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 matrix.
-=- Olivier 2011/6/10 Brandt Belson <[email protected]> > 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] >> >> >> > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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