Paper Info
Title
pPython for Parallel Python Programming
Abstract
pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics (PGAS) on top of a simple file-based messaging library (PythonMPI) in pure Python. The core data structure in pPython is a distributed numerical array whose distribution onto multiple processors is specified with a ‘map’ construct. Communication operations between distributed arrays are abstracted away from the user and pPython transparently supports redistribution between any block-cyclic-overlapped distributions in up to four dimensions. pPython follows a SPMD (single program multiple data) model of computation. pPython runs on any combination of heterogeneous systems that support Python, including Windows, Linux, and MacOS operating systems. In addition to running transparently on single-node (e.g., a laptop), pPython provides a scheduler interface, so that pPython can be executed in a massively parallel computing environment. The initial implementation uses the Slurm scheduler. Performance of pPython on the HPC Challenge benchmark suite demonstrates both ease of programming and scalability.
Year
DOI
Venue
2022
10.1109/HPEC55821.2022.9926365
2022 IEEE High Performance Extreme Computing Conference (HPEC)
Keywords
DocType
ISSN
PGAS,Python,MPI,Parallel Programming
Conference
2377-6943
ISBN
Citations 
PageRank 
978-1-6654-9787-9
0
0.34
References 
Authors
5
21
Name
Order
Citations
PageRank
Chansup Byun172.49
William Arcand272.49
David Bestor372.15
Bill Bergeron400.68
Vijay Gadepally531.77
Michael Houle600.34
Matthew Hubbell772.15
Hayden Jananthan8144.78
Michael J. Jones911341927.21
Kurt Keville1000.34
Anna Klein114910.10
Peter Michaleas1221.39
Lauren Milechin1310216.45
Guillermo Morales1400.34
Julie Mullen1552.09
Andrew Prout1672.15
Albert Reuther17282.75
Antonio Rosa1872.83
Siddharth Samsi1931.52
Charles Yee2073.17
Jeremy Kepner2160661.58