arXiv:1404.6388cs.MS
Performance of Python runtimes on a non-numeric scientific code
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
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 and 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.
On a tangentially related note: here is a comparison of various Python runtimes (interpreters and compilers including Nuitka and PyPy) on a fairly complex scientific code: http://arxiv.org/abs/1404.6388 The shootout, however, is from August 2013 so getting a bit old by now.
HerrMonnezzaView on HNParent context