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

Maciej Fijalkowski fijall at gmail.com
Sun Oct 16 17:50:45 CEST 2011

On Sun, Oct 16, 2011 at 2:29 PM, Stefan Behnel <stefan_ml at behnel.de> wrote
> Samuel Vaiter, 14.10.2011 17:59:
>> The main
>> reason why Numpy is my main interest is that as Ph.D student in
>> Applied Mathematics, I really hope one day we will be able to perform
>> numerical computation without using heavy binding in C/Fortran or
>> intermediate solution like Cython.
> I guess you didn't mean it that way, but "intermediate solution" makes it
> sound like you expect any of these to go away one day. They sure won't.
> Manually optimised C and Fortran code will always beat JIT compilers,
> especially in numerics. It's a game they can't win - whenever JIT compilers
> get too close to hand optimised code, someone will come along and write
> better code.
> Stefan

I guess what you say is at best [citation needed]. We have proven
already that we can perform several optimizations that are very hard
to perform at the C level. And indeed, while you can always argue
"well, you can just write a better compiler", it's true also for JITs.
And we're only at the beginning of what we can do.

One example that I have in mind is array expressions that depend on
runtime - we can optimize them fairly well in the JIT (which means SSE
and whatnot), but you just can't get the same thing in C, because
you're unable to compile native code per a set of array operations.


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