A Monday 14 December 2009 18:20:32 Jasper van de Gronde escrigué: > 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.
I don't think so. Your machine is slow for nowadays standards, so the 5x slowness should be due to python/numpy overhead, but unfortunately nothing that could be solved magically by using a newer python/numpy version. -- Francesc Alted _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
