SC17 Denver, CO

P98: Energy Efficiency in HPC with Machine Learning and Control Theory


Authors: Connor Imes (University of Chicago), Steven Hofmeyr (Lawrence Berkeley National Laboratory), Henry Hoffmann (University of Chicago)

Abstract: Performance and power management in HPC has historically favored a race-to-idle approach in order to complete applications as quickly as possible, but this is not energy-efficient on modern systems. As we move toward exascale and hardware over-provisioning, power management is becoming more critical than ever for HPC system administrators, opening the door for more balanced approaches to performance and power management. We propose two projects to address balancing application performance and system power consumption in HPC during application runtime, using closed loop feedback designs based on the Self-Aware Computing Model to observe, decide, and act.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf


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