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Kenneth Reitz
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Merge pull request realpython#73 from rday/master
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‎docs/scenarios/speed.rst

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CPython, the most commonly used implementation of Python, is slow for CPU bound tasks. `PyPy`_ is fast.
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.. todo:: Fill in stub for Speed comparisons
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Using a slightly modified version of `David Beazleys`_ CPU bound test code(added loop for multiple tests), you can see the difference between CPython and PyPy's processing.
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::
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PyPy
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$ ./pypy -V
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Python 2.7.1 (7773f8fc4223, Nov 18 2011, 18:47:10)
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[PyPy 1.7.0 with GCC 4.4.3]
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$ ./pypy measure2.py
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0.0683999061584
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0.0483210086823
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0.0388588905334
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0.0440690517426
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0.0695300102234
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::
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CPython
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$ ./python -V
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Python 2.7.1
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$ ./python measure2.py
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1.06774401665
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1.45412397385
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1.51485204697
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1.54693889618
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1.60109114647
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Context
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The GIL
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-------
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`The GIL`_ (Global Interpreter Lock) is how Python allows multiple threads to operate at the same time. Python's
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memory management isn't entirely thread-safe, so the GIL is requried to prevents multiple threads from running
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the same Python code at once.
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David Beazley has a great `guide`_ on how the GIL operates. He also covers the `new GIL`_ in Python 3.2. His
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results show that maximizing performance in a Python application requires a strong understanding of the GIL,
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how it affects your specific application, how many cores you have, and where your application bottlenecks are.
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C Extentions
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------------
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The GIL
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-------
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`Special care`_ must be taken when writing C extensions to make sure you register your threads
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with the interpreter.
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C Extentions
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---------------
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.. _`PyPy`: http://pypy.org
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.. _`PyPy`: http://pypy.org
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.. _`The GIL`: http://wiki.python.org/moin/GlobalInterpreterLock
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.. _`guide`: http://www.dabeaz.com/python/UnderstandingGIL.pdf
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.. _`New GIL`: http://www.dabeaz.com/python/NewGIL.pdf
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.. _`Special care`: http://docs.python.org/c-api/init.html#threads
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.. _`David Beazleys`: http://www.dabeaz.com/GIL/gilvis/measure2.py

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