Francesc Alted wrote:
A Monday 14 December 2009 17:09:13 Francesc Alted escrigué:
The things seems to be worst than 1.6x times slower for numpy, as matlab orders arrays by column, while numpy order is by row. So, if we want to compare pears with pears:
For Python 600x200: Add a row: 0.113243 (1.132425e-05 per iter) For Matlab 600x200: Add a column: 0.021325 (2.132527e-006 per iter)
Mmh, I've repeated this benchmark on my machine and got:
In [59]: timeit E + Xi2[P/2] 100000 loops, best of 3: 2.8 µs per loop
that is, very similar to matlab's 2.1 µs and quite far from the 11 µs you are getting for numpy in your machine... I'm using a Core2 @ 3 GHz.
I'm using Python 2.6 and numpy 1.4.0rc1 on a Core2 @ 1.33 GHz (notebook). I'll have a look later to see if upgrading Python to 2.6.4 makes a difference.