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Title
Japanese: 
English:Migrating Legacy Fortran to Python While Retaining Fortran-Level Performance Through Transpilation and Type Hints 
Author
Japanese: BYSIEK MATEUSZ JAROSLAW, DROZD Aleksandr, 松岡 聡.  
English: Mateusz Bysiek, Aleksandr Drozd, Satoshi Matsuoka.  
Language English 
Journal/Book name
Japanese: 
English:Proceedings of the 6th Workshop on Python for High-Performance and Scientific Computing 
Volume, Number, Page         pp. 9-18
Published date Nov. 14, 2016 
Publisher
Japanese: 
English:IEEE Press 
Conference name
Japanese: 
English:6th Workshop on Python for High-Performance and Scientific Computing 
Conference site
Japanese: 
English:Salt Lake City 
Official URL http://ieeexplore.ieee.org/document/7836839
 
DOI https://doi.org/10.1109/PyHPC.2016.006
Abstract We propose a method of accelerating Python code by just-in-time compilation leveraging type hints mechanism introduced in Python 3.5. In our approach performance-critical kernels are expected to be written as if Python was a strictly typed language, however without the need to extend Python syntax. This approach can be applied to any Python application, however we focus on a special case when legacy Fortran applications are automatically translated into Python for easier maintenance. We developed a framework implementing two-way transpilation and achieved performance equivalent to that of Python manually translated to Fortran, and better than using other currently available JIT alternatives (up to 5x times faster than Numba in some experiments).

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