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DTSTAMP:20181221T160905Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess323_post207@linklings.com
SUMMARY:Refactoring and Optimizing Multiphysics Combustion Models for Data
  Parallelism
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nRefactorin
 g and Optimizing Multiphysics Combustion Models for Data Parallelism\n\nSt
 one, Poludnenko, Taylor\n\nHigh-fidelity combustion simulations combine hi
 gh-resolution computational fluid dynamics numerical methods with multi-ph
 ysics models to capture chemical kinetics and transport processes. These m
 ulti-physics models can dominate the computation cost of the simulation. D
 ue to the high cost of combustion simulations and the important role simul
 ations play in propulsion and power research, acceleration methods are nee
 ded to reduce the computational time and cost.  Multi-physics models withi
 n each mesh cell are often independent leading to significant parallelism.
  However, the iterative algorithms often impede efficient SIMD data parall
 elism, a key performance feature on modern HPC systems.  Refactoring metho
 ds for multi-physics models (e.g., kinetics, equation-of-state, diffusion)
  with nonuniform workloads are demonstrated and benchmarked on a range of 
 platforms (AVX2, KNL, AVX-512). Realized speed-ups over 6x were achieved o
 n KNL and 4x on Skylake (SKX) for complex chemical kinetics models and ove
 r 3x on SKX for iterative EOS computations.
URL:https://sc18.supercomputing.org/presentation/?id=post207&sess=sess323
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