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. <br /> <br /> The talk will present both how Pythran works and how to make it work!
