[pypy-dev] Contributing to pypy [especially numpy]

Stefan Behnel stefan_ml at behnel.de
Mon Oct 17 10:16:14 CEST 2011

Maciej Fijalkowski, 17.10.2011 09:34:
> On Mon, Oct 17, 2011 at 12:10 AM, Armin Rigo wrote:
>> On Sun, Oct 16, 2011 at 23:41, David Cournapeau wrote:
>>> Interesting to know. But then, wouldn't this limit the speed gains to
>>> be expected from the JIT ?
>> Yes, to some extent.  It cannot give you the last bit of performance
>> improvements you could expect from arithmetic optimizations, but (as
>> usual) you get already the several-times improvements of e.g. removing
>> the boxing and unboxing of float objects.  Personally I'm wary of
>> going down that path, because it means that the results we get could
>> suddenly change their least significant digit(s) when the JIT kicks
>> in.  At least there are multiple tests in the standard Python test
>> suite that would fail because of that.
> The thing is that as with python there are scenarios where we can
> optimize a lot (like you said by doing type specialization or folding
> array operations or using multithreading based on runtime decisions)
> where we don't have to squeeze the last 2% of performance. This is the
> approach that worked great for optimizing Python so far (concentrate
> on the larger picture).

That's what I meant. It's not surprising that a JIT compiler can be faster 
than an interpreter, and it's not surprising that it can optimise generic 
code into several times faster specialised code. That's what JIT compilers 
are there for, and PyPy does a really good job at that.

It's much harder to reach up to the performance of specialised, hand tuned 
code, though. And there is a lot of specialised, hand tuned code in SciPy 
and Sage, for example. That's a different kind of game than the "running 
generic Python code faster than CPython" business, however worthy that is 
by itself.


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