pPython for Parallel Python Programming

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...

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Vydané v:arXiv.org
Hlavní autori: Byun, Chansup, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Hayden Jananthan, Jones, Michael, Keville, Kurt, Klein, Anna, Michaleas, Peter, Milechin, Lauren, Morales, Guillermo, Mullen, Julie, Prout, Andrew, Reuther, Albert, Rosa, Antonio, Samsi, Siddharth, Yee, Charles, Kepner, Jeremy
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 31.08.2022
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Data structures
Programming languages
Python
Semantics
ISSN:2331-8422
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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.
AbstractList 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.
Author Prout, Andrew
Gadepally, Vijay
Klein, Anna
Morales, Guillermo
Houle, Michael
Bergeron, Bill
Byun, Chansup
Arcand, William
Hubbell, Matthew
Keville, Kurt
Reuther, Albert
Jones, Michael
Mullen, Julie
Michaleas, Peter
Kepner, Jeremy
Milechin, Lauren
Yee, Charles
Bestor, David
Hayden Jananthan
Rosa, Antonio
Samsi, Siddharth
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Semantics
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