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26 lines
2.5 KiB
Plaintext
26 lines
2.5 KiB
Plaintext
[section:benchmarks Benchmarks]
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The library is designed to be fast. While it is unlikely that the time spend in the histogram is limiting an application, we cannot predict how the library is used.
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The following plot shows results of a benchmark on a 2.9 GHz Macbook Pro. Random numbers from a uniform and a normal distribution are filled into histograms with 1, 3, and 6 axes. 100 bins are used in the first case, 100x100x100=10^6 in the second, and 10x10x10x10x10x10=10^6 in the third. Each test is repeated 50 times and the minimum time is taken.
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There are two bars per benchmarked histogram. 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 slightly because of branch-prediction misses.
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[$../benchmark.png [width 5in] [height 5in]]
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[variablelist Plot legend:
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[[root] [[@https://root.cern.ch ROOT classes] (`TH1I` for 1D, `TH3I` for 3D and `THnI` for 6D)]]
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[[py:numpy] [numpy functions ([python]`numpy.histogram` for 1D, `numpy.histogramdd` for 2D, 3D, and 6D)]]
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[[gsl] [[@https://www.gnu.org/software/gsl/doc/html/histogram.html GSL histograms] for 1D and 2D]]
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[[hs_ss] [[funcref boost::histogram::make_static_histogram static histogram] with [classref std::vector<int>]]]
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[[hs_sd] [[funcref boost::histogram::make_static_histogram static histogram] with [classref boost::histogram::default_storage]]]
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[[hd_ss] [[funcref boost::histogram::make_dynamic_histogram dynamic histogram] with [classref std::vector<int>]]]
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[[hd_sd] [[funcref boost::histogram::make_dynamic_histogram dynamic histogram] with [classref boost::histogram::default_storage]]]
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]
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A [classref boost::histogram::make_static_histogram static histogram] is always faster than [classref boost::histogram::make_dynamic_histogram dynamic histogram] and safer to use, as more checks are done at compile time. [classref boost::histogram::adaptive_storage] is faster than [classref boost::histogram::default_storage] 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 overhead of handling memory in a dynamic way.
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The histograms in this library are mostly faster than the competition, in some cases by a factor of 2. Simultaneously they are more flexible, since binning strategies and the counter management can be customised.
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[endsect]
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