Hi, 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, Brandt
Message: 1 > Date: Thu, 09 Jun 2011 13:11:40 -0700 > From: Christopher Barker <[email protected]> > Subject: Re: [Numpy-discussion] Using multiprocessing (shared memory) > with numpy array multiplication > To: Discussion of Numerical Python <[email protected]> > Message-ID: <[email protected]> > 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. > > -Chris > > > > > > -- > Christopher Barker, Ph.D. > Oceanographer > > Emergency Response Division > NOAA/NOS/OR&R (206) 526-6959 voice > 7600 Sand Point Way NE (206) 526-6329 fax > Seattle, WA 98115 (206) 526-6317 main reception > > [email protected] > > >
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