Soft Cluster Powercap at SuperMUC-NG with EAR

Corbalan, Julita; Alonso, Lluis; Navarrete, Carmen B.; Guillen, Carla

doi:10.1109/igsc55832.2022.9969360

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…43,44 Finally, there is a growing number of prediction models, which try to predict future outcomes using statistical or AI-driven approaches. Examples of the earlier would be a linear multivariate regression methods proposed by D. Economou et al 45 and L. Brochard et al, 25,46,47 where server power consumption is treated as function of giga-instructions per second (GIPS) and giga-bytes of memory read or written per second (GBS) at different processor frequencies. Another class of prediction models uses deep learning (DL) for power consumption prediction.…”

Section: Related Workmentioning

confidence: 99%

Towards bespoke optimizations of energy efficiency in HPC environments

Tracey,

Elisseev,

Smyrnakis

et al. 2023

Applied AI Letters

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We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.

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