On 26/06/2014 17:49, CM wrote:
I'm reposting my question with, I hope, better

I occasionally hear about performance improvements
for Python by various projects like psyco (now old),
ShedSkin, Cython, PyPy, Nuitka, Numba, and probably
many others.  The benchmarks are out there, and they
do make a difference, and sometimes a difference on
par with C, from what I've heard.

What I have never quite been able to get is the
degree  to which one can currently use these
approaches to speed up a Python application that
uses 3rd party libraries...and that the approaches
will "just work" without the developer having to
know C or really do a lot of difficult under-the-
hood sort of work.

For examples, and considering an application
written for Python 2.7, say, and using a GUI
toolkit, and a handful of 3rd party libraries:

- Can you realistically package up the PyPy
interpreter and have the app run faster with PyPy?
And can the application be released as a single file
executable if you use PyPy?

- Can you compile it with Nuitka to C?

I've had the (perhaps overly pessimistic) sense
that you still *can't* do these things, because
these projects only work on pure Python, or if
they do work with other libraries, it's always
described with major caveats that "I wouldn't
try this in production" or "this is just a test"
sort of thing, such as PyPy and wxPython.

I'd love to know what's possible, since getting
some even modest performance gains would probably
make apps feels snappier in some cases, and yet I
am not up for the job of the traditional advice
about "re-writing those parts in C".


Have you tried everything listed here https://wiki.python.org/moin/PythonSpeed/PerformanceTips ?

My fellow Pythonistas, ask not what our language can do for you, ask what you can do for our language.

Mark Lawrence

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