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.