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LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251118T080000
DTEND;TZID=America/Chicago:20251118T170000
UID:submissions.supercomputing.org_SC25_sess527_post199@linklings.com
SUMMARY:Memory-Efficient CFD Based on MPS: Effective One-Billion-Cell Reso
 lution on a Single Node
DESCRIPTION:Junya Onishi (RIKEN Center for Computational Science (R-CCS));
  Ayato Takii (Kobe University, Japan; RIKEN Center for Computational Scien
 ce (R-CCS)); Sangwon Kim (RIKEN Center for Computational Science (R-CCS));
  Younghwa Cho (Hokkaido University, Japan); and Makoto Tsubokura (Kobe Uni
 versity, Japan; RIKEN Center for Computational Science (R-CCS))\n\nWe inve
 stigate matrix product states (MPS), a tensor-network compression method, 
 as a memory-efficient representation of flow variables. A three-dimensiona
 l incompressible Navier-Stokes solver is implemented entirely in MPS form 
 and is applied to canonical flow problems. Results show substantial memory
  savings and the ability to perform a $1024^3$ simulation on a single GPU.
  Performance analysis revealed new bottlenecks, particularly bond-dimensio
 n growth during nonlinear operations,\nsuggesting novel optimization strat
 egies are needed to fully realize MPS-based CFD at extreme scales.\n\nTag:
  Research & ACM SRC Posters\n\nRegistration Category: Technical Program Re
 g Pass\n\nSession Chairs: Kento Sato (RIKEN Center for Computational Scien
 ce (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Research Centre; Com
 monwealth Scientific and Industrial Research Organisation (CSIRO), Austral
 ia); and Anja Gerbes (Georg-August-Universität Göttingen)\n\n
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