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Kirill Smelkov
cpython
Commits
9cb41dfb
Commit
9cb41dfb
authored
Dec 03, 2011
by
Antoine Pitrou
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Remove references to psyco, which is mostly unmaintained and doesn't work with Python 3.
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5dd95d6f
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Doc/faq/extending.rst
Doc/faq/extending.rst
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Doc/faq/programming.rst
Doc/faq/programming.rst
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Doc/faq/extending.rst
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9cb41dfb
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@@ -37,13 +37,7 @@ Writing C is hard; are there any alternatives?
There are a number of alternatives to writing your own C extensions, depending
on what you're trying to do.
.. XXX make sure these all work; mention Cython
If you need more speed, `Psyco <http://psyco.sourceforge.net/>`_ generates x86
assembly code from Python bytecode. You can use Psyco to compile the most
time-critical functions in your code, and gain a significant improvement with
very little effort, as long as you're running on a machine with an
x86-compatible processor.
.. XXX make sure these all work
`Cython <http://cython.org>`_ and its relative `Pyrex
<http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>`_ are compilers
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Doc/faq/programming.rst
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@@ -121,19 +121,11 @@ My program is too slow. How do I speed it up?
That's a tough one, in general. There are many tricks to speed up Python code;
consider rewriting parts in C as a last resort.
In some cases it's possible to automatically translate Python to C or x86
assembly language, meaning that you don't have to modify your code to gain
increased speed.
.. XXX seems to have overlap with other questions!
`Cython <http://cython.org>`_ and `Pyrex <http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>`_
can compile a slightly modified version of Python code into a C extension, and
can be used on many different platforms.
`Psyco <http://psyco.sourceforge.net>`_ is a just-in-time compiler that
translates Python code into x86 assembly language. If you can use it, Psyco can
provide dramatic speedups for critical functions.
can be used on many different platforms. Depending on your code, Cython
may be able to make it significantly faster than when run by the Python
interpreter.
The rest of this answer will discuss various tricks for squeezing a bit more
speed out of Python code. *Never* apply any optimization tricks unless you know
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