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This work examines the challenges and opportunities of Machine Learning (ML)for Monitoring and Operational Data Analytics (MODA) in the context ofQuantitative Codesign of Supercomputers (QCS). MODA is employed to gaininsights into the behavior of current High Performance Computing (HPC) systemsto improve system efficiency, performance, and reliability (e.g. throughoptimizing cooling infrastructure, job scheduling, and application parametertuning). In this work, we take the position that QCS in general, and MODA inparticular, require close exchange with the ML community to realize the fullpotential of data-driven analysis for the benefit of existing and future HPCsystems. This exchange will facilitate identifying the appropriate ML methodsto gain insights into current HPC systems and to go beyond expert-basedknowledge and rules of thumb.

Challenges and Opportunities of Machine Learning for Monitoring and Operational Data Analytics in Quantitative Codesign of Supercomputers