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Bailey, D.H., Barszcz, E., Barton, J.T., Browning, D.S., Carter, R.L., Dagum, L., Fatoohi, R.A., Frederickson, P.O., Lasinski, T.A., Schreiber, R.S., Simon, H.D., Venkatakrishnan, V. and Weeratunga, S.K. (1991) The NAS Parallel Benchmarks-Summary and Preliminary Results. Proceedings of the 1991 ACM/IEEE conference on Supercomputing, Albuquerque, 18-22 November 1991, 158-165.
https://doi.org/10.1145/125826.125925

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.