project_6/cccl_upstream/thrust/examples/histogram.cu

#include <thrust/adjacent_difference.h>
#include <thrust/binary_search.h>
#include <thrust/copy.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/inner_product.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/random.h>
#include <thrust/sort.h>

#include <cuda/iterator>

#include <iomanip>
#include <iostream>
#include <iterator>

// This example illustrates several methods for computing a
// histogram [1] with Thrust.  We consider standard "dense"
// histograms, where some bins may have zero entries, as well
// as "sparse" histograms, where only the nonzero bins are
// stored.  For example, histograms for the data set
//    [2 1 0 0 2 2 1 1 1 1 4]
// which contains 2 zeros, 5 ones, and 3 twos and 1 four, is
//    [2 5 3 0 1]
// using the dense method and
//    [(0,2), (1,5), (2,3), (4,1)]
// using the sparse method. Since there are no threes, the
// sparse histogram representation does not contain a bin
// for that value.
//
// Note that we choose to store the sparse histogram in two
// separate arrays, one array of keys and one array of bin counts,
//    [0 1 2 4] - keys
//    [2 5 3 1] - bin counts
// This "structure of arrays" format is generally faster and
// more convenient to process than the alternative "array
// of structures" layout.
//
// The best histogramming methods depends on the application.
// If the number of bins is relatively small compared to the
// input size, then the binary search-based dense histogram
// method is probably best.  If the number of bins is comparable
// to the input size, then the reduce_by_key-based sparse method
// ought to be faster.  When in doubt, try both and see which
// is fastest.
//
// [1] http://en.wikipedia.org/wiki/Histogram

// simple routine to print contents of a vector
template <typename Vector>
void print_vector(const std::string& name, const Vector& v)
{
  using T = typename Vector::value_type;
  std::cout << "  " << std::setw(20) << name << "  ";
  thrust::copy(v.begin(), v.end(), std::ostream_iterator<T>(std::cout, " "));
  std::cout << '\n';
}

// dense histogram using binary search
template <typename Vector1, typename Vector2>
void dense_histogram(const Vector1& input, Vector2& histogram)
{
  using ValueType = typename Vector1::value_type; // input value type
  using IndexType = typename Vector2::value_type; // histogram index type

  // copy input data (could be skipped if input is allowed to be modified)
  thrust::device_vector<ValueType> data(input);

  // print the initial data
  print_vector("initial data", data);

  // sort data to bring equal elements together
  thrust::sort(data.begin(), data.end());

  // print the sorted data
  print_vector("sorted data", data);

  // number of histogram bins is equal to the maximum value plus one
  IndexType num_bins = data.back() + 1;

  // resize histogram storage
  histogram.resize(num_bins);

  // find the end of each bin of values
  thrust::counting_iterator<IndexType> search_begin(0);
  thrust::upper_bound(data.begin(), data.end(), search_begin, search_begin + num_bins, histogram.begin());

  // print the cumulative histogram
  print_vector("cumulative histogram", histogram);

  // compute the histogram by taking differences of the cumulative histogram
  thrust::adjacent_difference(histogram.begin(), histogram.end(), histogram.begin());

  // print the histogram
  print_vector("histogram", histogram);
}

// sparse histogram using reduce_by_key
template <typename Vector1, typename Vector2, typename Vector3>
void sparse_histogram(const Vector1& input, Vector2& histogram_values, Vector3& histogram_counts)
{
  using ValueType = typename Vector1::value_type; // input value type
  using IndexType = typename Vector3::value_type; // histogram index type

  // copy input data (could be skipped if input is allowed to be modified)
  thrust::device_vector<ValueType> data(input);

  // print the initial data
  print_vector("initial data", data);

  // sort data to bring equal elements together
  thrust::sort(data.begin(), data.end());

  // print the sorted data
  print_vector("sorted data", data);

  // number of histogram bins is equal to number of unique values (assumes data.size() > 0)
  IndexType num_bins = thrust::inner_product(
    data.begin(),
    data.end() - 1,
    data.begin() + 1,
    IndexType(1),
    cuda::std::plus<IndexType>(),
    cuda::std::not_equal_to<ValueType>());

  // resize histogram storage
  histogram_values.resize(num_bins);
  histogram_counts.resize(num_bins);

  // compact find the end of each bin of values
  thrust::reduce_by_key(
    data.begin(), data.end(), cuda::constant_iterator<IndexType>(1), histogram_values.begin(), histogram_counts.begin());

  // print the sparse histogram
  print_vector("histogram values", histogram_values);
  print_vector("histogram counts", histogram_counts);
}

int main()
{
  thrust::default_random_engine rng;
  thrust::uniform_int_distribution<int> dist(0, 9);

  const int N = 40;
  const int S = 4;

  // generate random data on the host
  thrust::host_vector<int> input(N);
  for (int i = 0; i < N; i++)
  {
    int sum = 0;
    for (int j = 0; j < S; j++)
    {
      sum += dist(rng);
    }
    input[i] = sum / S;
  }

  // demonstrate dense histogram method
  {
    std::cout << "Dense Histogram" << '\n';
    thrust::device_vector<int> histogram;
    dense_histogram(input, histogram);
  }

  // demonstrate sparse histogram method
  {
    std::cout << "Sparse Histogram" << '\n';
    thrust::device_vector<int> histogram_values;
    thrust::device_vector<int> histogram_counts;
    sparse_histogram(input, histogram_values, histogram_counts);
  }

  // Note:
  // A dense histogram can be converted to a sparse histogram
  // using stream compaction (i.e. thrust::copy_if).
  // A sparse histogram can be expanded into a dense histogram
  // by initializing the dense histogram to zero (with thrust::fill)
  // and then scattering the histogram counts (with thrust::scatter).

  return 0;
}