Characterizing HPC Performance Variation with Monitoring and Unsupervised Learning-Reference-Cited by-同舟云学术

Characterizing HPC Performance Variation with Monitoring and Unsupervised Learning

Published:2020 Issue: Volume: Page:280-292
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Ozer Gence,Netti Alessio,Tafani Daniele,Schulz Martin

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-59851-8_18

Reference21 articles.

1. Lecture Notes in Computer Science;E Ates,2018

2. Baseman, E., Blanchard, S., DeBardeleben, N., Bonnie, A., et al.: Interpretable anomaly detection for monitoring of high performance computing systems. In: Proceedings of the ACM SIGKDD 2016 Workshops (2016)

3. Borghesi, A., Libri, A., Benini, L., Bartolini, A.: Online anomaly detection in HPC systems. In: Proceedings of AICAS 2019, pp. 229–233. IEEE (2019)

4. Bourassa, N., Johnson, W., Broughton, J., Carter, D.M., et al.: Operational data analytics: optimizing the national energy research scientific computing center cooling systems. In: Proceedings of the ICPP 2019 Workshops, pp. 5:1–5:7. ACM (2019)

5. Bourassa, N., Ott, M.: EEHPCWG operational data analytics survey (2019). https://eehpcwg.llnl.gov/assets/sc19_11_425_525_operational_data_analytics_ott_bourassa.pdf

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