Scalable parallel programming in python with PArsL

Yadu Babuji, Anna Woodard, Zhuozhao Li, Daniel S. Katz, Ben Clifford, Ian Foster, Michael Wilde, Kyle Chard

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

Abstract

Python is increasingly the lingua franca of scientific computing. It is used as a higher level language to wrap lower-level libraries and to compose scripts from various independent components. However, scaling and moving Python programs from laptops to supercomputers remains a challenge. Here we present Parsl, a parallel scripting library for Python. Parsl makes it straightforward for developers to implement parallelism in Python by annotating functions that can be executed asynchronously and in parallel, and to scale analyses from a laptop to thousands of nodes on a supercomputer or distributed system. We examine how Parsl is implemented, focusing on syntax and usage. We describe two scientific use cases in which Parsl’s intuitive and scalable parallelism is used.

Original languageEnglish (US)
Title of host publicationProceedings of the Practice and Experience in Advanced Research Computing
Subtitle of host publicationRise of the Machines (Learning), PEARC 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450372275
DOIs
StatePublished - Jul 28 2019
Event2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019 - Chicago, United States
Duration: Jul 28 2019Aug 1 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019
CountryUnited States
CityChicago
Period7/28/198/1/19

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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