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DTSTART:19700308T020000
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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_post207@linklings.com
SUMMARY:Refactoring and Optimizing Multiphysics Combustion Models for Data
  Parallelism
DESCRIPTION:Christopher Stone (US Department of Defense HPC Modernization 
 Program, Engility Corporation); Alexei Poludnenko (Texas A&M University); 
 and Brian Taylor (US Air Force Research Laboratory)\n\nHigh-fidelity combu
 stion simulations combine high-resolution computational fluid dynamics num
 erical methods with multi-physics models to capture chemical kinetics and 
 transport processes. These multi-physics models can dominate the computati
 on cost of the simulation. Due to the high cost of combustion simulations 
 and the important role simulations play in propulsion and power research, 
 acceleration methods are needed to reduce the computational time and cost.
   Multi-physics models within each mesh cell are often independent leading
  to significant parallelism. However, the iterative algorithms often imped
 e efficient SIMD data parallelism, a key performance feature on modern HPC
  systems.  Refactoring methods for multi-physics models (e.g., kinetics, e
 quation-of-state, diffusion) with nonuniform workloads are demonstrated an
 d benchmarked on a range of platforms (AVX2, KNL, AVX-512). Realized speed
 -ups over 6x were achieved on KNL and 4x on Skylake (SKX) for complex chem
 ical kinetics models and over 3x on SKX for iterative EOS computations.\n\
 nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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