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PyOMP: Multithreaded Parallel Programming in Python

Journal Article · · Computing in Science and Engineering
 [1];  [1];  [2]
  1. Intel Corporation, Hillsboro, OR (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
+ Show Author Affiliations
We know that Python is a widely used language in scientific computing. When the goal is high performance, however, Python lags far behind low-level languages such as C and Fortran. To support applications that stress performance, Python needs to access the full capabilities of modern CPUs. That means support for parallel multithreading. In this paper, we describe PyOMP, a system that enables OpenMP in Python. Programmers write code in Python with OpenMP, Numba generates code that compiles to LLVM, and the resulting programs run with performance that approaches that from code written with C and OpenMP. In this paper we provide an update on the PyOMP project and explain how to install it and use it to write parallel multithreaded code in Python.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1843562
Report Number(s):
LLNL-JRNL--829084; 1044930
Journal Information:
Computing in Science and Engineering, Journal Name: Computing in Science and Engineering Journal Issue: 6 Vol. 23; ISSN 1521-9615
Publisher:
IEEE Computer SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (2)

Numba: a LLVM-based Python JIT compiler conference January 2015
Multithreaded parallel Python through OpenMP support in Numba conference January 2021

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97 MATHEMATICS AND COMPUTING