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A Visual Analytics Method for Time-Series Log Data Using Multiple Dimensionality Reduction
Kobe University.
Kobe University.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
RIKEN R-CCS.
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2022 (English)In: Journal of Advanced Simulation in Science and Engineering, E-ISSN 2188-5303, Vol. 9, no 2, p. 206-219Article in journal (Refereed) Published
Abstract [en]

The size and complexity of supercomputer systems and their power and cooling facilities have continuously increased, thus posing additional challenge for long-term and stable operation. Supercomputers are shared computational resources and usually operate with different computational workloads at different locations (space) and timings (time). Better understanding of the supercomputer systems heat generation and cooling behavior is highly desired from the facility operational side for decision making and optimization planning. In this work, we present a dimensionality reduction-based visual analytics method for time-series log data, from supercomputer system and its facility, to capture characteristic spatio-temporal features and behaviors during the operation.

Place, publisher, year, edition, pages
JAPAN SOC SIMULATION TECHNOLOGY-JSST , 2022. Vol. 9, no 2, p. 206-219
Keywords [en]
Visual analytics; time-series log data; dimensionality reduction; high performance computing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-187947DOI: 10.15748/jasse.9.206ISI: 000842936000001OAI: oai:DiVA.org:liu-187947DiVA, id: diva2:1692045
Note

Funding: JSPS KAKENHI [20H04194, 21H04903]

Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2023-09-19

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Fujiwara, Takanori

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  • nn-NB
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Output format
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  • asciidoc
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