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DTSTART;TZID=America/Denver:20231112T152000
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UID:submissions.supercomputing.org_SC23_sess433_ws_ships102@linklings.com
SUMMARY:An End-to-End HPC Framework for Dynamic Power Objectives
DESCRIPTION:Workshop\n\nDaniel C. Wilson (Boston University, Intel Corpora
 tion); Fatih Acun (Boston University); Siddhartha Jana, Federico Ardanaz, 
 and Jonathan M. Eastep (Intel Corporation); and Ioannis Ch. Paschalidis an
 d Ayse K. Coskun (Boston University)\n\nHigh-Performance Computing (HPC) c
 enters demand a lot of power, and continue to grow through the exascale er
 a. This work establishes the need for a multi-tiered, feedback-driven powe
 r management framework to follow dynamic power objectives while maximizing
  job performance, highlighting the need to respond to external factors (e.
 g., power constraints), and internal factors (e.g., performance variation)
 . We present a practical implementation of this framework on a real-world 
 cluster in addition to conducting simulations for larger data centers. We 
 accurately track a moving power target for demand response while reacting 
 to incomplete or inaccurate prior knowledge about job power and performanc
 e properties. We demonstrate that online performance feedback from a job r
 untime enables a cluster power management policy to recover most of the pe
 rformance degradation introduced by job-type misclassification.\n\nTag: Ar
 tificial Intelligence/Machine Learning, Energy Efficiency, Green Computing
 , Performance Measurement, Modeling, and Tools, Sustainability\n\nRegistra
 tion Category: Workshop Reg Pass\n\nSession Chairs: Andrea Borghesi (Unive
 rsity of Bologna; Department of Electrical, Electronic and Information Eng
 ineering) and Daniela Loreti (University of Bologna)
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