2020/12/01 by Dominik Straßel, Strassel, Dominik, Philipp Reusch +3
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #Computational Physics and Python Applications #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Scientific Computing and Data Management #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2012.00365
openalex publication_date 2020/12/01 · openalex created_date 2020/12/07 · openalex updated_date 2026/07/28
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 multi-user 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.