CUDA单独编译时的推力错误 [英] Thrust error with CUDA separate compilation

问题描述

当我尝试编译CUDA并启用可重定位设备代码（-rdc = true）时，我遇到错误。我使用Visual Studio 2013作为编译器与CUDA 7.5。下面是一个显示错误的小例子。为了说明，下面的代码运行正常，当-rdc = false，但是当设置为true时，错误出现。

I'm running into an error when I try to compile CUDA with relocatable device code enabled (-rdc = true). I'm using Visual Studio 2013 as compiler with CUDA 7.5. Below is a small example that shows the error. To clarify, the code below runs fine when -rdc = false, but when set to true, the error shows up.

错误简单地说：CUDA错误11 [ \\ cuda \detail\cub\device\dispatch/ device_radix_sort_dispatch.cuh，687]：无效的参数

The error simply says: CUDA error 11 [\cuda\detail\cub\device\dispatch/device_radix_sort_dispatch.cuh, 687]: invalid argument

然后我发现 this ，其中说：

When invoked with primitive data types, thrust::sort, thrust::sort_by_key,thrust::stable_sort, thrust::stable_sort_by_key may fail to link in some cases with nvcc -rdc=true.

是否有一些解决方法允许单独编译？

Is there some workaround to allow separate compilation?

main.cpp：

#include <stdio.h>
#include <vector>
#include "cuda_runtime.h"
#include "RadixSort.h"

typedef unsigned int uint;
typedef unsigned __int64 uint64;

int main()
{
   RadixSort sorter;

   uint n = 10;
   std::vector<uint64> test(n);
   for (uint i = 0; i < n; i++)
      test[i] = i + 1;

   uint64 * d_array;
   uint64 size = n * sizeof(uint64);

   cudaMalloc(&d_array, size);
   cudaMemcpy(d_array, test.data(), size, cudaMemcpyHostToDevice);

   try
   {
      sorter.Sort(d_array, n);
   }
   catch (const std::exception & ex)
   {
      printf("%s\n", ex.what());
   }
}

RadixSort.h：

RadixSort.h:

#pragma once
typedef unsigned int uint;
typedef unsigned __int64 uint64;

class RadixSort
{
public:
   RadixSort() {}
   ~RadixSort() {}

   void Sort(uint64 * input, const uint n);
};

RadixSort.cu：

RadixSort.cu:

#include "RadixSort.h"

#include <thrust/device_vector.h>
#include <thrust/device_ptr.h>
#include <thrust/sort.h>

void RadixSort::Sort(uint64 * input, const uint n)
{
    thrust::device_ptr<uint64> d_input = thrust::device_pointer_cast(input);
    thrust::stable_sort(d_input, d_input + n);
    cudaDeviceSynchronize();
}

推荐答案

Robert Crovella：

As mentioned in the comments by Robert Crovella:

将CUDA架构更改为更高的值会解决这个问题。在我的情况下，我把它改为compute_30和sm_30在CUDA C ++ - >设备 - >代码生成。

Changing the CUDA architecture to a higher value will solve this problem. In my case I changed it to compute_30 and sm_30 under CUDA C++ -> Device -> Code Generation.

编辑：

一般建议是为您的特定GPU选择最佳适合层次结构。有关其他信息，请参阅评论中的链接。

The general recommendation is to select the best fit hierarchy for your specific GPU. See the link in comments for additional information.

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