Anish Govind
ABSTRACT
Power is a limiting factor for supercomputers limiting their scale and operation. Characterizing the power signatures of different application types can enable data centers to operate efficiently, even when power constrained. This paper investigates power profiles of diverse scientific applications, spanning both traditional simulations and modern machine learning (ML) running on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC). Our findings indicate that traditional simulations typically consume more power on average than ML workloads. Furthermore, ML applications exhibit periodic power fluctuations attributed to epoch transitions during training. Finally, we discuss the potential implications of the research insights toward automatic demand response (ADR) and considerations for designing future systems.
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Index Terms
- Comparing Power Signatures of HPC Workloads: Machine Learning vs Simulation
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