An efficient parallel algorithm for 3D magnetotelluric modeling with edge-based finite element

Three-dimensional magnetotelluric modeling algorithm of high accuracy and high efficiency is required for data interpretation and inversion. In this paper, edge-based finite element method with unstructured mesh is used to solve 3D magnetotelluric problem. Two boundary conditions—Dirichlet boundary...

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Bibliographic Details
Published in:Computational geosciences Vol. 25; no. 1; pp. 1 - 16
Main Authors: Zhu, Xiaoxiong, Liu, Jie, Cui, Yian, Gong, Chunye
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2021
Springer Nature B.V
Subjects:
Algorithms
Boundary conditions
Computation
Cores
Data interpretation
Dirichlet problem
Distributed memory
Earth and Environmental Science
Earth Sciences
Efficiency
Finite element method
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical Modeling and Industrial Mathematics
Microprocessors
Model accuracy
Model testing
Modelling
Original Paper
Parallel processing
Soil Science & Conservation
Storage
Three dimensional models
Work platforms
Unstructured mesh
Magnetotelluric
Edge-based finite element
Parallel algorithm
ISSN:1420-0597, 1573-1499
Online Access:Get full text
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Summary:Three-dimensional magnetotelluric modeling algorithm of high accuracy and high efficiency is required for data interpretation and inversion. In this paper, edge-based finite element method with unstructured mesh is used to solve 3D magnetotelluric problem. Two boundary conditions—Dirichlet boundary condition and Neumann boundary condition—are set for cross-validation and comparison. We propose an efficient parallel algorithm to speed up computation and improve efficiency. The algorithm is based on distributed matrix storage and has three levels of parallelism. The first two are process level parallelization for frequencies and matrix solving, and the last is thread-level parallelization for loop unrolling. The algorithm is validated by several model studies. Scalability tests have been performed on two distributed-memory HPC platforms, one consists of Intel Xeon E5-2660 microprocessors and the other consists of Phytium FT2000 Plus microprocessors. On Intel platform, computation time of our algorithm solving Dublin Test Model-1 with 3,756,373 edges at 21 frequencies is 365 s on 2520 cores. The speedup and efficiency are 1609 and 60% compared to 100 cores. On Phytium platform, scalability test shows that the speedup from 256 cores to 86,016 cores has been increased to 11,255.
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ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-020-09976-z