Boost C++ Libraries

The fill performance of different configurations of Boost.Histogram are compared with histogram classes and functions from other libraries. 6 million random numbers from a uniform and a normal distribution are filled into histograms with 1, 2, 3, and 6 axes. 100 bins per axis are used for 1, 2, 3 axes. 10 bins per axis for the case with 6 axes. Shown is the average computing time per number in nanoseconds.

There are two bars per benchmark. The upper is for random data from a uniform distribution which never falls outside the axis range. The lower is for random data from a normal distribution, which falls outside of the axis range in about 10 % of the cases. The second differs because values outside of the histogram range trigger a different code path, so that the computing pipeline gets interrupted.

The benchmarks are compiled with g++-7.3.0 and -O3.

Boost.Histogram is mostly faster than the competition. Simultaneously, it is much more flexible, since the axis and storage types can be customized.

A histogram with compile-time configured axes is always faster than one with run-time configured axes. boost::histogram::unlimited_storage is faster than a std::vector<int> for histograms with many bins, because it uses the cache more effectively due to its smaller memory consumption per bin. If the number of bins is small, it is slower because of the overhead of handling memory dynamically.

Boost.Histogram provides the boost::histogram::indexed range generator for convenient iteration over the histogram cells. Using the range generator is very convenient and it is faster than by writing nested for-loops.

// nested for loops over 2d histogram
for (int i = 0; i < h.axis(0).size(); ++i) {
  for (int j = 0; j < h.axis(1).size(); ++j) {
    std::cout << i << " " << j << " " << h.at(i, j) << std::endl;
  }
}

// same, with indexed range generator
for (auto x : boost::histogram::indexed(h)) {
  std::cout << x.index(0) << " " << x.index(1) << " " << *x << std::endl;
}

The access time per bin is compared for these two iteration strategies for histograms that hold the axes in a std::tuple (tuple), in a std::vector (vector), and in a std::vector<boost::histogram::axis::variant> (vector of variants). The access time per bin is measured for axis with 4 to 128 bins per axis.