[Numpy-discussion] NumPy EIG much slower than MATLAB EIG
Mon Apr 2 11:36:20 CDT 2012
numpy.random are not optimized. If matlab use the random number from
mkl, they will be much faster.
On Mon, Apr 2, 2012 at 12:04 PM, David Cournapeau <email@example.com> wrote:
> On Mon, Apr 2, 2012 at 4:45 PM, Chris Barker <firstname.lastname@example.org> wrote:
>> On Mon, Apr 2, 2012 at 2:25 AM, Nathaniel Smith <email@example.com> wrote:
>> > To see if this is an effect of numpy using C-order by default instead of
>> > Fortran-order, try measuring eig(x.T) instead of eig(x)?
>> Just to be clear, .T re-arranges the strides (Making it Fortran
>> order), butyou'll have to make sure your ariginal data is the
>> transpose of whatyou want.
>> I posted this on slashdot, but for completeness:
>> the code posted on slashdot is also profiling the random number
>> generation -- I have no idea how numpy and MATLAB's random number
>> generation compare, nor how random number generation compares to
>> eig(), but you should profile them independently to make sure.
> While this is true, the cost is most likely negligeable compared to the cost
> of eig (unless something weird is going on in random as well).
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