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

Jia Shi; Ruipeng Li; Yuanzhe Xi; Yousef Saad; Maarten V. De Hoop

doi:10.1109/tpds.2021.3050448

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

Jia Shi, Ruipeng Li, Yuanzhe Xi, Yousef Saad, Maarten V. De Hoop

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This article is an extension of work entitled 'Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver.' by Shi et al., [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.

Original language	English (US)
Article number	9319555
Pages (from-to)	2609-2622
Number of pages	14
Journal	IEEE Transactions on Parallel and Distributed Systems
Volume	32
Issue number	11
DOIs	https://doi.org/10.1109/tpds.2021.3050448
State	Published - Nov 1 2021
Externally published	Yes

Bibliographical note

Funding Information:
The authors would like to thank Beth Plale and Stephen Harrell for the invitation. This work was supported in part by the Simons Foundation under the MATH+X Program, in part by the National Science Foundation under Grant DMS-1815143, in part by the members of the Geo-Mathematical Imaging Group at Rice University, and in part by the XSEDE Research Allocation TG-EAR170019. Jia Shi would like to thank Petroleum Geo-Services for using their supercomputer Abel and Cooperative Institute for Dynamic Earth Research (NSF EAR-1135452) for initial planetary science collaboration. The work of Ruipeng Li was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-JRNL-814373). The work of Yuanzhe Xi was supported by NSF-OAC 2003720. The work of Yousef Saad was supported by NSF-CCF 1812695.

Publisher Copyright:
© 1990-2012 IEEE.

Keywords

Eigenvalues and eigenvectors
earth and planetary sciences
numerical linear algebra
reproducibility

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10.1109/tpds.2021.3050448

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title = "Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility",

abstract = "This article is an extension of work entitled 'Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver.' by Shi et al., [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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author = "Jia Shi and Ruipeng Li and Yuanzhe Xi and Yousef Saad and {De Hoop}, {Maarten V.}",

note = "Funding Information: The authors would like to thank Beth Plale and Stephen Harrell for the invitation. This work was supported in part by the Simons Foundation under the MATH+X Program, in part by the National Science Foundation under Grant DMS-1815143, in part by the members of the Geo-Mathematical Imaging Group at Rice University, and in part by the XSEDE Research Allocation TG-EAR170019. Jia Shi would like to thank Petroleum Geo-Services for using their supercomputer Abel and Cooperative Institute for Dynamic Earth Research (NSF EAR-1135452) for initial planetary science collaboration. The work of Ruipeng Li was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-JRNL-814373). The work of Yuanzhe Xi was supported by NSF-OAC 2003720. The work of Yousef Saad was supported by NSF-CCF 1812695. Publisher Copyright: {\textcopyright} 1990-2012 IEEE.",

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N2 - This article is an extension of work entitled 'Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver.' by Shi et al., [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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Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility

Abstract

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