Thrust error with CUDA separate compilation

Question

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.

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

Then I found this, which says:

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.

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:

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

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

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

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();
}

Answer 1

As mentioned in the comments by Robert Crovella:

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.

Edit:

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