[pythran] Re: Numpy Benchmarks

  • From: serge Guelton <sguelton@xxxxxxxxxxxxx>
  • To: pythran@xxxxxxxxxxxxx
  • Date: Mon, 2 Jun 2014 13:37:08 +0200

On Mon, Jun 02, 2014 at 12:40:48PM +0200, Ian Ozsvald wrote:
> Hi Serge. I'm just back in the UK from teaching High Performance
> Python to PhDs in Denmark last week. We covered Pythran (mainly
> Cython, also PyPy and Numba).

That's great news! Any feedback concerning Pythran? Can you share your

> For the above benchmarks what is the main reason for the speed
> improvements over numpy? Is it because you're using locally defined
> variants of the numpy functions? Is it because you have
> parallelisation (there's no note about openmp/multi-core)?

There are several reasons. (No parallelization involved)

1. Avoid temporary: several array expressions are computed lazily and
merged in a single one

2. Faster Implementation of numpy functions: It's hard to say otherwise,
but I have the feeling that some numpy functions are not as efficient as
they could be. We have a raw C++ implementations for many of them and we
sometime get a x2 speedup just by calling them.

Just wait for the vectorized/ parallelized versions ;-)

> Cheers, Ian.
It's a pleasure to get some news! I still have some work to be done on
the numpy-benchmarks, but I'll keep you informed once everything is
ready for official disclosure!


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