Open Access

ARTICLE

Parallelization and I/O Performance Optimization of a Global Nonhydrostatic Dynamical Core Using MPI

Tiejun Wang¹, Liu Zhuang², Julian M. Kunkel³, Shu Xiao¹, Changming Zhao^{1, *}

1 School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
2 National Supercomputing Center in Wuxi, Wuxi, 214072, China.
3 Department of Computer Science, University of Reading, Berkshire, RG6 6UR, UK.

* Corresponding Author: Changming Zhao. Email: .

Computers, Materials & Continua 2020, 63(3), 1399-1413. https://doi.org/10.32604/cmc.2020.09701

Received 18 January 2020; Accepted 28 February 2020; Issue published 30 April 2020

Abstract

The Global-Regional Integrated forecast System (GRIST) is the nextgeneration weather and climate integrated model dynamic framework developed by Chinese Academy of Meteorological Sciences. In this paper, we present several changes made to the global nonhydrostatic dynamical (GND) core, which is part of the ongoing prototype of GRIST. The changes leveraging MPI and PnetCDF techniques were targeted at the parallelization and performance optimization to the original serial GND core. Meanwhile, some sophisticated data structures and interfaces were designed to adjust flexibly the size of boundary and halo domains according to the variable accuracy in parallel context. In addition, the I/O performance of PnetCDF decreases as the number of MPI processes increases in our experimental environment. Especially when the number exceeds 6000, it caused system-wide outages (SWO). Thus, a grouping solution was proposed to overcome that issue. Several experiments were carried out on the supercomputing platform based on Intel x86 CPUs in the National Supercomputing Center in Wuxi. The results demonstrated that the parallel GND core based on grouping solution achieves good strong scalability and improves the performance significantly, as well as avoiding the SWOs.

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Keywords

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