[Numpy-discussion] Array vectorization in numpy

[Carlos Becker]

Hi. That is really amazing.
I checked out that numexpr branch and saw some strange results when
evaluating expressions on a multi-core i7 processor.
Running the numexpr.test() yields a few 'F', which I suppose are failing
tests. I tried to let the tests finish but it takes more than 20 min, is
there any way to run the tests individually?

Is there a specific mailing list for numexpr, so I can avoid 'spamming'
numpy?

Thanks!

----------------------
Carlos Becker


On Wed, Jul 20, 2011 at 8:01 PM, Mark Wiebe <mwwiebe at gmail.com> wrote:

>
> On Wed, Jul 20, 2011 at 5:52 PM, srean <srean.list at gmail.com> wrote:
>
>> >> I think this is essential to speed up numpy. Maybe numexpr could handle
>> this in the future? Right now the general use of numexpr is result =
>> numexpr.evaluate("whatever"), so the same problem seems to be there.
>> >>
>> >> With this I am not saying that numpy is not worth it, just that for
>> many applications (specially with huge matrices/arrays), pre-allocation does
>> make a huge difference, especially if we want to attract more people to
>> using numpy.
>> >
>> > The ufuncs and many scipy functions take a "out" parameter where you
>> > can specify a pre-allocated array.  It can be a little awkward writing
>> > expressions that way, but the capability is there.
>>
>> This is a slight digression: is there a way to have a out parameter
>> like semantics with numexpr. I have always used it as
>>
>> a[:] = numexpr(expression)
>>
>> But I dont think numexpr builds the value in place. Is it possible to
>> have side-effects with numexpr as opposed to obtaining values, for
>> example
>>
>> "a= a * b + c"
>>
>> The documentation is not clear about this. Oh and I do not find the
>> "out" parameter awkward at all. Its very handy. Furthermore, if I may,
>> here is a request that the Blitz++ source be updated. Seems like there
>> is a lot of activity on the Blitz++ repository and weave is very handy
>> too and can be used as easily as numexpr.
>>
>
> In order to make sure the 1.6 nditer supports multithreading, I adapted
> numexpr to use it. The branch which does this is here:
>
> http://code.google.com/p/numexpr/source/browse/#svn%2Fbranches%2Fnewiter
>
> This supports out, order, and casting parameters, visible here:
>
>
> http://code.google.com/p/numexpr/source/browse/branches/newiter/numexpr/necompiler.py#615
>
> It's pretty much ready to go, just needs someone to do the release
> management.
>
> -Mark
>
> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20110729/092c6fb4/attachment.html>