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Development of a Parallel Computing Watershed Model for Flood Forecasts

机译:开发洪水预报的平行计算流域模型

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In this study, using the WASH123D (WAterSHed Systems of 1-D Stream-River Network, 2-D Overland Regime, and 3-D Subsurface Media) numerical model that was developed by the University of Florida professor Ye, its feature is the ability to combine rivers, surface and groundwater with simulation, and it can be used in variety flow. Currently WASH123D has been extended in various research projects, since WASH123D can calculate different kind of cases, the model that is necessary to set a large number of simulation parameters, so it cause a long time to compute. In the research, WASH123D model as the basis for the development of HERO (HypErcomputing wateRshed mOdel) model, HERO model decrease memory usage by reducing the computation of matrix, and add parallelizing calculations to make the subroutine that computation is huge calculate in different core. Then more cores will decrease the computation time in CPU, and then it add the infiltration equation of Green-Ampt Method in the model, reducing the subsequent calculation of the output value. The results, WASH123D mode need to modify the grid for different cases, but HERO mode can accept various cases, and the highest memory usage can be reduced 120bytes , then the method of parallel, OpenMP, effectively reduces the computation time nearly 50%.
机译:在这项研究中,使用佛罗里达大学教授叶教授开发的Wash123D(分流系统,二维河流网络,二维陆河网络,2-D陆地制度和3-D次媒体)的数值模型,其特点是能力将河流,表面和地下水结合在一起,可用于各种流动。目前Wash123D已在各种研究项目中延伸,因为WASK123D可以计算不同类型的情况,模型是设置大量仿真参数所必需的,因此它导致长时间计算。在研究中,Wash123D模型作为HERO的开发基础(超计算流域模型)模型,HERO模型通过减少矩阵的计算来降低内存使用量,并在不同核心中添加并行计算计算计算巨大计算的子程序。然后,更多的核心将减少CPU中的计算时间,然后它在模型中添加绿色AMPT方法的渗透方程,减少了后续计算输出值。结果,WASH123D模式需要修改网格的不同情况,但英雄模式可以接受各种情况,并且最高的内存使用情况可以减少120多个,然后并行,OpenMP的方法有效地降低了近50%的计算时间。

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