A Comprehensive Informative Metric for Summarizing HPC System Status

Yawei Hui; Byung Hoon Park; Christian Engelmann

doi:10.1109/LDAV.2018.8739234

A Comprehensive Informative Metric for Summarizing HPC System Status

Yawei Hui, Byung Hoon Park, Christian Engelmann

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

Abstract

It remains a major challenge to effectively summarize and visualize in a comprehensive form the status of a complex computer system, such as the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). In the ongoing research highlighted in this poster, we present system information entropy (SIE), a newly developed system metric that leverages the powers of traditional machine learning techniques and information theory. By compressing the multi-variant multi-dimensional event information recorded during the operation of the targeted system into a single time series of SIE, we demonstrate that the historical system status can be sensitively summarized in form of SIE and visualized concisely and comprehensively.

Original language	English
Title of host publication	2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	102-103
Number of pages	2
ISBN (Electronic)	9781538668733
DOIs	https://doi.org/10.1109/LDAV.2018.8739234
State	Published - Oct 2018
Event	8th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2018 - Berlin, Germany Duration: Oct 21 2018 → …

Publication series

Name	2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018

Conference

Conference	8th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2018
Country/Territory	Germany
City	Berlin
Period	10/21/18 → …

Funding

This work was sponsored by the U.S. Department of Energy's Office of Advanced Scientific Computing Research, program manager Dr. Lucy Nowell. This work was also supported by the Compute and Data Environment for Science (CADES) facility and the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.

Funders	Funder number
Compute and Data Environment for Science
U.S. Department of Energy	DE-AC05-00OR22725
Advanced Scientific Computing Research
Oak Ridge National Laboratory

Keywords

General and reference-Metrics
Human-centered computing-Visual analytics
Mathematics of computing-Time series analysis

Access to Document

10.1109/LDAV.2018.8739234

Cite this

Hui, Y., Park, B. H., & Engelmann, C. (2018). A Comprehensive Informative Metric for Summarizing HPC System Status. In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018 (pp. 102-103). Article 8739234 (2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2018.8739234

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abstract = "It remains a major challenge to effectively summarize and visualize in a comprehensive form the status of a complex computer system, such as the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). In the ongoing research highlighted in this poster, we present system information entropy (SIE), a newly developed system metric that leverages the powers of traditional machine learning techniques and information theory. By compressing the multi-variant multi-dimensional event information recorded during the operation of the targeted system into a single time series of SIE, we demonstrate that the historical system status can be sensitively summarized in form of SIE and visualized concisely and comprehensively.",

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series = "2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018",

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Hui, Y, Park, BH & Engelmann, C 2018, A Comprehensive Informative Metric for Summarizing HPC System Status. in 2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018., 8739234, 2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018, Institute of Electrical and Electronics Engineers Inc., pp. 102-103, 8th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2018, Berlin, Germany, 10/21/18. https://doi.org/10.1109/LDAV.2018.8739234

A Comprehensive Informative Metric for Summarizing HPC System Status. / Hui, Yawei; Park, Byung Hoon ; Engelmann, Christian.
2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 102-103 8739234 (2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018).

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

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Hui Y, Park BH , Engelmann C. A Comprehensive Informative Metric for Summarizing HPC System Status. In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 102-103. 8739234. (2018 IEEE 8th Symposium on Large Data Analysis and Visualization, LDAV 2018). doi: 10.1109/LDAV.2018.8739234

A Comprehensive Informative Metric for Summarizing HPC System Status

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