我收到了这个错误
/usr/local/cuda-5.0/bin/../include/host_config.h:82:2: 错误：#error -- 不支持的 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，它们确保兼容 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@选择桌面：/usr/local/cuda-5.0/bin$ gcc gcc
gcc-4.7 gcc-nm-4.7 gcc-4.6 gcc-ar-4.7
gcc-ranlib-4.7
在互联网上，他们建议在 cuda bin 目录中添加指向 gcc-4.6 的链接。
所以我做了
选择@选择桌面：/usr/local/cuda-5.0/bin$ sudo ln -s /usr/bin/gcc-4.6 gcc
我得到另一个错误
**** 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 ****
再次谷歌搜索并没有给我带来一些明确的情况（gcc降级等）
所以我在这里问现在问题是什么，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
这是我目前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;
}