Hi,

There have been optimization in numpy too from memory. Can you tell

witch version of numpy was installed on each just to make sure this

isn't the reason.

For example, the speed up from numpy.dot() could be due to that.

Fred

p.s. I'm not working with Enthought, but I follow the numpy

development for our project Theano.

On Wed, Sep 25, 2013 at 7:49 AM, Vutshi <

[hidden email]> wrote:

> Dear Enthought,

>

> recently, I did a comparison of MKL 10.3 from EPD and MKL 11 from Anaconda.

> On my laptop with core i7 (sandy bridge) the results are as follows

>

> import numpy as np

> N = 3000

> a = np.random.rand(N, N)

> c = np.random.rand(N, N)

>

> %timeit np.dot(a,c)

> MKL 10.3 gives 2.41 s

> MKL 11 gives 1.22 s !!!

>

> %timeit np.linalg.eig(a)

> MKL 10.3 gives 35.3 s

> MKL 11 gives 29.3 s

>

> %timeit np.linalg.eigh(a + a.T)

> MKL 10.3 gives 8.45 s

> MKL 11 gives 6.28 s

>

> %timeit np.linalg.inv(a)

> MKL 10.3 gives 3.77 s

> MKL 11 gives 2.27 s

>

> %timeit np.linalg.pinv(a)

>

> MKL 10.3 gives 25.7 s

> MKL 11 gives 19.5 s

>

> This is quite a good improvement I would say and newer CPUs should gain even

> more. I'd like to see it in EPD!

>

>

>

> --

> View this message in context:

http://enthought-dev.117412.n3.nabble.com/MKL-11-in-EPD-tp4026502p4026679.html> Sent from the Enthought Dev mailing list archive at Nabble.com.

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