Usage Pattern Analysis for the Summit Login Nodes

Brett Eiffert; Chen Zhang

doi:10.1007/978-3-031-23606-8_21

Usage Pattern Analysis for the Summit Login Nodes

Brett Eiffert, Chen Zhang

Application Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit’s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.

Original language	English
Title of host publication	Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers
Editors	Kothe Doug, Geist Al, Swaroop Pophale, Hong Liu, Suzanne Parete-Koon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	329-344
Number of pages	16
ISBN (Print)	9783031236051
DOIs	https://doi.org/10.1007/978-3-031-23606-8_21
State	Published - 2022
Event	Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022 - Virtual, Online Duration: Aug 24 2022 → Aug 25 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1690 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022
City	Virtual, Online
Period	08/24/22 → 08/25/22

Funding

Acknowledgements. 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. 6th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC22) This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan).

Funders	Funder number
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science

Keywords

HPC
Performance analysis
Summit

Access to Document

10.1007/978-3-031-23606-8_21

Cite this

Eiffert, B., & Zhang, C. (2022). Usage Pattern Analysis for the Summit Login Nodes. In K. Doug, G. Al, S. Pophale, H. Liu, & S. Parete-Koon (Eds.), Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers (pp. 329-344). (Communications in Computer and Information Science; Vol. 1690 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23606-8_21

Eiffert, Brett ; Zhang, Chen. / Usage Pattern Analysis for the Summit Login Nodes. Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. editor / Kothe Doug ; Geist Al ; Swaroop Pophale ; Hong Liu ; Suzanne Parete-Koon. Springer Science and Business Media Deutschland GmbH, 2022. pp. 329-344 (Communications in Computer and Information Science).

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abstract = "High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit{\textquoteright}s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.",

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Eiffert, B & Zhang, C 2022, Usage Pattern Analysis for the Summit Login Nodes. in K Doug, G Al, S Pophale, H Liu & S Parete-Koon (eds), Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1690 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 329-344, Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Virtual, Online, 08/24/22. https://doi.org/10.1007/978-3-031-23606-8_21

Usage Pattern Analysis for the Summit Login Nodes. / Eiffert, Brett; Zhang, Chen.
Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. ed. / Kothe Doug; Geist Al; Swaroop Pophale; Hong Liu; Suzanne Parete-Koon. Springer Science and Business Media Deutschland GmbH, 2022. p. 329-344 (Communications in Computer and Information Science; Vol. 1690 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Eiffert B, Zhang C. Usage Pattern Analysis for the Summit Login Nodes. In Doug K, Al G, Pophale S, Liu H, Parete-Koon S, editors, Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 329-344. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-23606-8_21