Add reduce_each example

[jaredhoberock]

This code for summing matrix rows is appropriate and simple:

#include <thrust/tabulate.h>
#include <thrust/reduce.h>
#include <thrust/tabulate.h>
#include <thrust/device_vector.h>
#include <thrust/execution_policy.h> // needed for thrust::device
#include <thrust/copy.h>
#include <thrust/sequence.h>
#include <iterator>

struct reduce_functor
{
  thrust::device_ptr<float> matrix;
  int width;

  __host__ __device__
  float operator()(int row_index)
  {
    auto begin = matrix + width * row_index;
    return thrust::reduce(thrust::device, begin, begin + width, 0.0f);
  }
};

template<class Vector>
void print_matrix(const Vector& mtx, int width)
{
  auto num_rows = mtx.size() / width;
  for(auto row = 0; row < num_rows; ++row)
  {
    std::cout << "|";
    auto begin = mtx.begin() + row * width;
    thrust::copy(begin, begin + width, std::ostream_iterator<typename Vector::value_type>(std::cout, " "));
    std::cout << "|" << std::endl;
  }
}

int main()
{
  int width = 10;
  thrust::device_vector<float> matrix(width * width);

  // fill the matrix with ascending values
  thrust::sequence(matrix.begin(), matrix.end());

  auto f = reduce_functor{matrix.data(), width};

  thrust::device_vector<float> row_sums(width);

  // call the reduce_functor on the indices of the rows of matrix
  // this reduces each row of the matrix to a single float containing the row's sum
  thrust::tabulate(row_sums.begin(), row_sums.end(), f);

  std::cout << "matrix: " << std::endl;
  print_matrix(matrix, width);
  std::cout << std::endl;

  std::cout << "row sums:" << std::endl;
  print_matrix(row_sums, 1);
  std::cout << std::endl;

  return 0;
}

[judfs]

This seems simpler than the thrust solutions at http://www.orangeowlsolutions.com/archives/1239 -> https://github.com/OrangeOwlSolutions/Thrust/blob/master/Reduce_rows.cu . (ie, what you get googling "cuda matrix reduction"). Shame this tabulate approach is buried.
(Though it's a shame there is no native convince function for reducing matrix rows. Also for "sum" reduction, a blas vector multiply is still 10x faster.)