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DTSTAMP:20260422T143139Z
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DTSTART;TZID=America/New_York:20241117T120000
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UID:submissions.supercomputing.org_SC24_sess739_ws_ss107@linklings.com
SUMMARY:Sustainable AI: Experiences, Challenges and Recommendations
DESCRIPTION:Eleanor Broadway, Joseph Lee, and Michele Weiland (Edinburgh P
 arallel Computing Centre (EPCC))\n\nThe use of Artificial Intelligence (AI
 ) and Machine Learning (ML) as part of scientific workloads is becoming in
 creasingly widespread. It is imperative to understand how to configure AI 
 and ML applications on HPC systems to optimise their performance and energ
 y efficiency, thereby minimising their environmental impact. In this study
 , we use MLPerf HPC's DeepCAM benchmark to assess and explore the energy e
 fficiency of ML applications on different hardware platforms. We highlight
  the challenges that, despite growing popularity, ML frameworks still pres
 ent in a traditional HPC environment, as well as the challenges of measuri
 ng power and energy on a variety of HPC and cloud-like virtualised systems
 . We conclude our study by proposing recommendations that will improve and
  encourage best practices around sustainable AI and ML workloads on HPC sy
 stems.\n\nTag: Energy Efficiency, HPC Infrastructure, Sustainability\n\nRe
 gistration Category: Workshop Reg Pass\n\nSession Chairs: Cate Berard (US 
 Department of Energy); James H. Rogers (Oak Ridge National Laboratory (ORN
 L)); Fumiyoshi Shoji (RIKEN Center for Computational Science (R-CCS), Cent
 er for Computational Science); Michèle Weiland (EPCC, The University of Ed
 inburgh; The University of Edinburgh); and Mike Woodacre (Hewlett Packard 
 Enterprise (HPE))\n\n
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