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DTSTART;TZID=America/Chicago:20181112T103000
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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:Cristian Ramon-Cortes, Ramon Amela, and Jorge Ejarque (Barcelo
 na Supercomputing Center); Philippe Clauss (French Institute for Research 
 in Computer Science and Automation (INRIA), University of Strasbourg); and
  Rosa M. Badia (Barcelona Supercomputing Center, Spanish National Research
  Council)\n\nThe latest improvements in programming languages, programming
  models, and frameworks have focused on abstracting the users from many pr
 ogramming issues. Among others, recent programming frameworks include simp
 ler syntax, automatic memory management and garbage collection, simplifies
  code re-usage through library packages, and easily configurable tools for
  deployment. For instance, Python has raised to the top of the list of the
  programming languages due to the simplicity of its syntax, while still ac
 hieving a good performance even being an interpreted language. Moreover, t
 he community has helped to develop a large number of libraries and modules
 , tuning the most commonly used to obtain great performance.\n\nHowever, t
 here is still room for improvement when preventing users from dealing dire
 ctly with distributed and parallel issues. This paper proposes and evaluat
 es AutoParallel, a Python module to automatically find an appropriate task
 -based parallelization of affine loop nests to execute them in parallel in
  a distributed computing infrastructure. This parallelization can also inc
 lude the building of data blocks to increase task granularity in order to 
 achieve a good execution performance. Moreover, AutoParallel is based on s
 equential programming and only contains a small annotation in the form of 
 a Python decorator so that anyone with little programming skills can scale
  up an application to hundreds of cores.\n\nTag: Parallel Application Fram
 eworks, Reproducibility, Scientific Computing\n\nRegistration Category: Wo
 rkshop Reg Pass\n\n
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