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Studies on Parallel Algorithm of Bicubic Numerical Prediction Model

Author: ZhangLei
Tutor: ZhaoJun
School: National University of Defense Science and Technology
Course: Computer Science and Technology
Keywords: Bicubic Numerical Model Atmospheric models Parallel computing Cubic spline interpolation
CLC: TP399-C3
Type: Master's thesis
Year: 2007
Downloads: 26
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The research and development of numerical weather prediction model is one of the core technology in weather forecasting . This paper describes the research background of weather prediction models , analysis of the current mainstream numerical prediction model and its characteristics ; pair of study of this subject then propose three numerical models , and discuss the importance of parallel computing in numerical weather ; concludes with an overview of current basic parallel theory . First, in-depth study Bicubic Numerical Model physical framework and mathematical framework to analyze the mathematical properties ; research model time step integration scheme and structure of the program flow , calculate the efficiency of the execution time of each module , serial mode algorithm optimization work ; time step integration scheme and its structural characteristics of the binding mode , parallel feasibility study bicubic Numerical Model proposed mode of parallel programs . Secondly, in the process of achieving mode parallel algorithm for the characteristics of the large amount of calculation , combined with the current main data division method proposed partitioning algorithm suitable for the mode of one-dimensional data ; striping data transposed parallel algorithm , effectively global grid points redistribution cubic spline fitting process ; parallel I / O methods to achieve continuous forecast data output by comparison with the POSIX I / O , parallel I / O algorithm can get greater output bandwidth. Bicubic Numerical Model parallel algorithm for testing cluster systems , studies show that the parallel algorithm has a communication simple and efficient , and easy to implement load balancing and better scalability speedup .

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