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Cacciari, M., Bartolini, A., Tilli, A. and Benini, L. (2013) Thermal and Energy Management of High-Performance Multicores: Distributed and Self-Calibrating Model-Predictive Controller. IEEE Transactions on Parallel and Distributed Systems, 24, 170-183.
https://doi.org/10.1109/TPDS.2012.117

has been cited by the following article:

• TITLE: Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

  AUTHORS: Vaibhav Sundriyal, Masha Sosonkina

  KEYWORDS: Machine Learning, RAPL, DVFS, Uncore Frequency Scaling, Energy Savings, Performance Modeling

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.