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71 lines
2.5 KiB
ReStructuredText
71 lines
2.5 KiB
ReStructuredText
Benchmark
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=========
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The following is the result of a synthetic benchmark comparing both compilation
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time and module size of pybind11 against Boost.Python.
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A python script (see the ``docs/benchmark.py`` file) was used to generate a
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set of dummy classes whose count increases for each successive benchmark
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(between 1 and 512 classes in powers of two). Each class has four methods with
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a randomly generated signature with a return value and four arguments. (There
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was no particular reason for this setup other than the desire to generate many
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unique function signatures whose count could be controlled in a simple way.)
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Here is an example of the binding code for one class:
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.. code-block:: cpp
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...
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class cl034 {
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public:
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cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
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cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
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cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
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cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
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};
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...
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PYBIND11_PLUGIN(example) {
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py::module m("example");
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...
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py::class_<cl034>(m, "cl034")
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.def("fn_000", &cl034::fn_000)
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.def("fn_001", &cl034::fn_001)
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.def("fn_002", &cl034::fn_002)
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.def("fn_003", &cl034::fn_003)
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...
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return m.ptr();
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}
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The Boost.Python version looks almost identical except that a return value
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policy had to be specified as an argument to ``def()``. For both libraries,
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compilation was done with
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.. code-block:: bash
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Apple LLVM version 7.0.0 (clang-700.0.72)
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and the following compilation flags
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.. code-block:: bash
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g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++11
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The following log-log plot shows how the compilation time grows for an
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increasing number of class and function declarations. pybind11 includes fewer
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headers, which initially leads to shorter compilation times, but the
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performance is ultimately very similar (pybind11 is 1 second faster for the
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largest file, which is less than 1% of the total compilation time).
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.. image:: pybind11_vs_boost_python1.svg
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Differences between the two libraries become more pronounced when considering
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the file size of the generated Python plugin. Note that the plot below does not
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include the size of the Boost.Python shared library, hence Boost actually has a
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slight advantage.
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.. image:: pybind11_vs_boost_python2.svg
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Despite this, the libraries procuced by Boost.Python for more than a few
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functions are consistently larger by a factor of 1.75.
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