A Conceptual Framework for HPC Operational Data Analytics

Conference · Wed Sep 01 04:00:00 EDT 2021

DOI:https://doi.org/10.1109/Cluster48925.2021.00086· OSTI ID:1820791

Netti, Alessio ^[1]; Shin, Woong ^[2]; Ott, Michael ^[3]; Wilde, Torsten ^[4]; Bates, Natalie ^[5]

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1. LRZ
2. ORNL
3. Leibniz Supercomputing Centre
4. Hewlett Packard Enterprise
5. Energy Efficient HPC Working Group

This paper provides a broad framework for under- standing trends in Operational Data Analytics (ODA) for High- Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four- pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading- edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1820791
• Country of Publication: United States
• Language: English