The project page has a patch manager for contributions. Please note that Travis is in the middle of a substantial reimplementation and so I think nobody would want to do a lot of optimizing right now.
-----Original Message----- From: numpy-discussion-admin@lists.sourceforge.net [mailto:numpy-discussion-admin@lists.sourceforge.net]On Behalf Of Pete Shinners Sent: Saturday, July 08, 2000 12:56 AM To: numpy-discussion@sourceforge.net Subject: [Numpy-discussion] Optimizing Numpy
i've been throwing my hand and getting more speed out of numpy. i've birthed a little fruit from my efforts. my area of use is specifically with 2D arrays with image info.
anyways, i've attached an 'arrayobject.c' file that is from the 15.3 release and optimized.
in my test case the code ran about twice the speed of the original 15.3 release. i went and tested out other uses and found on a 20% speedup pretty consistent. (for example, i cranked the mandelbrot demo resolution to 320 x 200 and removed the 'print' command and it went from a runtime of 5.5 to 4.5)
i'm not sure how people 'officially' make contributions to the code, but i hope this is easy enough to merge. i also hope this is accepted (or at least reviewed) for inclusion in the next release.
optimizing further... i also plan on a few more optimizations. the least is going to be a 'C' version of 'arrayrange' and probably 'ones'. the current arrayrange is pretty slow (slower than the standard python 'range' in all my tests). the other optimization is a bit more drastic, and i'd like to hear feedback from more 'numpy experts' before making the change. in the file 'arraytypes.c' with all the arrays of conversion functions, i've found that the conversion routines are a little too 'elaborate'. these routines are only ever called from one line and the the two "increment/skip" arguments are always hardcoded one. there are two possible roads to speedup the conversion of array types. 1-- optimize all the conversion routines so they aren't so generic. this should be a pretty easy fix and should offer noticeable speed. 2-- do a better job of converting arrays. instead of creating a whole new array of the new type and simply copying that, create a conversion method that simply converts the data directly into the destination array. this would mean using all those conversion routines to their full power. this would offer more speed than the first option, but is also a lot more work
well, what do people think? my initial thought is to make a quick python script to take 'arraytypes.c' and convert all the functions to be a quicker version.
numpy is amazing, and i'm glad to get a chance to better it!