Data Analysis for Improving High Performance Computing Operations and Research. An Eucor Seed Money Project

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dc.contributor.author Ciorba, Florina M.
dc.contributor.author Schneider, Gerhard
dc.contributor.author Suchodoletz, Dirk von
dc.contributor.author Cavelan, Aurélien
dc.contributor.author Sengstag, Thierry
dc.contributor.author Gless, Sabine
dc.contributor.author Lachiche, Nicolas
dc.contributor.author Samet, Ahmed
dc.date.accessioned 2019-04-09T13:21:36Z
dc.date.available 2019-04-09T13:21:36Z
dc.date.issued 2019-04
dc.identifier.other 166885046X
dc.identifier.uri http://hdl.handle.net/10900/87656
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-876560 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-29042
dc.description.abstract This work addresses the challenges associated with analysis of data generated by high performance computing (HPC) systems under data protection and privacy requirements. The HPC systems are the workhorse of simulation science, enabling unique insights across many disciplines (climate modeling, life sciences, weather forecast, etc.). System monitoring and analysis of monitoring data are highly significant for the efficient operation and research in performance optimization of HPC systems. Such systems generate various and large volumes of data as they operate, constituting a case of Big Data that challenges key data protection and privacy principles. This paper describes the Data Analysis for Improving High Performance Computing Operations and Research (DA-HPC-OR) project funded through the Eucor - The European Campus EVTZ via the Seed Money program1. The main goal in this project is the analysis of data collected since July 2016 on the HPC system (NEMO) at the University of Freiburg in order to improve their research and operations activities. Data collected on the sciCORE cluster in Basel will be used to validate the knowledge extracted from NEMO. This knowledge will be used to improve the monitoring, operational, and research activities of the three HPC systems (Freiburg, Basel, and Strasbourg). Data protection requires legal monitoring the relevant Swiss, German, and EU legislation. Compliance with such laws will be ensured via data de-identification and anonymization prior to analysis. We leverage the HPC, legal, and data analysis expertise of the consortium to develop solutions that can be transferred to other Eucor members at no additional legislative inquiries or overheads. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.publisher Universität Tübingen de_DE
dc.subject.classification Hochleistungsrechnen de_DE
dc.subject.ddc 004 de_DE
dc.subject.other High Performance Computing en
dc.subject.other bwHPC Symposium de_DE
dc.subject.other Data analysis en
dc.subject.other HPC operations and research en
dc.subject.other HPC monitoring en
dc.subject.other Data protection and privacy en
dc.subject.other EUCOR en
dc.title Data Analysis for Improving High Performance Computing Operations and Research. An Eucor Seed Money Project en
dc.type ConferencePaper de_DE
utue.publikation.fachbereich Informatik de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
utue.opus.portal bwhpc5 de_DE

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