Unveiling User Behavior on Summit Login Nodes as a User

Sean R. Wilkinson, Ketan Maheshwari, Rafael Ferreira da Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We observe and analyze usage of the login nodes of the leadership class Summit supercomputer from the perspective of an ordinary user—not a system administrator—by periodically sampling user activities (job queues, running processes, etc.) for two full years (2020–2021). Our findings unveil key usage patterns that evidence misuse of the system, including gaming the policies, impairing I/O performance, and using login nodes as a sole computing resource. Our analysis highlights observed patterns for the execution of complex computations (workflows), which are key for processing large-scale applications.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2022, 22nd International Conference, Proceedings
EditorsDerek Groen, Clélia de Mulatier, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Maciej Paszynski, Jack J. Dongarra
PublisherSpringer Science and Business Media Deutschland GmbH
Pages516-529
Number of pages14
ISBN (Print)9783031087509
DOIs
StatePublished - 2022
Event22nd Annual International Conference on Computational Science, ICCS 2022 - London, United Kingdom
Duration: Jun 21 2022Jun 23 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13350 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Annual International Conference on Computational Science, ICCS 2022
Country/TerritoryUnited Kingdom
CityLondon
Period06/21/2206/23/22

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Acknowledgments. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We acknowledge Suzanne Parete-Koon for early brainstorming of some of the ideas presented here. We thank Scott Atchley, Bronson Messer, and Sarp Oral for their thorough revision of this paper.

FundersFunder number
U.S. Department of EnergyDE-AC05-00OR22725
Office of Science

    Keywords

    • High Performance Computing
    • User behavior
    • Workload characterization

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