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DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post215@linklings.com
SUMMARY:Optimizing Next Generation Hydrodynamics Code for Exascale Systems
DESCRIPTION:Dana Akhmetova (KTH Royal Institute of Technology); Sumathi La
 kshmiranganatha (University of Wyoming); Diptajyoti Mukherjee (Allegheny C
 ollege); Frederick Oullet (University of Florida); Patrick Payne (Los Alam
 os National Laboratory); Nicholas Stegmeier (South Dakota State University
 ); and Christoph Junghans, Robert Pavel, and Vinay Ramakrishnaiah (Los Ala
 mos National Laboratory)\n\nStudying continuum dynamics problems computati
 onally can illuminate complex physical phenomena where experimentation is 
 too costly. However, the models used in studying these phenomena usually r
 equire intensive calculations, some of which are beyond even the largest s
 upercomputers to date. Emerging high performance computing (HPC) platforms
  will likely have varied levels of heterogeneity, making hybrid programmin
 g with MPI+X essential for achieving optimal performance. This research in
 vestigates hybrid programming and unconventional approaches like machine l
 earning for a next generation hydrodynamics code, FleCSALE, in the context
  of tabular equation of state (EOS). We demonstrate an overall 5x speedup 
 to the code, the use of GPUs to accelerate EOS tabular interpolation, and 
 a proof of concept machine learning approach to EOS.\n\nRegistration Categ
 ory: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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