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A first general restructuration of the doc according to the pattern [tour|tutorial|reference]. In the reference part, objects are documented per topic. In each topic, [definition|c++|python|hdf5] (not yet implemented)
40 lines
1.2 KiB
ReStructuredText
40 lines
1.2 KiB
ReStructuredText
Profiling in C++ and Python
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=============================
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One can easily profile c++ and Python code using `Google perftools <http://code.google.com/p/gperftools/>`_. In Ubuntu: ::
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libgoogle-perftools-dev
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google-perftools
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One must link the executable with the profiling library with the flag ``-lprofiler``.
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C++
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-------
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First run the C++ executable (here ``simple_tests``) after setting the environment variable ``CPUPROFILE``: ::
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CPUPROFILE=profile_test.prof ./simple_tests
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Then, analyze the results (stored in `profile_test.prof`) with ``google-pprof``: ::
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google-pprof --text ./simple_tests profile_test.prof | less
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See the documentation of `Google perftools <http://code.google.com/p/gperftools/>`_ for more information.
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Python
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--------
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One needs to install the python package `yep <https://pypi.python.org/pypi/yep>`_ (e.g ``easy_install yep``)
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First, run your script (``my_test.py``): ::
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pytriqs -myep -v my_test.py
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Then, analyze the results (stored in `my_test.py.prof`) with ``google-pprof``: ::
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google-pprof --text my_test.py my_test.py.prof | less
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Alternatively, to view the results more graphically, run: ::
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google-pprof --web my_test.py my_test.py.prof
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