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DTSTAMP:20181221T160727Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181112T103000
DTEND;TZID=America/Chicago:20181112T110000
UID:submissions.supercomputing.org_SC18_sess172_ws_phpsc101@linklings.com
SUMMARY:AutoParallel: A Python Module for Automatic Parallelization and Di
 stributed Execution of Affine Loop Nests
DESCRIPTION:Workshop\nParallel Application Frameworks, Reproducibility, Sc
 ientific Computing, Workshop Reg Pass\n\nAutoParallel: A Python Module for
  Automatic Parallelization and Distributed Execution of Affine Loop Nests\
 n\nRamon-Cortes, Amela, Ejarque, Clauss, Badia\n\nThe latest improvements 
 in programming languages, programming models, and frameworks have focused 
 on abstracting the users from many programming issues. Among others, recen
 t programming frameworks include simpler syntax, automatic memory manageme
 nt and garbage collection, simplifies code re-usage through library packag
 es, and easily configurable tools for deployment. For instance, Python has
  raised to the top of the list of the programming languages due to the sim
 plicity of its syntax, while still achieving a good performance even being
  an interpreted language. Moreover, the community has helped to develop a 
 large number of libraries and modules, tuning the most commonly used to ob
 tain great performance.\n\nHowever, there is still room for improvement wh
 en preventing users from dealing directly with distributed and parallel is
 sues. This paper proposes and evaluates AutoParallel, a Python module to a
 utomatically find an appropriate task-based parallelization of affine loop
  nests to execute them in parallel in a distributed computing infrastructu
 re. This parallelization can also include the building of data blocks to i
 ncrease task granularity in order to achieve a good execution performance.
  Moreover, AutoParallel is based on sequential programming and only contai
 ns a small annotation in the form of a Python decorator so that anyone wit
 h little programming skills can scale up an application to hundreds of cor
 es.
URL:https://sc18.supercomputing.org/presentation/?id=ws_phpsc101&sess=sess
 172
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