On Mon, Jun 02, 2014 at 12:40:48PM +0200, Ian Ozsvald wrote: > Hi Serge. I'm just back in the UK from teaching High Performance > Python to PhDs in Denmark last week. We covered Pythran (mainly > Cython, also PyPy and Numba). That's great news! Any feedback concerning Pythran? Can you share your materials? > For the above benchmarks what is the main reason for the speed > improvements over numpy? Is it because you're using locally defined > variants of the numpy functions? Is it because you have > parallelisation (there's no note about openmp/multi-core)? There are several reasons. (No parallelization involved) 1. Avoid temporary: several array expressions are computed lazily and merged in a single one 2. Faster Implementation of numpy functions: It's hard to say otherwise, but I have the feeling that some numpy functions are not as efficient as they could be. We have a raw C++ implementations for many of them and we sometime get a x2 speedup just by calling them. Just wait for the vectorized/ parallelized versions ;-) > Cheers, Ian. It's a pleasure to get some news! I still have some work to be done on the numpy-benchmarks, but I'll keep you informed once everything is ready for official disclosure! s.