Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 528-534.

• Interdiscipline & Application • Previous Articles     Next Articles

Parallel Design and Optimization of GRAPES_CUACE On-line Coupled Air Quality Mode

YE Yue-jin1, CHEN De-xun1, HU Jiang-kai2, MA Xin2, ZHANG Xiao-ye3   

  1. (Jiangnan Institute of Computing Technology,Wuxi,Jiangsu 214083,China)1;
    (Numerical Weather Prediction Center of CMA,Beijing 100081,China)2;
    (Chinese Academy of Meteorological Sciences,Beijing 100081,China) 3
  • Online:2019-11-10 Published:2019-11-20

Abstract: This article mainly introduced the research and analysis of the parallel optimization algorithm of the meteorological particulate_meso dust aerosol coupling model under different versions of the x86 architecture.Drawing on the current mainstream parallel optimization design methods at home and abroad,combined with the GRAPES_MESO system’s own program architecture and parallel framework,corresponding parallelization transformation was implemented for different versions of x86 architecture.Using the gprof tool and poke pile timing,the test hotspot module has three main parts:IO,communication and physical process.The main optimization methods for the IO module are:1)continuous reading and writing by discrete reading and writing;2)opening buffer from sparse memory access to continuous memory access;3)asynchronous IO.The following methods are adopted for the communication part:1)the fine-grained communication is changed from fine-grained to coarse-grained;2)the aggregate communication with lower time complexity is adopted.Analysis of optimization results for IO and communication modules show that the time-consuming ratio of IO module optimization decreased from 43.7% to 1.41%.The proportion is greatly reduced,and the optimal performance is improved by 317 times.Therefore,the method described in this paper greatly improves the operating efficiency of the IO module.In addition,the main optimization methods used to optimize the physical process are as follows:1)the multi-layer cyclic calculation process is changed from discrete to continuous;2)the communication mechanism is cyclically shifted;3)the data is reused to reduce computational redundancy;4)the stack variable space is reduced.The computational performance is increased by 22%,which further improves the parallel efficiency of the program and the strong scalability of the model.

Key words: Asynchronous IO, Coarse-grained, Continuous memory access, Aggregate communicatio

CLC Number: 

  • TP302.7
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