[Numpy-discussion] Slicing slower than matrix multiplication?
Francesc Alted
faltet@pytables....
Mon Dec 14 11:51:58 CST 2009
A Monday 14 December 2009 18:20:32 Jasper van de Gronde escrigué:
> Francesc Alted wrote:
> > A Monday 14 December 2009 17:09:13 Francesc Alted escrigué:
> >> The things seems to be worst than 1.6x times slower for numpy, as matlab
> >> orders arrays by column, while numpy order is by row. So, if we want to
> >> compare pears with pears:
> >>
> >> For Python 600x200:
> >> Add a row: 0.113243 (1.132425e-05 per iter)
> >> For Matlab 600x200:
> >> Add a column: 0.021325 (2.132527e-006 per iter)
> >
> > Mmh, I've repeated this benchmark on my machine and got:
> >
> > In [59]: timeit E + Xi2[P/2]
> > 100000 loops, best of 3: 2.8 µs per loop
> >
> > that is, very similar to matlab's 2.1 µs and quite far from the 11 µs you
> > are getting for numpy in your machine... I'm using a Core2 @ 3 GHz.
>
> I'm using Python 2.6 and numpy 1.4.0rc1 on a Core2 @ 1.33 GHz
> (notebook). I'll have a look later to see if upgrading Python to 2.6.4
> makes a difference.
I don't think so. Your machine is slow for nowadays standards, so the 5x
slowness should be due to python/numpy overhead, but unfortunately nothing
that could be solved magically by using a newer python/numpy version.
--
Francesc Alted
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