Author(s)

Vaibhav Sundriyal, Masha Sosonkina

Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, USA.

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.

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

Sundriyal, V. and Sosonkina, M. (2022) Runtime Energy Savings Based on Machine Learning Models for Multicore Applications. Journal of Computer and Communications, 10, 63-80. doi: 10.4236/jcc.2022.106006.