Scalable Finite-Element Viscoelastic Crustal Deformation Analysis Accelerated with Data-Driven Method
DescriptionTargeting viscoelastic crustal deformation analysis, we develop a scalable unstructured implicit finite-element solver accelerated by a data-driven method. Here, we combine a data-driven predictor, that uses past time step data for estimating high-accuracy initial solutions, and a multi-grid based conjugate gradient solver for efficient solving of the remaining errors. When compared to using a standard initial solution 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 is localized and can be conducted without communication between computation nodes, the solver attained high weak scalability efficiency of 78.5% up to 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.
Event Type
TimeSunday, 13 November 202211:15am - 11:40am CST
Registration Categories
Exascale Computing
Extreme Scale Computing
Heterogeneous Systems
Post-Moore Computing
Quantum Computing
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