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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260422T143140Z
LOCATION:B312
DTSTART;TZID=America/New_York:20241118T113000
DTEND;TZID=America/New_York:20241118T120000
UID:submissions.supercomputing.org_SC24_sess811_ws_ciss107@linklings.com
SUMMARY:Predicting Protein Folding on Intel’s Data Center GPU Max Series A
 rchitecture (PVC)
DESCRIPTION:Madhavan Prasanna (Purdue University), Dhani Ruhela (Westwood 
 High School), and Aaditya Saxena (Bob Jones High School)\n\nPredicting the
  structure of proteins has been a grand challenge for over 60 years. Googl
 e's DeepMind team leveraged Artificial intelligence  in 2020 to develop Al
 phaFold and achieved an accuracy above 90 for two-thirds of the proteins i
 n CASP's competition. AlphaFold has been very successful in biology and me
 dicine. However, a lack of training code and expansive computational requi
 rements created an open-source implementation, OpenFold. OpenFold is fast,
  memory-efficient, and provides an OpenProtein dataset with five million M
 SAs. MLCommons added OpenFold to their HPC benchmarks suite in 2023 and wa
 s evaluated by four institutions on NVIDIA GPU architectures. This work pr
 esents our endeavours to port, run and tune OpenFold on Intel's Ponte Vecc
 hio (PVC) GPUs. To the best of our knowledge, this is the first large-scal
 e study of the distributed implementation of OpenFold application with Int
 el PVC GPU, presenting the challenges, opportunities and performance of th
 e application on Intel's Max series architecture.\n\nTag: I/O, Storage, Ar
 chive\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Glenn
  Brook (Cornelis Networks, University of Tennessee); Clayton Hughes (Sandi
 a National Laboratories); Nalini Kumar (Intel Corporation); Hatem Ltaief (
 King Abdullah University of Science and Technology (KAUST)); David Martin 
 (Lawrence Berkeley National Laboratory (LBNL), Energy Sciences Network (ES
 net)); and Amit Ruhela (Texas Advanced Computing Center (TACC), University
  of Texas)\n\n
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