Question

我收到此错误

/usr/local/cuda-5.0/bin /../ include / host_config.h：82：2：错误：#error - 不受支持的GNU版本！不支持gcc 4.7及更高版本！ make： * [src / Throughput.o]错误1

在host_config.h中，他们确保兼容性高达4.6

#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 6)

#error -- unsupported GNU version! gcc 4.7 and up are not supported!

我有4.6和4.7

选举@ elect-desktop：/usr/local/cuda-5.0/bin$ gcc gcc
  gcc-4.7 gcc-nm-4.7 gcc-4.6 gcc-ar-4.7
  GCC-ranlib的-4.7

在互联网上查看他们建议在cuda bin目录中添加gcc-4.6的链接。

所以我做了

选举@ elect-desktop：/usr/local/cuda-5.0/bin$ sudo ln -s /usr/bin/gcc-4.6 gcc

我收到了另一个错误

**** Build of configuration Debug for project Throughput ****

make all 
Building file: ../src/Throughput.cu
Invoking: NVCC Compiler
nvcc -G -g -O0 -gencode arch=compute_20,code=sm_20 -odir "src" -M -o "src/Throughput.d"     "../src/Throughput.cu"
gcc: error trying to exec 'cc1plus': execvp: No such file or directory
make: *** [src/Throughput.o] Error 1

**** Build Finished ****

谷歌搜索再次没有带我一些明确的情况（gcc降级等）

所以我问这里现在的问题是什么，因为CUDA应该与gcc-4.6兼容......

我的系统：

Ubuntu 12.10 64b
cuda_5.0.35_linux_64_ubuntu11.10-1

这是我正在尝试编译的教程代码

/**
 * Copyright 1993-2012 NVIDIA Corporation.  All rights reserved.
 *
 * Please refer to the NVIDIA end user license agreement (EULA) associated
 * with this source code for terms and conditions that govern your use of
 * this software. Any use, reproduction, disclosure, or distribution of
 * this software and related documentation outside the terms of the EULA
 * is strictly prohibited.
 */
#include <stdio.h>
#include <stdlib.h>

static const int WORK_SIZE = 256;

/**
 * This macro checks return value of the CUDA runtime call and exits
 * the application if the call failed.
 */
#define CUDA_CHECK_RETURN(value) {                                          \
    cudaError_t _m_cudaStat = value;                                        \
    if (_m_cudaStat != cudaSuccess) {                                       \
        fprintf(stderr, "Error %s at line %d in file %s\n",                 \
                cudaGetErrorString(_m_cudaStat), __LINE__, __FILE__);       \
        exit(1);                                                            \
    } }

__device__ unsigned int bitreverse(unsigned int number) {
    number = ((0xf0f0f0f0 & number) >> 4) | ((0x0f0f0f0f & number) << 4);
    number = ((0xcccccccc & number) >> 2) | ((0x33333333 & number) << 2);
    number = ((0xaaaaaaaa & number) >> 1) | ((0x55555555 & number) << 1);
    return number;
}

/**
 * CUDA kernel function that reverses the order of bits in each element of the array.
 */
__global__ void bitreverse(void *data) {
    unsigned int *idata = (unsigned int*) data;
    idata[threadIdx.x] = bitreverse(idata[threadIdx.x]);
}

/**
 * Host function that prepares data array and passes it to the CUDA kernel.
 */
int main(void) {
    void *d = NULL;
    int i;
    unsigned int idata[WORK_SIZE], odata[WORK_SIZE];

    for (i = 0; i < WORK_SIZE; i++)
        idata[i] = (unsigned int) i;

    CUDA_CHECK_RETURN(cudaMalloc((void**) &d, sizeof(int) * WORK_SIZE));

    CUDA_CHECK_RETURN(cudaMemcpy(d, idata, sizeof(int) * WORK_SIZE, cudaMemcpyHostToDevice));

    bitreverse<<<1, WORK_SIZE, WORK_SIZE * sizeof(int)>>>(d);

    CUDA_CHECK_RETURN(cudaThreadSynchronize());
    // Wait for the GPU launched work to complete
    CUDA_CHECK_RETURN(cudaGetLastError());
    CUDA_CHECK_RETURN(cudaMemcpy(odata, d, sizeof(int) * WORK_SIZE, cudaMemcpyDeviceToHost));

    for (i = 0; i < WORK_SIZE; i++)
        printf("Input value: %u, device output: %u\n", idata[i], odata[i]);

    CUDA_CHECK_RETURN(cudaFree((void*) d));
    CUDA_CHECK_RETURN(cudaDeviceReset());

    return 0;
}

Answer 1

问题源于CUDA工具链无法找到有效的C ++编译器。 nvcc只是一个编译器驱动程序，它需要一个可用的C ++编译器来编译任何代码。

执行此操作的最正确方法[请注意您使用的是不受支持的Linux版本，因此请自行承担使用此建议]，设置一个本地目录，其中包含指向受支持的编译器套件的链接（这意味着匹配，支持gcc和g ++的版本）并在编译时将--compiler-bindir参数传递给nvcc。例如：

$ ls -l $HOME/cuda/bin
total 16
lrwxr-xr-x  1 talonmies  koti  16 Feb  9 12:41 g++ -> /usr/bin/g++-4.2
lrwxr-xr-x  1 talonmies  koti  16 Feb  9 12:41 gcc -> /usr/bin/gcc-4.2

这里我有一组支持编译器的链接。然后我可以像这样编译：

$ nvcc --compiler-bindir=$HOME/cuda/bin -c -arch=sm_12 -Xptxas="-v" nanobench.cu 
ptxas info    : 0 bytes gmem
ptxas info    : Compiling entry function '_Z5benchIfLi128000EEvPjPT_i' for 'sm_12'
ptxas info    : Used 5 registers, 28 bytes smem, 12 bytes cmem[1]

这可能是使用不支持系统编译器的替代编译器的最安全且侵入性最小的方法。

Answer 2

如其他地方所见：

su -c 'update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.6 10'  
sudo update-alternatives --config gcc

为我工作。我正在编译CudaMiner。

CUDA和gcc兼容性问题

2 个答案: