Replicate a vector multiple times using CUDA Thrust

Question

I am trying to solve a problem using CUDA Thrust.

I have a host array with 3 elements. Is it possible, using Thrust, to create a device array of 3

Answer 1

One possible approach:

1. create a device vector of appropriate size
2. create 3 strided ranges, one for each of the element positions {1, 2, 3} in the final output (device) vector
3. use thrust::fill to fill each of the 3 strided ranges with the appropriate (host vector) element {1, 2, 3}

This code is a trivial modification of the strided range example to demonstrate. You can change the REPS define to 128 to see the full expansion to 384 output elements:

#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>
#include <thrust/iterator/permutation_iterator.h>
#include <thrust/functional.h>

#include <thrust/fill.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>

// for printing
#include <thrust/copy.h>
#include <ostream>


#define STRIDE 3
#define REPS  15  // change to 128 if you like
#define DSIZE (STRIDE*REPS)

// this example illustrates how to make strided access to a range of values
// examples:
//   strided_range([0, 1, 2, 3, 4, 5, 6], 1) -> [0, 1, 2, 3, 4, 5, 6]
//   strided_range([0, 1, 2, 3, 4, 5, 6], 2) -> [0, 2, 4, 6]
//   strided_range([0, 1, 2, 3, 4, 5, 6], 3) -> [0, 3, 6]
//   ...

template <typename Iterator>
class strided_range
{
    public:

    typedef typename thrust::iterator_difference<Iterator>::type difference_type;

    struct stride_functor : public thrust::unary_function<difference_type,difference_type>
    {
        difference_type stride;

        stride_functor(difference_type stride)
            : stride(stride) {}

        __host__ __device__
        difference_type operator()(const difference_type& i) const
        {
            return stride * i;
        }
    };

    typedef typename thrust::counting_iterator<difference_type>                   CountingIterator;
    typedef typename thrust::transform_iterator<stride_functor, CountingIterator> TransformIterator;
    typedef typename thrust::permutation_iterator<Iterator,TransformIterator>     PermutationIterator;

    // type of the strided_range iterator
    typedef PermutationIterator iterator;

    // construct strided_range for the range [first,last)
    strided_range(Iterator first, Iterator last, difference_type stride)
        : first(first), last(last), stride(stride) {}

    iterator begin(void) const
    {
        return PermutationIterator(first, TransformIterator(CountingIterator(0), stride_functor(stride)));
    }

    iterator end(void) const
    {
        return begin() + ((last - first) + (stride - 1)) / stride;
    }

    protected:
    Iterator first;
    Iterator last;
    difference_type stride;
};

int main(void)
{
    thrust::host_vector<int> h_data(STRIDE);
    h_data[0] = 1;
    h_data[1] = 2;
    h_data[2] = 3;

    thrust::device_vector<int> data(DSIZE);

    typedef thrust::device_vector<int>::iterator Iterator;
    strided_range<Iterator> pos1(data.begin(), data.end(), STRIDE);
    strided_range<Iterator> pos2(data.begin()+1, data.end(), STRIDE);
    strided_range<Iterator> pos3(data.begin()+2, data.end(), STRIDE);

    thrust::fill(pos1.begin(), pos1.end(), h_data[0]);
    thrust::fill(pos2.begin(), pos2.end(), h_data[1]);
    thrust::fill(pos3.begin(), pos3.end(), h_data[2]);


    // print the generated data
    std::cout << "data: ";
    thrust::copy(data.begin(), data.end(), std::ostream_iterator<int>(std::cout, " "));  std::cout << std::endl;

    return 0;
}

Answer 2

Robert Crovella has already answered this question using strided ranges. He has also pointed out the possibility of using the expand operator.

Below, I'm providing a worked example using the expand operator. Opposite to the use of strided ranges, it avoids the need of for loops.

#include <thrust/device_vector.h>
#include <thrust/gather.h>
#include <thrust/sequence.h>
#include <stdio.h>

using namespace thrust::placeholders;

/*************************************/
/* CONVERT LINEAR INDEX TO ROW INDEX */
/*************************************/
template <typename T>
struct linear_index_to_row_index : public thrust::unary_function<T,T> {

    T Ncols; // --- Number of columns

    __host__ __device__ linear_index_to_row_index(T Ncols) : Ncols(Ncols) {}

    __host__ __device__ T operator()(T i) { return i / Ncols; }
};

/*******************/
/* EXPAND OPERATOR */
/*******************/
template <typename InputIterator1, typename InputIterator2, typename OutputIterator>
OutputIterator expand(InputIterator1 first1,
                      InputIterator1 last1,
                      InputIterator2 first2,
                      OutputIterator output)
{
    typedef typename thrust::iterator_difference<InputIterator1>::type difference_type;

    difference_type input_size  = thrust::distance(first1, last1);
    difference_type output_size = thrust::reduce(first1, last1);

    // scan the counts to obtain output offsets for each input element
    thrust::device_vector<difference_type> output_offsets(input_size, 0);
    thrust::exclusive_scan(first1, last1, output_offsets.begin()); 

    // scatter the nonzero counts into their corresponding output positions
    thrust::device_vector<difference_type> output_indices(output_size, 0);
    thrust::scatter_if(thrust::counting_iterator<difference_type>(0), thrust::counting_iterator<difference_type>(input_size),
                       output_offsets.begin(), first1, output_indices.begin());

    // compute max-scan over the output indices, filling in the holes
    thrust::inclusive_scan(output_indices.begin(), output_indices.end(), output_indices.begin(), thrust::maximum<difference_type>());

    // gather input values according to index array (output = first2[output_indices])
    OutputIterator output_end = output; thrust::advance(output_end, output_size);
    thrust::gather(output_indices.begin(), output_indices.end(), first2, output);

    // return output + output_size
    thrust::advance(output, output_size);

    return output;
}

/**************************/
/* STRIDED RANGE OPERATOR */
/**************************/
template <typename Iterator>
class strided_range
{
    public:

    typedef typename thrust::iterator_difference<Iterator>::type difference_type;

    struct stride_functor : public thrust::unary_function<difference_type,difference_type>
    {
        difference_type stride;

        stride_functor(difference_type stride)
            : stride(stride) {}

        __host__ __device__
        difference_type operator()(const difference_type& i) const
        {
            return stride * i;
        }
    };

    typedef typename thrust::counting_iterator<difference_type>                   CountingIterator;
    typedef typename thrust::transform_iterator<stride_functor, CountingIterator> TransformIterator;
    typedef typename thrust::permutation_iterator<Iterator,TransformIterator>     PermutationIterator;

    // type of the strided_range iterator
    typedef PermutationIterator iterator;

    // construct strided_range for the range [first,last)
    strided_range(Iterator first, Iterator last, difference_type stride)
        : first(first), last(last), stride(stride) {}

    iterator begin(void) const
    {
        return PermutationIterator(first, TransformIterator(CountingIterator(0), stride_functor(stride)));
    }

    iterator end(void) const
    {
        return begin() + ((last - first) + (stride - 1)) / stride;
    }

    protected:
    Iterator first;
    Iterator last;
    difference_type stride;
};

/********/
/* MAIN */
/********/
int main(){

    /**************************/
    /* SETTING UP THE PROBLEM */
    /**************************/

    const int Nrows = 10;           // --- Number of objects
    const int Ncols =  3;           // --- Number of centroids  

    thrust::device_vector<int> d_sequence(Nrows * Ncols);
    thrust::device_vector<int> d_counts(Ncols, Nrows);
    thrust::sequence(d_sequence.begin(), d_sequence.begin() + Ncols);
    expand(d_counts.begin(), d_counts.end(), d_sequence.begin(), 
        thrust::make_permutation_iterator(
                                d_sequence.begin(),
                                thrust::make_transform_iterator(thrust::make_counting_iterator(0),(_1 % Nrows) * Ncols + _1 / Nrows)));

    printf("\n\nCentroid indices\n");
    for(int i = 0; i < Nrows; i++) {
        std::cout << " [ ";
        for(int j = 0; j < Ncols; j++)
            std::cout << d_sequence[i * Ncols + j] << " ";
        std::cout << "]\n";
    }

    return 0;
}

Answer 3

As an apparently simpler alternative to using CUDA Thrust, I'm posting below a worked example implementing in CUDA the classical Matlab's meshgrid function.

In Matlab

x = [1 2 3];
y = [4 5 6 7];
[X, Y] = meshgrid(x, y);

produces

X =

     1     2     3
     1     2     3
     1     2     3
     1     2     3

and

Y =

     4     4     4
     5     5     5
     6     6     6
     7     7     7

X is exactly the four-fold replication of the x array, which is the OP's question and first guess of Robert Crovella's answer, while Y is the three-fold consecutive replication of each element of the y array, which is the second guess of Robert Crovella's answer.

Here is the code:

#include <cstdio>

#include <thrust/pair.h>

#include "Utilities.cuh"

#define BLOCKSIZE_MESHGRID_X    16
#define BLOCKSIZE_MESHGRID_Y    16

#define DEBUG

/*******************/
/* MESHGRID KERNEL */
/*******************/
template <class T>
__global__ void meshgrid_kernel(const T * __restrict__ x, size_t Nx, const float * __restrict__ y, size_t Ny, T * __restrict__ X, T * __restrict__ Y) 
{
    unsigned int tidx = blockIdx.x * blockDim.x + threadIdx.x;
    unsigned int tidy = blockIdx.y * blockDim.y + threadIdx.y;

    if ((tidx < Nx) && (tidy < Ny)) {   
        X[tidy * Nx + tidx] = x[tidx];
        Y[tidy * Nx + tidx] = y[tidy];
    }
}

/************/
/* MESHGRID */
/************/
template <class T>
thrust::pair<T *,T *> meshgrid(const T *x, const unsigned int Nx, const T *y, const unsigned int Ny) {

    T *X; gpuErrchk(cudaMalloc((void**)&X, Nx * Ny * sizeof(T)));
    T *Y; gpuErrchk(cudaMalloc((void**)&Y, Nx * Ny * sizeof(T)));

    dim3 BlockSize(BLOCKSIZE_MESHGRID_X, BLOCKSIZE_MESHGRID_Y);
    dim3 GridSize (iDivUp(Nx, BLOCKSIZE_MESHGRID_X), iDivUp(BLOCKSIZE_MESHGRID_Y, BLOCKSIZE_MESHGRID_Y));

    meshgrid_kernel<<<GridSize, BlockSize>>>(x, Nx, y, Ny, X, Y);
#ifdef DEBUG
    gpuErrchk(cudaPeekAtLastError());
    gpuErrchk(cudaDeviceSynchronize());
#endif

    return thrust::make_pair(X, Y);
}

/********/
/* MAIN */
/********/
int main()
{
    const int Nx = 3;
    const int Ny = 4;

    float *h_x = (float *)malloc(Nx * sizeof(float));
    float *h_y = (float *)malloc(Ny * sizeof(float));

    float *h_X = (float *)malloc(Nx * Ny * sizeof(float));
    float *h_Y = (float *)malloc(Nx * Ny * sizeof(float));

    for (int i = 0; i < Nx; i++) h_x[i] = i;
    for (int i = 0; i < Ny; i++) h_y[i] = i + 4.f;

    float *d_x; gpuErrchk(cudaMalloc(&d_x, Nx * sizeof(float)));
    float *d_y; gpuErrchk(cudaMalloc(&d_y, Ny * sizeof(float)));

    gpuErrchk(cudaMemcpy(d_x, h_x, Nx * sizeof(float), cudaMemcpyHostToDevice));
    gpuErrchk(cudaMemcpy(d_y, h_y, Ny * sizeof(float), cudaMemcpyHostToDevice));

    thrust::pair<float *, float *> meshgrid_pointers = meshgrid(d_x, Nx, d_y, Ny);
    float *d_X = (float *)meshgrid_pointers.first;
    float *d_Y = (float *)meshgrid_pointers.second;

    gpuErrchk(cudaMemcpy(h_X, d_X, Nx * Ny * sizeof(float), cudaMemcpyDeviceToHost));
    gpuErrchk(cudaMemcpy(h_Y, d_Y, Nx * Ny * sizeof(float), cudaMemcpyDeviceToHost));

    for (int j = 0; j < Ny; j++) {
        for (int i = 0; i < Nx; i++) {
            printf("i = %i; j = %i; x = %f; y = %f\n", i, j, h_X[j * Nx + i], h_Y[j * Nx + i]);
        }
    }

    return 0;

}