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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 8/2018

09.05.2018

An efficient parallel algorithm for the coupling of global climate models and regional climate models on a large-scale multi-core cluster

verfasst von: Yuzhu Wang, Jinrong Jiang, Junqiang Zhang, Juanxiong He, He Zhang, Xuebin Chi, Tianxiang Yue

Erschienen in: The Journal of Supercomputing | Ausgabe 8/2018

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Abstract

High-performance computing for climate models has always been an interesting research area. It is valuable to nest a regional climate model within a global climate model, but large-scale simulation of the nesting or coupling severely challenges to the development of efficient parallel algorithms that fit well into multi-core clusters. This paper first presents research on the coupling of the Institute of Atmospheric Physics of Chinese Academy of Sciences Atmospheric General Circulation Model version 4.0 and the Weather Research and Forecasting model, then proposes an efficient parallel algorithm of the coupling. The algorithm includes initialization of input data, decomposition of computing grid and processes, parallel computing of component models, and data exchange by a coupler. By calling some subroutines of the Model Coupling Toolkit, the parallelization of the proposed algorithm is implemented. Experiments show that the parallel algorithm is very effective and scalable. The parallel efficiency of the algorithm on 1,024 CPU cores can reach up to 70%. Moreover, its parallel efficiency with respect to weak scalability is 72.56% on a multi-core cluster.
Vorheriger Artikel CPU and GPU parallelized kernel K-means
Nächster Artikel Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm

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Metadaten
Titel
An efficient parallel algorithm for the coupling of global climate models and regional climate models on a large-scale multi-core cluster
verfasst von
Yuzhu Wang
Jinrong Jiang
Junqiang Zhang
Juanxiong He
He Zhang
Xuebin Chi
Tianxiang Yue
Publikationsdatum
09.05.2018
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 8/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-018-2406-6

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