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DTSTAMP:20181221T160903Z
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:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nOptimizing
  Next Generation Hydrodynamics Code for Exascale Systems\n\nAkhmetova, Lak
 shmiranganatha, Mukherjee, Oullet, Payne...\n\nStudying continuum dynamics
  problems computationally can illuminate complex physical phenomena where 
 experimentation is too costly. However, the models used in studying these 
 phenomena usually require intensive calculations, some of which are beyond
  even the largest supercomputers to date. Emerging high performance comput
 ing (HPC) platforms will likely have varied levels of heterogeneity, makin
 g hybrid programming with MPI+X essential for achieving optimal performanc
 e. This research investigates hybrid programming and unconventional approa
 ches like machine learning for a next generation hydrodynamics code, FleCS
 ALE, 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.
URL:https://sc18.supercomputing.org/presentation/?id=post215&sess=sess324
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