[Numpy-discussion] Using multiprocessing (shared memory) with numpy array multiplication
Fri Jun 10 08:23:10 CDT 2011
It may not work for you depending on your specific problem constraints, but
if you could flatten the arrays, then it would be a dot, and you could maybe
compute multiple such dot products by storing those flattened arrays into a
2011/6/10 Brandt Belson <firstname.lastname@example.org>
> Thanks for getting back to me.
> I'm doing element wise multiplication, basically innerProduct =
> numpy.sum(array1*array2) where array1 and array2 are, in general,
> multidimensional. I need to do many of these operations, and I'd like to
> split up the tasks between the different cores. I'm not using numpy.dot, if
> I'm not mistaken I don't think that would do what I need.
> Thanks again,
> Message: 1
>> Date: Thu, 09 Jun 2011 13:11:40 -0700
>> From: Christopher Barker <Chris.Barker@noaa.gov>
>> Subject: Re: [Numpy-discussion] Using multiprocessing (shared memory)
>> with numpy array multiplication
>> To: Discussion of Numerical Python <email@example.com>
>> Message-ID: <4DF128FC.firstname.lastname@example.org>
>> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>> Not much time, here, but since you got no replies earlier:
>> > > I'm parallelizing some code I've written using the built in
>> > multiprocessing
>> > > module. In my application, I need to multiply many large arrays
>> > together
>> is the matrix multiplication, or element-wise? If matrix, then numpy
>> should be using LAPACK, which, depending on how its built, could be
>> using all your cores already. This is heavily dependent on your your
>> numpy (really the LAPACK it uses0 is built.
>> > > and
>> > > sum the resulting product arrays (inner products).
>> are you using numpy.dot() for that? If so, then the above applies to
>> that as well.
>> I know I could look at your code to answer these questions, but I
>> thought this might help.
>> Christopher Barker, Ph.D.
>> Emergency Response Division
>> NOAA/NOS/OR&R (206) 526-6959 voice
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