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BEGIN:VEVENT
DTSTAMP:20260422T143141Z
LOCATION:B304
DTSTART;TZID=America/New_York:20241118T160400
DTEND;TZID=America/New_York:20241118T162100
UID:submissions.supercomputing.org_SC24_sess761_ws_hppss106@linklings.com
SUMMARY:Seamlessly scale your python program from single CPU core to multi
 -GPU multi-node HPC cluster with cuNumeric
DESCRIPTION:Wonchan Lee, Manolis Papadakis, Mike Bauer, and Bo Dong (NVIDI
 A Corporation)\n\nPython is a powerful and user-friendly programming langu
 age widely adopted by researchers and scientists. As data sizes and comput
 ational complexities grow, CPU-based Python struggles to meet the speed an
 d scale demanded by cutting-edge research. Distributed accelerated computi
 ng offers an infrastructure to efficiently solve and test hypotheses in da
 ta-driven problems. Whether it’s analyzing data generated by recording the
  scattering of high-energy electron beams, building new methodology to sol
 ve complex CFD problems, or build machine learning (ML) models. Researcher
 s are increasingly seeking ways to effortlessly scale their programs. Our 
 upcoming demonstration will provide a comprehensive walkthrough on how to 
 use cuNumeric and Legate to seamlessly scale your Python programs from a s
 ingle CPU core to multi-GPU, multi-node supercomputers without any modific
 ations to your code.\n\nAcknowledgment and potential co-presenter: \nJason
  R. Green, Professor, Department of Chemistry, Department of Physics, Univ
 ersity of Massachusetts, Boston\nPat McCormick, Senior Computer Scientist,
  Team Leader, LANL\n\nTag: Applications and Application Frameworks, Artifi
 cial Intelligence/Machine Learning, Parallel Programming Methods, Models, 
 Languages and Environments\n\nRegistration Category: Workshop Reg Pass\n\n
 Session Chairs: Sunita Chandrasekaran (University of Delaware), Sam Forema
 n (Argonne National Laboratory (ANL)), Daniel Margala (Lawrence Berkeley N
 ational Laboratory (LBNL)), and Pete Mendygral (Hewlett Packard Enterprise
  (HPE))\n\n
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