Seminar on Modeling May 2015 - Serge Guelton: Turning Python numerical kernels into parallel, vectorized native modules

Python is getting widely used to quickly prototype numerical kernels, thanks to the numpy/scipy/matplotlib/ipython team. But when it comes to performance, it still lags behind equivalent native code. The Pythran compiler proposes a solution to this problem, by statically compiling and optimizing high-level Python/numpy kernels into parallel, vectorized C++11 code. The leitmotiv is to take high-level Numpy code, without the need of explicit loops, and rely on the semantic of the numpy operations to generate efficient code.

The talk will present both how Pythran works and how to make it work!