Journal of Computer and Communications
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.
Original Publication Citation
Sundriyal, V. & Sosonkina, M. (2022) Runtime energy savings based on machine learning models for multicore applications. Journal of Computer and Communications, 10(6), 63-80. https://doi.org/10.4236/jcc.2022.106006
Sundriyal, Vaibhav and Sosonkina, Masha, "Runtime Energy Savings Based on Machine Learning Models for Multicore Applications" (2022). Electrical & Computer Engineering Faculty Publications. 332.