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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160555Z
LOCATION:Track 8
DTSTART;TZID=America/New_York:20201112T170000
DTEND;TZID=America/New_York:20201112T172500
UID:submissions.supercomputing.org_SC20_sess214_ws_lasalss114@linklings.co
 m
SUMMARY:A Fast Scalable Iterative Implicit Solver with Green's Function-Ba
 sed Neural Networks
DESCRIPTION:Workshop\n\nA Fast Scalable Iterative Implicit Solver with Gre
 en's Function-Based Neural Networks\n\nIchimura, Fujita, Hori, Maddegedara
 , Ueda...\n\nBased on the Green's functions that reflect mathematical prop
 erties of partial differential equations (PDE), we developed a novel preco
 nditioner using neural networks (NNs) with high accuracy and small computa
 tional cost for improving the convergence property of an iterative implici
 t solver. As the dense and uniform computation involved in NNs are more ef
 ficient than that of the conventional PDE solver schemes, we could solve t
 he time evolution of a 405,017,091 degrees-of-freedom highly heterogeneous
  problem in 5.48-fold shorter time compared to a typical PDE solver. The m
 ethod is also suitable for use with low-precision arithmetic in NNs as the
  accuracy of the final solution is guaranteed. The localized property of N
 Ns enable high scalability for solving large problems (103,305,758,211 deg
 rees-of-freedom problem solved with 97.4% weak scalability using 256 Casca
 de Lake Xeon CPU-based Oakbridge-CX nodes with a total of 14336 CPU cores 
 with developed MPI-OpenMP hybrid code). This method can be used in various
  PDE-based simulations and has potential to make broad ripple effects in v
 arious fields.\n\nTag: Algorithms, Extreme Scale Computing, Performance/Pr
 oductivity Measurement and Evaluation, Scalable Computing, Scientific Comp
 uting\n\nRegistration Category: Workshop Reg Pass
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