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Python Workflows on HPC Systems

  • The recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multiuser environments or parallel programming - making it quite challenging to maintain stable and secureThe recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multiuser environments or parallel programming - making it quite challenging to maintain stable and secure Python workflows on a HPC system. In this paper, we analyze the key problems induced by the usage of Python on HPC clusters and sketch appropriate workarounds for efficiently maintaining multi-user Python software environments, securing and restricting resources of Python jobs and containing Python processes, while focusing on Deep Learning applications running on GPU clusters.show moreshow less

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Metadaten
Author:Janis KeuperORCiDGND, Dominik Straßel, Philipp Reusch
Creating Corporation:IEEE
Year of Publication:2020
ISBN:978-0-7381-1086-8 (digital)
ISBN:978-0-7381-1087-5 (Print on Demand)
Language:English
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Parent Title (English):Conference Proceedings: PYHPC 2020
First Page:32
Last Page:40
Document Type:Conference Proceeding
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2021/01/13
Licence (German):License LogoEs gilt das UrhG
Note:
Konferenz: 2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC)
Nov. 13 2020, GA, USA
DOI:https://doi.org/10.1109/PyHPC51966.2020.00009