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DTSTART;TZID=America/New_York:20241118T165500
DTEND;TZID=America/New_York:20241118T171200
UID:submissions.supercomputing.org_SC24_sess761_ws_hppss105@linklings.com
SUMMARY:PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Pyth
 on
DESCRIPTION:Giorgis Georgakoudis (Lawrence Livermore National Laboratory (
 LLNL)), Todd Anderson (Intel Corporation), Stuart Archibald (The Numba pro
 ject), Bronis de Supinski (Lawrence Livermore National Laboratory (LLNL)),
  and Timothy Mattson (Human Learning Group)\n\nPython is the most popular 
 programming language.  OpenMP is the most\npopular parallel programming AP
 I. Projecting OpenMP into Python\nwill help expand the HPC community.  We 
 \ncall our Python-based OpenMP system PyOMP.\n\nIn this short paper we des
 cribe PyOMP and \nits use for parallel programming\nfor CPUs and GPUs.  We
  describe its implementation through the well\nknown Numba just-in-time (J
 IT) compiler and how to install PyOMP\non your own systems.   We provide s
 ome performance results suggesting\nperformance on par with that from C an
 d OpenMP, but our focus here\nis not detailed benchmarking. We leave that 
 to other papers.  Our\ngoal here is to show how to use PyOMP so we can gro
 w the PyOMP community.\n\nTag: Applications and Application Frameworks, Ar
 tificial Intelligence/Machine Learning, Parallel Programming Methods, Mode
 ls, Languages and Environments\n\nRegistration Category: Workshop Reg Pass
 \n\nSession Chairs: Sunita Chandrasekaran (University of Delaware), Sam Fo
 reman (Argonne National Laboratory (ANL)), Daniel Margala (Lawrence Berkel
 ey National Laboratory (LBNL)), and Pete Mendygral (Hewlett Packard Enterp
 rise (HPE))\n\n
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