (int i = 0; i < N; i++)
        {
            B[i * N + j] = A[j * N + i];
        }
    }
}

int main(int argc, char const* argv[])
{

    cudaEvent_t start, stop_gpu;
    CHECK(cudaEventCreate(&start));
    CHECK(cudaEventCreate(&stop_gpu));


    for (int i = 0; i < N; ++i) {
        for (int j = 0; j < N; ++j) {
            input_M[i * N + j] = rand() % 1000;
        }
    }
    cpu_transpose(input_M, cpu_result);

    CHECK(cudaEventRecord(start));
    unsigned int grid_rows = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
    unsigned int grid_cols = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
    dim3 dimGrid(grid_cols, grid_rows);
    dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
    ip_transpose << <dimGrid, dimBlock >> > (input_M);
    CHECK(cudaDeviceSynchronize());
    CHECK(cudaEventRecord(stop_gpu));
    CHECK(cudaEventSynchronize(stop_gpu));

    float elapsed_time_gpu;
    CHECK(cudaEventElapsedTime(&elapsed_time_gpu, start, stop_gpu));
    printf("Time_GPU = %g ms.\n", elapsed_time_gpu);

    CHECK(cudaEventDestroy(start));
    CHECK(cudaEventDestroy(stop_gpu));

    int ok = 1;
    for (int i = 0; i < N; ++i)
    {
        for (int j = 0; j < N; ++j)
        {
            if (fabs(input_M[i * N + j] - cpu_result[i * N + j]) > (1.0e-10))
            {
                ok = 0;
            }
        }
    }


    if (ok)
    {
        printf("Pass!!!\n");
    }
    else
    {
        printf("Error!!!\n");
    }

    return 0;
}

结果

GPU信息读取代码

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

#include 

void myDeviceInfo();

int main()
{
    // mykernel <<<1, 10 >>> ();
    myDeviceInfo();
    cudaDeviceSynchronize();
    return 0;
}

void myDeviceInfo()
{
    int dev_count;
    cudaGetDeviceCount(&dev_count);
    cudaDeviceProp dev_prop;
    int i;
    for (i = 0; i < dev_count; i++) {
        cudaGetDeviceProperties(&dev_prop, i);
        printf("----------- Information of device %d -----------\n", i);
        printf("The streaming multiprocessor(SM) number is %d\n", dev_prop.multiProcessorCount);
        printf("The max thread block numberof per SM is %d\n", dev_prop.maxBlocksPerMultiProcessor);
        printf("The max threads number of per SM is %d\n", dev_prop.maxThreadsPerMultiProcessor);
        printf("The max threads number of per block is %d\n", dev_prop.maxThreadsPerBlock);
        printf("The max thread blocks number in (x, y, z) dim is (%d, %d, %d)\n", dev_prop.maxGridSize[0], dev_prop.maxGridSize[1], dev_prop.maxGridSize[2]);
        printf("The max threads number of (x, y, z) dim is (%d. %d, %d)\n", dev_prop.maxThreadsDim[0], dev_prop.maxThreadsDim[1], dev_prop.maxThreadsDim[2]);
        printf("----------- Information of device end -----------\n");
    }
}

参考

教程1
教程2
教程3
教程4
教程5