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Pythran: Static Compilation of Parallel Scientific Kernels


As the use of Python coupled to Numpy/SciPy for numerical computation increases, many tools to optimize performance have emerged. Indeed, this duo has relatively poor performance when compared to scientific codes written in legacy languages like C or Fortran. Cython, Numba, numexpr and parakeet belongs to this new compiler ecosystem. And so does Pythran, a Python to C++11 translator for scientific Python.

Pythran uses a static compilation approach a la Cython, but with full backward compatibility with Python. It does not only turns Python code into C++ code, it also performs Python/Numpy specific optimizations, generates calls to a parallel, vectorized runtime and makes it possible to write OpenMP annotation in the original Python code. It supports a large range of Numpy functions and can combine them in efficient ways: it can optimize high­level modern Python/Numpy codes and not only Fortran­ with­ a­ Python­ flavor ones.

This talk presents the existing compilation approach and optimization opportunities for numerical Python, their strengths and weaknesses, then focus on the specificities of the Pythran compiler.


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