The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: Second International Symposium on Quantitative Codesign of Supercomputers
Authors: Thomas Jakobsche and Florina M. Ciorba (University of Basel, Switzerland) and Nicolas Lachiche (University of Strasbourg)
Abstract: This work examines the challenges and opportunities of using Machine Learning (ML) for Monitoring and Operational Data Analytics (MODA) in the context of Quantitative Codesign of Supercomputers (QCS). MODA is employed to gain insights into the behavior of current High Performance Computing (HPC) systems to improve system efficiency, performance, and reliability (e.g. through optimizing cooling infrastructure, job scheduling, and application parameter tuning). In this work, we take the position that QCS in general, and MODA in particular, require close exchange with the ML community to realize the full potential of data-driven analysis for the benefit of existing and future HPC systems. This exchange will facilitate identifying the appropriate ML methods to gain insights into current HPC systems and to go beyond expert-based knowledge and rules of thumb.
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