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DTSTAMP:20260422T000605Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231116T100000
DTEND;TZID=America/Denver:20231116T170000
UID:submissions.supercomputing.org_SC23_sess304_rpost224@linklings.com
SUMMARY:Parallel Optimization Methods for Direct Numerical Simulation of H
 igh Reynolds Number Wall Turbulence with a Grid Size of 100 Billion
DESCRIPTION:Jiabin Xie, Guangnan Feng, Han Huang, Junxuan Feng, and Yutong
  Lu (Sun Yat-sen University, Guangzhou, China)\n\nDirect numerical simulat
 ion (DNS) is a technique that directly solves the fluid Navier-Stokes equa
 tions with high spatial and temporal resolutions. However, its utility in 
 studying high Reynolds number (Re) wall turbulence of particular interest 
 is limited by the rapidly growing grid size (i.e., the memory and computat
 ion requirement) with Re^3. \n\nWe present PowerLLEL, a high-performance f
 inite difference solver tailored for the challenging DNS of incompressible
  wall turbulence at extreme scales. An adaptive multi-level parallelizatio
 n strategy is proposed to fully exploit the multi-level parallelism of var
 ious architectures and enhance computational performance. The communicatio
 n performance of global transpose and halo exchange is significantly impro
 ved by a tridiagonal solver based on the parallel diagonal dominant (PDD) 
 algorithm and three RDMA-implemented communication optimizations.  Strong 
 scaling tests on the Tianhe-2A supercomputer show that PowerLLEL achieves 
 nearly 92% parallel efficiency with up to 31,104 cores on a grid size of 1
 43.3 billion.\n\nTag: Artificial Intelligence/Machine Learning, Architectu
 re and Networks, Heterogeneous Computing, I/O and File Systems, Performanc
 e Measurement, Modeling, and Tools, Post-Moore Computing, Programming Fram
 eworks and System Software, Quantum Computing\n\nRegistration Category: Te
 ch Program Reg Pass, Exhibits Reg Pass\n\n
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