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DTSTART:19700308T020000
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DTSTAMP:20230124T171520Z
LOCATION:C146
DTSTART;TZID=America/Chicago:20221113T111500
DTEND;TZID=America/Chicago:20221113T114000
UID:submissions.supercomputing.org_SC22_sess424_ws_scalah104@linklings.com
SUMMARY:Scalable Finite-Element Viscoelastic Crustal Deformation Analysis 
 Accelerated with Data-Driven Method
DESCRIPTION:Workshop\n\nScalable Finite-Element Viscoelastic Crustal Defor
 mation Analysis Accelerated with Data-Driven Method\n\nFujita, Murakami, I
 chimura, Hori, Hori...\n\nTargeting viscoelastic crustal deformation analy
 sis, we develop a scalable unstructured implicit finite-element solver acc
 elerated by a data-driven method. Here, we combine a data-driven predictor
 , that uses past time step data for estimating high-accuracy initial solut
 ions, and a multi-grid based conjugate gradient solver for efficient solvi
 ng of the remaining errors. When compared to using a standard initial solu
 tion predictor on a block Jacobi-preconditioned conjugate gradient solver,
  a 3.19-fold speedup was attained by using the data-driven predictor, and 
 combination with a multi-grid solver attained a total speedup of 76.8-fold
  on Fugaku. Furthermore, as the computation of the data-driven predictor i
 s localized and can be conducted without communication between computation
  nodes, the solver attained high weak scalability efficiency of 78.5% up t
 o 73728 compute nodes of Fugaku, leading to 6.88% FP64 peak efficiency for
  the whole application. Such development is also expected to be useful for
  accelerating other PDE-based time evolution problems.\n\nSession Format: 
 Recorded\n\nTag: Algorithms, Exascale Computing, Extreme Scale Computing, 
 Heterogeneous Systems, Post-Moore Computing, Quantum Computing\n\nRegistra
 tion Category: Workshop Reg Pass
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