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

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 tha...

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Bibliographic Details
Published in:The Journal of supercomputing 2018-08, Vol.74 (8), p.3999-4018
Main Authors: Wang, Yuzhu, Jiang, Jinrong, Zhang, Junqiang, He, Juanxiong, Zhang, He, Chi, Xuebin, Yue, Tianxiang
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric circulation
Atmospheric General Circulation Models
Atmospheric physics
Clusters
Compilers
Computation
Computer Science
Computer simulation
Coupling
Data exchange
General circulation models
Global climate models
Interpreters
Microprocessors
Nesting
Parallel processing
Processor Architectures
Programming Languages
Scale (ratio)
Subroutines
Weather forecasting
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2406-6