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UID:submissions.supercomputing.org_SC24_sess421_tut122@linklings.com
SUMMARY:PyOMP: Parallel Programming in Python with OpenMP
DESCRIPTION:Timothy Mattson (Human Learning Group), Giorgis Georgakoudis (
 Lawrence Livermore National Laboratory (LLNL)), and Todd Anderson (Intel C
 orporation)\n\nPeople know Python. In many cases, it is their primary prog
 ramming language.  When they move to HPC, however, they have to translate 
 everything into a low-level language such as C.   This creates a barrier o
 f entry to HPC.  Wouldn’t it be nice if people could “keep everything in P
 ython”?\nIn this tutorial we present a system for parallel programming in 
 Python.  We use OpenMP directives (identical to those used in C/Fortran) e
 xpressed as strings inside Python with statements coupled to the Numba JIT
  compiler. Numba drops us into LLVM with built-in OpenMP support, resultin
 g in performance on par with that from C/OpenMP.    We call our system PyO
 MP.\nIn this tutorial we present PyOMP for both the CPU and the GPU.  We d
 escribe how to install PyOMP on a system and walk through the core design 
 patterns from HPC.  We run the tutorial in an interactive “demo-mode” as w
 e write PyOMP programs together.  Then, we will explore how our PyOMP prog
 rams map onto other well-known approaches to parallel programming in Pytho
 n.  By looking at how core patterns in PyOMP move between different progra
 mming models, we learn a great deal about those different programming mode
 ls and greatly deepen our understanding of PyOMP.\n\nTag: Applications and
  Application Frameworks, Parallel Programming Methods, Models, Languages a
 nd Environments\n\nRegistration Category: Tutorial Reg Pass\n\n
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