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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost160@linklings.com
SUMMARY:Fast Scalable Implicit Solver with Convergence of Physics-Based Si
 mulation and Data-Driven Learning: Toward High-Fidelity Simulation with Di
 gital Twin City
DESCRIPTION:Posters, Research Posters\n\nFast Scalable Implicit Solver wit
 h Convergence of Physics-Based Simulation and Data-Driven Learning: Toward
  High-Fidelity Simulation with Digital Twin City\n\nIchimura, Fujita, Koya
 ma, Kusakabe, Minami...\n\nWe propose an HPC-based scalable implicit low-o
 rder unstructured nonlinear finite-element solver that uses data generated
  during physics-based simulations for data-driven learning. Here, a cost e
 fficient preconditioner is developed using the data-driven learning method
  for accelerating the iterative solver. Together with Arm scalable vector 
 extension-aware SIMD and multi-core tuning of core sparse matrix-vector mu
 ltiplication kernel on Fugaku, the developed solver achieved a 15.2-fold s
 peedup over the conventional preconditioned conjugate gradient solver with
  96.4% size-up scalability up to 1,179,648 cores of Fugaku (11.8% of FP64 
 peak with 8.87 PFLOPS). Furthermore, the developed solver achieved a 10.3-
 fold speedup over the state-of-the-art SC14 Gordon Bell Prize finalist sol
 ver on a high resolution urban model with over 11 billion degrees of freed
 om. Such development in merging HPC-enhanced physics-based simulations wit
 h data-driven learning is expected to enhance physics-based simulation cap
 ability and is expected to contribute to various applications such as digi
 tal twins of cities.\n\nRegistration Category: Tech Program Reg Pass, Exhi
 bits Reg Pass
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