Language-Agnostic Optimization and Parallelization for Interpreted Languages

Michelle Mills Strout, Saumya Debray, Kate Isaacs, Barbara Kreaseck, Julio Cárdenas-Rodríguez, Bonnie Hurwitz, Kat Volk, Sam Badger, Jesse Bartels, Ian Bertolacci, Sabin Devkota, Anthony Encinas, Ben Gaska, Brandon Neth, Theo Sackos, Jon Stephens, Sarah Willer, Babak Yadegari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 parallelization; and second, even where such analysis is possible, a language-specific approach implies that each language would need its own parallelizing compiler and/or constructs, resulting in significant duplication of effort. The Science Up To Par project is investigating a radically different approach to this problem: automatic parallelization at the machine code level using trace information. The key to accomplishing this will be the static and dynamic analysis of executables and the reconstitution of such executables into parallel executables. The key insight is that with trace information it should be possible optimize out the interpreter and other dynamic features in a language-agnostic manner and create parallelized executables for multicore architectures. If successful, this can enable scientists to continue to develop in programming environments that most conveniently support their scientific exploration without paying the performance overheads currently associated with many such environments.

Original languageEnglish (US)
Title of host publicationLanguages and Compilers for Parallel Computing - 30th International Workshop, LCPC 2017, Revised Selected Papers
EditorsLawrence Rauchwerger
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030352240
StatePublished - 2019
Event30th Workshop on Languages and Compilers for Parallel Computing, LCPC 2017 - College Station, United States
Duration: Oct 11 2017Oct 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11403 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th Workshop on Languages and Compilers for Parallel Computing, LCPC 2017
Country/TerritoryUnited States
CityCollege Station

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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