On Wed, Dec 22, 2010 at 12:21 AM, Francesc Alted <fal...@pytables.org>wrote:
> <snip>
> Wow, really nice work! It would be great if that could make into NumPy
> :-) Regarding your comment on numexpr being faster, I'm not sure (your
> new_iterator branch does not work for me; it gives me an error like:
> AttributeError: 'module' object has no attribute 'newiter'),
What are you using to build it? So far I've just modified the setup.py
scripts, I still need to add it to numscons.
> but my
> guess is that your approach seems actually faster:
>
> >>> a = np.random.random((50,50,50,10))
> >>> b = np.random.random((50,50,1,10))
> >>> c = np.random.random((50,50,50,1))
> >>> timeit 3*a+b-(a/c)
> 10 loops, best of 3: 67.5 ms per loop
> >>> import numexpr as ne
> >>> ne.evaluate("3*a+b-(a/c)
> >>> timeit ne.evaluate("3*a+b-(a/c)")
> 10 loops, best of 3: 42.8 ms per loop
>
> i.e. numexpr is not able to achieve the 2x speedup mark that you are
> getting with ``luf`` (using a Core2 @ 3 GHz here).
>
That's promising! I based my assertion on getting a slower speedup than
numexpr does on their front page example.
-Mark
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