Language-Agnostic Optimization and Parallelization for Interpreted Languages

@inproceedings{Strout2017LanguageAgnosticOA,
  title={Language-Agnostic Optimization and Parallelization for Interpreted Languages},
  author={Michelle Mills Strout and Saumya Debray and Kate Isaacs and Barbara Kreaseck and Julio C{\'a}rdenas-Rodŕıguez and Bonnie L. Hurwitz and Kat Volk and Sam Badger and Jesse Bartels and Ian J. Bertolacci and Sabin Devkota and Anthony Encinas and Ben Gaska and Brandon Neth and Theo Sackos and Jon Barton Stephens and Sarah Elizbeth Willer and Babak Yadegari},
  year={2017}
}
Scientists are increasingly turning to interpreted languages, such as Python, Java, R, Matlab, and Perl, to implement their data analysis algorithms. While such languages permit rapid software development, their implementations often run into performance issues that slow down the scientific process. Source-level approaches for parallelization are problematic for two reasons: first, many of the language features common to these languages can be challenging for the kinds of analyses needed for… CONTINUE READING

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