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Kang, H.C., Kim, S.S., and Lee, C.H. (2015) Parallel Processing with the Subsystem Synthesis Method for Efficient Vehicle Analysis. Journal of Mechanical Science & Technology, 29, 2663-2669.
https://doi.org/10.1007/s12206-015-0512-4

has been cited by the following article:

  • TITLE: A Parallelization Research for FY Satellite Rainfall Estimate Day Knock off Product Algorithm

    AUTHORS: Weixia Lin, Xiangang Zhao, Cunqun Fan, Manyun Lin, Lizi Xie

    KEYWORDS: Rainfall Estimate, Parallelization, Multithreading, MPI

    JOURNAL NAME: Atmospheric and Climate Sciences, Vol.8 No.2, April 30, 2018

    ABSTRACT: With the development of satellite remote sensing technology, more and more requirements are put forward on the timeliness and stability of the satellite weather service system. The FY satellite rainfall estimate day knock off product algorithm runs longer, about 20 minutes, which affects the estimated rainfall product generated timeliness. Research and development of parallel optimization algorithms based on the needs of satellite meteorological services and their effectiveness in practical applications are necessary ways to enhance the high-performance and high-availability capabilities of satellite meteorological services. So aiming at this problem, we started the parallel algorithm research based on the analysis of precipitation estimation algorithm. Firstly, we explained the steps of precipitation estimated date knock off product algorithm; secondly, we analyzed the four main calculation module calculating the amount of algorithms; thirdly, multithreaded parallel algorithm and MPI parallelization was designed. Finally, the multithreaded parallel and MPI parallelization were realized. Experimental results show that the multithreaded parallel and MPI parallelization algorithm could greatly improve the overall degree of computational efficiency. And, MPI parallelization mode has a higher operating efficiency. The performance of parallel processing is closely related to the architecture of the computer. From the perspective of service scheduling and product algorithms, the MPI parallelization approach is adopted to achieve the purpose of improving service quality.