Thomas Jakobsche
University of Basel
8 Papers
Thomas Jakobsche is an academic researcher from University of Basel. The author has contributed to research in topics: System monitoring & Fingerprint (computing). The author has co-authored 2 publications.
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Papers
Investigating HPC Job Resource Requests and Job Efficiency Reporting
Thomas Jakobsche,Nicolas Lachiche,Florina M. Ciorba +2 more
- 01 Jul 2023
TL;DR: This work analyzes almost 350’000 jobs collected over 2 months on a local university HPC cluster and places an emphasis on time limit accuracy, QoS characteristics, and reasons for long wait times, as well as how to engage users to improve resource requests.
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Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations
Francieli Zanon Boito,Jim Brandt,Valeria Cardellini,Philip Carns,Florina M. Ciorba,Hilary Egan,Ahmed Eleliemy,Ann Gentile,Thomas Gruber,Jeff Hanson,Utz-Uwe Haus,Kevin Huck,Thomas Ilsche,Thomas Jakobsche,Terry Jones,Sven Karlsson,Abdullah Mueen,Michael Ott,Tapasya Patki,Ivy Peng,Krishnan Raghavan,Stephen Simms,Kathleen Shoga,Michael Showerman,Devesh Tiwari,Torsten Wilde,Keiji Yamamoto +26 more
- 31 Oct 2023
TL;DR: This position paper seeks to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems.
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•Posted Content
An Execution Fingerprint Dictionary for HPC Application Recognition
TL;DR: In this paper, an Execution Fingerprint Dictionary (EFD) is proposed to store execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs.
An Execution Fingerprint Dictionary for HPC Application Recognition
Thomas Jakobsche,Nicolas Lachiche,Aurélien Cavelan,Florina M. Ciorba +3 more
- 01 Sep 2021
TL;DR: In this article, an Execution Fingerprint Dictionary (EFD) is proposed to store execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs.
Challenges and Opportunities of Machine Learning for Monitoring and Operational Data Analytics in Quantitative Codesign of Supercomputers
TL;DR: This work examines the challenges and opportunities of Machine Learning (ML) for Monitoring and Operational Data Analytics (MODA) in the context of Quantitative Codesign of Supercomputers (QCS) and formulate opportunities to bring ML expertise into QCS and facilitate close collaboration.