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

Autor:	Hung-Hsin Chen, Shao-Fu Lin, Wei-Fang Sun, Jerry Chou, En-Te Lin, Jing-Wei Wu, Yuan-Ching Lin
Rok vydání:	2021
Předmět:	Computational Theory and Mathematics Scale (ratio) Hardware and Architecture Computer science Computation Signal Processing Scalability Systems architecture Parallel algorithm Cluster (physics) Degree of a polynomial Parallel computing Supercomputer
Zdroj:	IEEE Transactions on Parallel and Distributed Systems. 32:2623-2626
ISSN:	2161-9883 1045-9219
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ff20fdd22a48c5e631216c2cb41ba206 https://doi.org/10.1109/tpds.2021.3051725 Zobrazit plný text záznamu