A Scalable Parallel Algorithm for 3-D Magnetotelluric Finite Element Modeling in Anisotropic Media

3-D magnetotelluric (MT) forward modeling has always been faced with the problems of high memory requirements and long computing time. In this article, we design a scalable parallel algorithm for 3-D MT finite element modeling in anisotropic media. The parallel algorithm is based on the distributed...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 14
Hauptverfasser: Zhu, Xiaoxiong, Liu, Jie, Cui, Yian, Gong, Chunye
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Anisotropic media
Anisotropy
Boundary conditions
Computational modeling
Computing time
Conductivity
Conductivity anisotropy
Dirichlet problem
Finite element analysis
Finite element method
finite element method (FEM)
magnetotelluric (MT)
Mathematical model
Mathematical models
Memory management
Message passing
Modelling
Mumps
parallel algorithm
Parallel algorithms
Parallel processing
Solvers
Sparse matrices
Storage
ISSN:0196-2892, 1558-0644
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Zusammenfassung:3-D magnetotelluric (MT) forward modeling has always been faced with the problems of high memory requirements and long computing time. In this article, we design a scalable parallel algorithm for 3-D MT finite element modeling in anisotropic media. The parallel algorithm is based on the distributed mesh storage, including multiple parallel granularities, and is implemented through multiple tools. Message-passing interface (MPI) is used to exploit process parallelisms for subdomains, frequencies, and solving equations. Thread parallelisms for merge sorting, element analysis, matrix assembly, and imposing Dirichlet boundary conditions are developed by Open Multi-Processing (OpenMP). We validate the algorithm through several model simulations and study the effects of topography and conductivity anisotropy on apparent resistivities and phase responses. Scalability tests are performed on the Tianhe-2 supercomputer to analyze the parallel performance of different parallel granularities. Three parallel direct solvers Supernodal LU (SUPERLU), MUltifrontal Massively Parallel sparse direct Solver (MUMPS), and Parallel Sparse matriX package (PASTIX) are compared in solving sparse systems of equations. As a result, reasonable parallel parameters are suggested for practical applications. The developed parallel algorithm is proven to be efficient and scalable.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3078735