Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility

This article is an extension of work entitled "Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver." by Shi et al. , <xref ref-type="bibr" rid="ref1">[1] originally presented at the SC18 conference. A highly parallel p...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 32; H. 11; S. 2609 - 2622
Hauptverfasser: Shi, Jia, Li, Ruipeng, Xi, Yuanzhe, Saad, Yousef, de Hoop, Maarten V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Computational modeling
earth and planetary sciences
Eigenvalues
Eigenvalues and eigenfunctions
Eigenvalues and eigenvectors
Finite element analysis
Finite element method
Mars
Mathematical analysis
Moon
numerical linear algebra
Planetary interiors
Planets
Polynomials
Reproducibility
Surfaces
Teams
ISSN:1045-9219, 1558-2183
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Zusammenfassung:This article is an extension of work entitled "Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver." by Shi et al. , <xref ref-type="bibr" rid="ref1">[1] originally presented at the SC18 conference. A highly parallel polynomial filtered eigensolver was developed and exploited to calculate the planetary normal modes. The proposed method is ideally suited for computing interior eigenpairs for large-scale eigenvalue problems as it greatly enhances memory and computational efficiency. In this article, the second-order finite element method is used to further improve the accuracy as only the first-order finite element method was deployed in the previous work. The parallel algorithm, its parallel performance up to 20k processors, and the great computational accuracy are illustrated. The reproducibility of the previous work was successfully performed on the Student Cluster Competition at the SC19 conference by several participant teams using a completely different Mars-model dataset on different clusters. Both weak and strong scaling performances of the reproducibility by the participant teams were impressive and encouraging. The analysis and reflection of their results are demonstrated and future direction is discussed.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2021.3050448