Recognizing HPC Workloads Based on Power Draw Signatures

Abstract

The power draw of computing infrastructure— besides being a critical operating resource—can give valuable insights into the type and behavior of workloads running on it. In consequence, runtime power analysis can be a promising non-invasive monitoring approach. Recent work has shown that a system’s power draw can support reliable conclusions about running workloads, which serves as a basis for runtime placement decisions to adapt the system’s cumulative energy demand to the available energy supply in a volatile electricity grid.In this work, we reproduce earlier findings on the classification of running workload from a set of previously known workloads purely through external power measurements. Using a k-nearest neighbors classifier, we identify workloads of the NAS benchmark suite with a macro F1-score of 98% for OpenMP-based implementations and 85% for MPI-based implementations.