Critique of "Planetary Normal Mode Computation: Parallel Algorithms, Performance, and Reproducibility" by SCC Team From National Tsing Hua University

As a special activity of the Student Cluster Competition at SC19 conference, we made an attempt to reproduce the scalability evaluations of a highly paralleled polynomial filtering eigensolver for computing planetary interior normal modes. Our experiments were conducted on a Mars dataset using a sma...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 32; no. 11; pp. 2623 - 2626
Main Authors: Sun, Wei-Fang, Chen, Hung-Hsin, Lin, Shao-Fu, Lin, Yuan-Ching, Wu, Jing-Wei, Lin, En-Te, Chou, Jerry
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Central processing units
Clusters
Computational modeling
Computer architecture
CPUs
Datasets
Eigenvalues and eigenfunctions
Mars
Mathematical model
planetary interior normal modes
Planetary interiors
polynomial filtering eigensolver
Polynomials
Reproducibility
Reproducible computation
Scalability
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:As a special activity of the Student Cluster Competition at SC19 conference, we made an attempt to reproduce the scalability evaluations of a highly paralleled polynomial filtering eigensolver for computing planetary interior normal modes. Our experiments were conducted on a Mars dataset using a small scale 4-node cluster with Intel Skylake CPU architecture, while the original article's were conducted on a Moon dataset using a large scale 256-node supercomputer with Intel CPU Skylake and KNL architectures. This article shares our experiences and observations from our reproducibility activity and discusses our findings on three main sections: the weak scalability, the strong scalability, and the relationships between variables. The results of weak scalability and strong scalability were successfully reproduced. But due to the differences on the problem scale, input dataset, and system architecture, different behaviors regarding the polynomial degree were observed.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2021.3051725