
On 12/29/11 10:37 PM, Jaidev Deshpande wrote:
Hi!
Along with test coverage, have any of you considered any systematic monitoring of NumPy performance?
I'm mildly obsessed with performance and benchmarking of NumPy. I used to use a lot of MATLAB until a year back and I tend to compare Python performance with it all the time. I generally don't feel happy until I'm convinced that I've extracted the last bit of speed out of my Python code.
I think the generalization of this idea is more or less equivalent to performance benchmarking. Of course, I know there's a lot more than 'MATLAB vs Python' to it. I'd be more than happy to be involved. GSoC or otherwise.
Where do I start?
We've recently had a discussion about more intelligent timeit commands and timing objects in Python/Sage. People here might find the discussion interesting, and it might also be interesting to collaborate on code. The basic idea was a much smarter timeit command that uses more intelligent statistics and presents a much more comprehensive look at the timing information. Here is the discussion: https://groups.google.com/forum/#!topic/sage-devel/8lq3twm9Olc Here is our ticket tracking the issue: http://trac.sagemath.org/sage_trac/ticket/12168 Here are some examples of the analysis: http://sagenb.org/home/pub/3857/ I've CCd the sage-devel list as well, which is where our discussion happened. Thanks, Jason