On Mon, Apr 2, 2012 at 2:25 AM, Nathaniel Smith <[email protected]> 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. -Chris > -n > > On Apr 1, 2012 2:28 PM, "Kamesh Krishnamurthy" <[email protected]> wrote: >> >> Hello all, >> >> I profiled NumPy EIG and MATLAB EIG on the same Macbook pro, and both were >> linking to the Accelerate framework BLAS. NumPy turns out to be ~4x slower. >> I've posted details on Stackoverflow: >> http://stackoverflow.com/q/9955021/974568 >> >> Can someone please let me know the reason for the performance gap? >> >> Thanks, >> Kamesh >> >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- 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
