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DTSTART:19700308T020000
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DTSTAMP:20260422T000713Z
LOCATION:710
DTSTART;TZID=America/Denver:20231113T171000
DTEND;TZID=America/Denver:20231113T173000
UID:submissions.supercomputing.org_SC23_sess445_ws_scalah104@linklings.com
SUMMARY:Moment Representation of Regularized Lattice Boltzmann Methods on 
 NVIDIA and AMD GPUs
DESCRIPTION:Pedro Valero-Lara, Jeffrey Vetter, and John Gounley (Oak Ridge
  National Laboratory (ORNL)) and Amanda Randles (Duke University)\n\nThe l
 attice Boltzmann method is a highly scalable Navier-Stokes solver that has
  been applied to flow problems in a wide array of domains. However, the me
 thod is bandwidth-bound on modern GPU accelerators and has a large memory 
 footprint. In this paper, we present new 2D and 3D GPU implementations of 
 two different regularized lattice Boltzmann methods, which are not only ab
 le to achieve an acceleration of ~1.4× w.r.t. reference lattice Boltzmann 
 implementations but also reduce the memory requirements by up to 35% and 4
 7% in 2D and 3D simulations respectively. These new approaches are evaluat
 ed on NVIDIA and AMD GPU architectures.\n\nTag: Algorithms, Heterogeneous 
 Computing, Large Scale Systems\n\nRegistration Category: Workshop Reg Pass
 \n\nSession Chairs: Vassil Alexandrov (Hartree Centre, STFC); Jack Dongarr
 a (University of Tennessee, Knoxville; Oak Ridge National Laboratory (ORNL
 )); Christian Engelmann (Oak Ridge National Laboratory (ORNL)); Al Geist (
 Oak Ridge National Laboratory (ORNL)); and Dieter A. Kranzlmueller (Ludwig
 -Maxmilians-Universität München, Leibniz Supercomputing Centre (LRZ))\n\n
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