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UID:submissions.supercomputing.org_SC25_sess204_ws_ss119@linklings.com
SUMMARY:EAS-Sim: A Framework and its Methodology for the Co-Design of Mult
 i-Objective, Energy-Aware Schedulers for AI Clusters
DESCRIPTION:Roblex NANA TCHAKOUTE and Claude TADONKI (Centre de recherche 
 en informatique (CRI), Mines Paris - PSL University)\n\nThe explosive grow
 th of large-scale Deep Learning (DL) models has made energy consumption a 
 first-order operational cost and constraint in modern High-Performance Com
 puting (HPC) datacenters. Existing DL schedulers, however, are largely sin
 gle-objective and energy oblivious, struggling to balance the competing de
 mands of performance, fairness, and Quality of Service (QoS). To address t
 his flaw, we propose a methodology for the co-design of multi-objective an
 d energy-aware schedulers together with the associated simulation framewor
 k, the so-called EAS-Sim. Our methodology stands as a systematic approach 
 to enhance State-of-the-Art (SOTA) scheduling heuristics with energy-effic
 iency objectives.\nUsing our framework, we design and evaluate four novel 
 and malleable job schedulers. Our flagship energy-aware policy, Zeus, esta
 blishes a new Pareto-optimal frontier and reduces total energy consumption
  by  ≈8-10% compared to the SOTA performance scheduler Pollux with no stat
 istically significant loss in system throughput. EAS-Sim is available as o
 pen-source on GitHub.\n\nRecording: Livestreamed, Recorded\n\nRegistration
  Category: Technical Program Reg Pass, Workshop Reg Pass\n\nSession Chairs
 : Mike Woodacre (Hewlett Packard Enterprise (HPE)); Michèle Weiland (EPCC,
  The University of Edinburgh; The University of Edinburgh); Fumiyoshi Shoj
 i (RIKEN Center for Computational Science (R-CCS), Center for Computationa
 l Science); Pekka Manninen (CSC - IT Center for Science; University of Hel
 sinki, Finland); James H. Rogers (Oak Ridge National Laboratory (ORNL)); a
 nd Cate Berard (US Department of Energy)\n\n
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