Performance of Python runtimes on a non-numeric scientific code

The Python library FatGHol [FatGHoL] used in [Murri2012] to reckon the rational homology of the moduli space of Riemann surfaces is an example of a non-numeric scientific code: most of the processing it does is generating graphs (represented by complex Python objects) and computing their isomorphisms (a triple of Python lists; again a nested data structure). These operations are repeated many times over: for example, the spaces M0,6 and M1,4 are triangulated by 4’583’322 and 747’664 graphs, respectively. This is an opportunity for every Python runtime to prove its strength in optimization. The purpose of this experiment was to assess the maturity of alternative Python runtimes, in terms of: compatibility with the language as implemented in CPython 2.7, and performance speedup. This paper compares the results and experiences from running FatGHol with different Python runtimes: CPython 2.7.5, PyPy 2.1, Cython 0.19, Numba 0.11, Nuitka 0.4.4 and Falcon.