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David, H., Gorbatov, E., Hanebutte U.R., Khannal R. and Le, C. (2010) RAPL: Memory Power Estimation and Capping. In: Proceedings of the 16th ACM/IEEE International Symposium on Low Power Electronics and Design, ACM, New York, 189-194.
https://doi.org/10.1145/1840845.1840883

has been cited by the following article:

• TITLE: Maximizing Performance under a Power Constraint on Modern Multicore Systems

  AUTHORS: Vaibhav Sundriyal, Masha Sosonkina, Bryce Westheimer, Mark S. Gordon

  KEYWORDS: Uncore Frequency Scaling (UFS), Dynamic Voltage and Frequency Scaling (DVFS), Power Budget, GAMESS

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.7, July 29, 2019

  ABSTRACT: Energy efficiency and energy-proportional computing have become a central focus in modern supercomputers. These supercomputers should provide high throughput per unit of power to be sustainable in terms of operating cost and failure rates. In this paper, a power-bounded strategy is proposed that maximizes parallel application performance under a given power constraint. The strategy dynamically allocates power to core, uncore, and memory power domains within a node to maximize performance under a given power budget. Experiments on a 20-core Haswell-EP platform for a real-world parallel application GAMESS demonstrate that the proposed strategy delivers performance within 4% of the best possible performance for as much as 25% reduction in the minimum power budget required for maximum performance.