Prediction and Characterization of Application Power Use in a High-performance Computing Environment
HPC, Queueing systems, Renewable energy, Scientific computing
Daniels College of Business, Business Information and Analytics
Power use in data centers and high‐performance computing (HPC) facilities has grown in tandem with increases in the size and number of these facilities. Substantial innovation is needed to enable meaningful reduction in energy footprints in leadership‐class HPC systems. In this paper, we focus on characterizing and investigating application‐level power usage. We demonstrate potential methods for predicting power usage based on a priori and in situ characteristics. Finally, we highlight a potential use case of this method through a simulated power‐aware scheduler using historical jobs from a real scientific HPC system.
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Bugbee, Bruce, et al. “Prediction and Characterization of Application Power Use in a High‐Performance Computing Environment.” Statistical Analysis and Data Mining, vol. 10, no. 3, 2017, pp. 155–165. doi: 10.1002/sam.11339.