Thanks Chad for the explanation on those details. I am new to python and I still have a lot to learn, this was very useful. Now I get similar results between matlab and numpy when I re-use the memory allocated for m with 'm -= 0.5'.
However, if I don't, I obtain this 4x penalty with numpy, even with the 8092x8092 array. Would it be possible to do k = m - 0.5 and pre-alllocate k such that python does not have to waste time on that? Another interesting case is something like k = m + n + p, which I guess should be run with numexpr in order to accelerate it. Regarding operator evaluation, I thought of the same thing, and it is what would happen in other languages as well if no expression 'templates' ( such as what the Eigen library uses ). I will look at numexpr, I hope I can replace all my matlab image processing and computational needs with python, and interface with C++ code that uses Eigen if I need an extra speed boost. Right now I am trying spyder as a debugger, which looks very nice. If a good debugger is available, it could totally replace matlab/octave for researchers/engineers/etc for some specific needs. I will try numexpr now to see the performance gain. Thanks to all that replied to this topic, it was very useful. On Tue, Jul 19, 2011 at 10:11 PM, Nadav Horesh <nad...@visionsense.com>wrote: > For such expressions you should try numexpr package: It allows the same > type of optimisation as Matlab does: run a single loop over the matrix > elements instead of repetitive loops and intermediate objects creation. > > Nadav > > > Besides the matlab/numpy comparison, I think that there is an inherent > problem with how expressions are handled, in terms of efficiency. > > For instance, k = (m - 0.5)*0.3 takes 52msec average here (2000x2000 > array), while k = (m - 0.5)*0.3*0.2 takes 0.079, and k = (m - > 0.5)*0.3*0.2*0.1 > takes 101msec. > > Placing parentheses around the scalar multipliers shows that it seems to > have to do with how expressions are handled, is there sometihng that can >be > done about this so that numpy can deal with expressions rather than single > operations chained by python itself? Maybe I am missing the point as well. > > ---------------------- > Carlos Becker > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
