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To: python-list@python.org
From: Stefan Behnel <stefan_ml@behnel.de>
Subject: Re: Writing code to be optimizable
Date: Wed, 23 Nov 2011 08:37:53 +0100
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snorble, 23.11.2011 06:19:
> Sometimes I want to prototype a program in Python, with the idea of
> optimizing it later by rewriting parts of it in C or Cython. But I
> usually find that in order to rewrite the slow parts, I end up writing
> those parts very much like C or C++ anyway, and I end up wondering
> what is the point of using Python in such a project.
>
> I liked the idea of Cython, mainly the idea of being able to write in
> pure Python, then create a file that modifies the pure Python file
> (with type declarations and such), but I ran into the same problems.
> In order to be able to modify the code, I can't make a lot of use of
> the really helpful things in Python like dicts and such. I have to
> dumb down Python to where I'm basically writing C code in Python, so
> again I ask myself what is the point?
>
> Is it reasonable to prototype an application in Python that will
> require performance? Are there any recommendations on how to write
> code in such a way that it can later be optimized or replaced with a
> module written in C or Cython?

I think the usual approach is to write it in Python and make sure it works 
by writing "enough" tests, then benchmark it, then decide if a non-Python 
level optimisation is required.

If it's required, Cython compile the entire module and add static types to 
your hot spots, either in Python notation ("pure mode") or in an external 
.pxd file. If that doesn't yield enough performance, copy code into a 
separate Cython module and optimise it there using C paradigms instead of 
Python paradigms, i.e. apply algorithmic optimisations by dropping data 
structures into C. Then use an import to use the code from your so-called 
accelerator module in your original module. Try to provide both a pure 
Python implementation and a Cython implementation, as that will allow you 
to run your code in other Python implementations directly and to compare 
your two implementations for equivalence and performance.

The advantage of stepping down into C-ish Cython code incrementally is that 
you will have a working implementation at each step and can decide to stop 
optimising because it is "good enough". If you started 'optimising' your 
code while still writing it, it will take longer to write it and you can 
never be sure that the complexity you add to the long-term maintenance by 
writing C-ish code is truly worth the performance gain (or if there even is 
any gain).

Stefan