Question

我试图比较并行添加向量的速度比非并行添加的速度快。为了使并行添加更快，我需要大数字。当我的N是10000时，我仍然可以运行它。但是，当我的N是100000时，我得到了

basicCuda.exe中0x00D25B89处的未处理异常：0xC00000FD：堆栈溢出（参数：0x00000000,0x002F2000）

如何解决此问题？

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
#include <stdio.h>
#include <time.h>
#include <chrono>
cudaError_t addWithCuda(int *c, const int *a, const int *b, uint64_t N);

__global__ void addKernel(int *c, const int *a, const int *b, uint64_t N)
{
    int i = threadIdx.x + blockIdx.x * blockDim.x;
    if (i < N) {
        c[i] = a[i] * b[i];
    }
}


void randomizeArr(int arr[],  int size) {

    unsigned int randNum;
    srand(time(NULL));
    for (int i = 0; i < size; i++) {
        randNum = rand() % 100 + 1;
        arr[i] = randNum;
    }
}

void addWithCPU(int c[], int a[], int b[], int size) {
    for (int i = 0; i < size; i++) {
        c[i] = a[i] + b[i];
    }
}

#define N (10000) // Number of elements each array has
#define M 1024 // 512 Threads Per Block
int main()
{
    const uint64_t arSize = N;
    int a[arSize] = { 0 };
    int b[arSize] = { 0 };
    randomizeArr(a, arSize);
    randomizeArr(b, arSize);
    int c[arSize] = { 0 };
    int d[arSize] = { 0 };

    // Add vectors in parallel.
    int iterations = 100;
    cudaError cudaStatus;
    auto begin = std::chrono::high_resolution_clock::now();
    for (uint32_t i = 0; i < iterations; ++i)
    {
        cudaStatus = addWithCuda(c, a, b, arSize);
    }
    auto end = std::chrono::high_resolution_clock::now();
    auto duration = std::chrono::duration_cast<std::chrono::nanoseconds>(end - begin).count();
    std::cout << "Parallel : " << duration / iterations << "ns." << std::endl;

    // Add vectors NOT in parallel.
    auto begin2 = std::chrono::high_resolution_clock::now();
    for (uint32_t i = 0; i < iterations; ++i)
    {
        addWithCPU(d, a, b, arSize);
    }
    auto end2 = std::chrono::high_resolution_clock::now();
    auto duration2 = std::chrono::duration_cast<std::chrono::nanoseconds>(end2 - begin2).count();
    std::cout << "Not Parallel : " << duration2 / iterations << "ns." << std::endl;


    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "addWithCuda failed!");
        std::getchar();
        return 1;
    }


    // cudaDeviceReset must be called before exiting in order for profiling and
    // tracing tools such as Nsight and Visual Profiler to show complete traces.
    cudaStatus = cudaDeviceReset();
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaDeviceReset failed!");
        return 1;
    }
    std::getchar();

    return 0;
}

// Helper function for using CUDA to add vectors in parallel.
cudaError_t addWithCuda(int *c, const int *a, const int *b, uint64_t size)
{
    int *dev_a = 0;
    int *dev_b = 0;
    int *dev_c = 0;
    cudaError_t cudaStatus;

    // Choose which GPU to run on, change this on a multi-GPU system.
    cudaStatus = cudaSetDevice(0);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");
        goto Error;
    }

    // Allocate GPU buffers for three vectors (two input, one output)    .
    cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    }

    cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    }

    cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    }

    // Copy input vectors from host memory to GPU buffers.
    cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    }

    cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    }

    // Launch a kernel on the GPU with one thread for each element.
    addKernel<<<(N + M - 1)/ M, M>>>(dev_c, dev_a, dev_b, N);

    // Check for any errors launching the kernel
    cudaStatus = cudaGetLastError();
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
        goto Error;
    }

    // cudaDeviceSynchronize waits for the kernel to finish, and returns
    // any errors encountered during the launch.
    cudaStatus = cudaDeviceSynchronize();
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
        goto Error;
    }

    // Copy output vector from GPU buffer to host memory.
    cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
    if (cudaStatus != cudaSuccess) {
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    }

Error:
    cudaFree(dev_c);
    cudaFree(dev_a);
    cudaFree(dev_b);

    return cudaStatus;
}

Answer 1

所有阵列：

int a[arSize]
int b[arSize]
int c[arSize]
int d[arSize]

在函数＆＃34; main＆＃34;中的堆栈上创建。给定arSize = 100000和sizeof（int）= 4，您要求分配1600000字节（1.5 MB），这可能需要使用编译器或OS参数进行调整以允许堆栈很大。

相反，您可以使用new分配内存：

int* a = new int[arSize]();

（请注意，所有数组值都将初始化为0，另请参阅c++ initial value of dynamic array）

CUDA编程未处理的异常和堆栈溢出

1 个答案: