How to use CUB and Thrust in one CUDA code

Question

I'm trying to introduce some CUB into my "old" Thrust code, and so have started with a small example to compare thrust::reduce_by_key with cub::DeviceReduce::ReduceByKey, both applied to thrust::device_vectors.

The thrust part of the code is fine, but the CUB part, which naively uses raw pointers obtained via thrust::raw_pointer_cast, crashes after the CUB calls. I put in a cudaDeviceSynchronize() to try to solve this problem, but it didn't help. The CUB part of the code was cribbed from the CUB web pages.

On OSX the runtime error is:

libc++abi.dylib: terminate called throwing an exception
Abort trap: 6 

On Linux the runtime error is:

terminate called after throwing an instance of 'thrust::system::system_error'
what():  an illegal memory access was encountered

The first few lines of cuda-memcheck are:

========= CUDA-MEMCHECK
========= Invalid __global__ write of size 4
=========     at 0x00127010 in /home/sdettrick/codes/MCthrust/tests/../cub-1.3.2/cub/device/dispatch/../../block_range/block_range_reduce_by_key.cuh:1017:void cub::ReduceByKeyRegionKernel<cub::DeviceReduceByKeyDispatch<unsigned int*, unsigned int*, float*, float*, int*, cub::Equality, CustomSum, int>::PtxReduceByKeyPolicy, unsigned int*, unsigned int*, float*, float*, int*, cub::ReduceByKeyScanTileState<float, int, bool=1>, cub::Equality, CustomSum, int>(unsigned int*, float*, float*, int*, cub::Equality, CustomSum, int, cub::DeviceReduceByKeyDispatch<unsigned int*, unsigned int*, float*, float*, int*, cub::Equality, CustomSum, int>::PtxReduceByKeyPolicy, unsigned int*, int, cub::GridQueue<int>)
=========     by thread (0,0,0) in block (0,0,0)
=========     Address 0x7fff7dbb3e88 is out of bounds
=========     Saved host backtrace up to driver entry point at kernel launch time

Unfortunately I'm not too sure what to do about that.

Any help would be greatly appreciated. I tried this on the NVIDIA developer zone but didn't get any responses. The complete example code is below. It should compile with CUDA 6.5 and cub 1.3.2:

#include <iostream>
#include <thrust/sort.h>
#include <thrust/gather.h>
#include <thrust/device_vector.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/iterator/permutation_iterator.h>
#include <thrust/iterator/discard_iterator.h>

#include <cub/cub.cuh>   // or equivalently <cub/device/device_radix_sort.cuh>

//========================================
// for CUB:
struct CustomSum
{
    template <typename T>
    CUB_RUNTIME_FUNCTION __host__ __device__ __forceinline__
    //__host__ __device__ __forceinline__
    T operator()(const T &a, const T &b) const {
        return b+a;
    }
};
//========================================

int main()
{
  const int Nkey=20;
  int Nseg=9;
  int ikey[Nkey] = {0, 0, 0, 6, 8, 0, 2, 4, 6, 8, 1, 3, 5, 7, 8, 1, 3, 5, 7, 8}; 

  thrust::device_vector<unsigned int> key(ikey,ikey+Nkey);
  thrust::device_vector<unsigned int> keysout(Nkey);

  // Let's reduce x, by key:

  float xval[Nkey];
  for (int i=0; i<Nkey; i++) xval[i]=ikey[i]+0.1f;

  thrust::device_vector<float> x(xval,xval+Nkey);

  // First, sort x by key:

  thrust::sort_by_key(key.begin(),key.end(),x.begin());

  //---------------------------------------------------------------------
  std::cout<<"=================================================================="<<std::endl
       <<" THRUST reduce_by_key:"<<std::endl
       <<"=================================================================="<<std::endl;

  thrust::device_vector<float> output(Nseg,0.0f);

  thrust::reduce_by_key(key.begin(),
            key.end(),
            x.begin(),
            keysout.begin(),
            output.begin());

  for (int i=0;i<Nkey;i++) std::cout << x[i] <<" ";  std::cout<<std::endl;
  for (int i=0;i<Nkey;i++) std::cout << key[i] <<" ";  std::cout<<std::endl;
  for (int i=0;i<Nseg;i++) std::cout << output[i] <<" ";  std::cout<<std::endl;

  float ototal=thrust::reduce(output.begin(),output.end());
  float xtotal=thrust::reduce(x.begin(),x.end());
  std::cout << "total="<< ototal <<", should be "<<xtotal<<std::endl;

  //---------------------------------------------------------------------
  std::cout<<"=================================================================="<<std::endl
       <<" CUB ReduceByKey:"<<std::endl
       <<"=================================================================="<<std::endl;


  unsigned int *d_keys_in   =thrust::raw_pointer_cast(&key[0]);
  float        *d_values_in =thrust::raw_pointer_cast(&x[0]);  
  unsigned int *d_keys_out  =thrust::raw_pointer_cast(&keysout[0]);
  float        *d_values_out=thrust::raw_pointer_cast(&output[0]);
  int          *d_num_segments=&Nseg;
  CustomSum   reduction_op;

  std::cout << "CUB input" << std::endl;
  for (int i=0; i<Nkey; ++i) std::cout << key[i]  << " ";  std::cout<<std::endl;
  for (int i=0; i<Nkey; ++i) std::cout << x[i] << " ";  std::cout<< std::endl;
  for (int i=0; i<Nkey; ++i) std::cout << keysout[i] << " ";  std::cout<< std::endl;
  for (int i=0; i<Nseg; ++i) std::cout << output[i] << " ";  std::cout<< std::endl;

  // Determine temporary device storage requirements
  void     *d_temp_storage = NULL;
  size_t   temp_storage_bytes = 0;
  cub::DeviceReduce::ReduceByKey(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_segments, reduction_op, Nkey);

  // Allocate temporary storage
  cudaMalloc(&d_temp_storage, temp_storage_bytes);
  std::cout << "temp_storage_bytes = " << temp_storage_bytes << std::endl;

  // Run reduce-by-key
  cub::DeviceReduce::ReduceByKey(d_temp_storage, temp_storage_bytes, d_keys_in, d_keys_out, d_values_in, d_values_out, d_num_segments, reduction_op, Nkey);
  cudaDeviceSynchronize();

  std::cout << "CUB output" << std::endl;

  std::cout<<Nkey<<" "<<Nseg<<std::endl;
  std::cout<<key.size() << " "<<x.size() << " "<<keysout.size() << " "<<output.size() << std::endl;

  // At this point onward it dies:
  //libc++abi.dylib: terminate called throwing an exception
  //Abort trap: 6  

  // If the next line is uncommented, it crashes the Mac!
  for (int i=0; i<Nkey; ++i) std::cout << key[i]  << " ";  std::cout<<std::endl;
  // for (int i=0; i<Nkey; ++i) std::cout << x[i] << " ";  std::cout<< std::endl;
  // for (int i=0; i<Nkey; ++i) std::cout << keysout[i] << " ";  std::cout<< std::endl;
  // for (int i=0; i<Nseg; ++i) std::cout << output[i] << " ";  std::cout<< std::endl;
  cudaFree(d_temp_storage);

  ototal=thrust::reduce(output.begin(),output.end());
  xtotal=thrust::reduce(x.begin(),x.end());
  std::cout << "total="<< ototal <<", should be "<<xtotal<<std::endl;
  return 1;
}

Answer 1

This is not appropriate:

 int          *d_num_segments=&Nseg;

You cannot take the address of a host variable and use it as a device pointer.

Instead do this:

int *d_num_segments;
cudaMalloc(&d_num_segments, sizeof(int));

This allocates space on the device for the size of data (a single integer that cub will write to), and assigns the address of that allocation to your d_num_segments variable. This then becomes a valid device pointer.

In (*ordinary, non-UM) CUDA, it is illegal dereference a host address in device code, or a device address in host code.