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DTSTART;TZID=America/Denver:20231112T112500
DTEND;TZID=America/Denver:20231112T114000
UID:submissions.supercomputing.org_SC23_sess420_ws_ss101@linklings.com
SUMMARY:Reducing HPC Energy Footprint for Large Scale GPU Accelerated Work
 loads
DESCRIPTION:Gabriel Hautreux and Etienne Malaboeuf (CINES)\n\nAs the energ
 y cost continues to rise, HPC centers need to reduce their energy footprin
 t.  We examine a French national machine hosted at CINES in Montpellier, A
 dastra, based on AMD-MI250X GPUs and #3 in Green500.  As a base for the st
 udy, we define a set of applications representative of our current HPC/AI 
 production workload. In this parametric study, we characterize our diverse
  workload by applying a range of frequency or power capping policies at th
 e node level in order to build an efficiency profile of each application. 
 Based on the collected results, we produce guidelines trading between pure
  energy savings to pure performance for each application and for the produ
 ction workload as a whole. We hope the results of this study will be of he
 lp to accelerators enabled HPC centers seeking to reduce their energy foot
 print by applying capping policies on their accelerators at the node level
 .\n\nTag: Energy Efficiency, Green Computing, Sustainability\n\nRegistrati
 on Category: Workshop Reg Pass\n\nSession Chairs: Kimmo Koski (CSC – IT Ce
 nter for Science Ltd, Finland); James H. Rogers (Oak Ridge National Labora
 tory (ORNL)); Fumiyoshi Shoji (RIKEN Center for Computational Science (R-C
 CS), Center for Computational Science); William W. Thigpen (NASA); Michèle
  Weiland (EPCC, The University of Edinburgh; The University of Edinburgh);
  and Mike Woodacre (Hewlett Packard Enterprise (HPE))\n\n
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