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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055407Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211115T113000
DTEND;TZID=America/Chicago:20211115T120000
UID:submissions.supercomputing.org_SC21_sess423_ws_mlhpce105@linklings.com
SUMMARY:MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Lear
 ning on HPC Systems
DESCRIPTION:Workshop\n\nMLPerf HPC: A Holistic Benchmark Suite for Scienti
 fic Machine Learning on HPC Systems\n\nFarrell, Emani, Balma, Drescher, Dr
 ozd...\n\nScientific communities are increasingly adopting machine learnin
 g and deep learning models in their applications to accelerate scientific 
 insights. High performance computing systems are pushing the frontiers of 
 performance with a rich diversity of hardware resources and massive scale-
 out capabilities. There is a critical need to understand fair and effectiv
 e benchmarking of machine learning applications that are representative of
  real-world scientific use cases. MLPerf(TM) is a community-driven standar
 d to benchmark machine learning workloads, focusing on end-to-end performa
 nce metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of 
 large-scale scientific machine learning training applications driven by th
 e MLCommons(TM) Association. We present the results from the first submiss
 ion round, including a diverse set of some of the world's largest HPC syst
 ems, along with a systematic framework for their joint analysis and insigh
 ts on implementations. Furthermore, we characterize each benchmark with co
 mpute, memory and I/O behaviours to parameterize extended roofline perform
 ance models.\n\nTag: Online Only, Machine Learning and Artificial Intellig
 ence\n\nRegistration Category: Workshop Reg Pass
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