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The Design and Realization of the Parallel Computing Platform for Digital Watershed Management

机译:数字流域管理并行计算平台的设计与实现

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Large-scale watershed systems are demanding in computing speed, so we adopt parallel computing method into it and build up a Parallel Operation Unified Platform (POUP) to control the computing process. POUP has a hierarchical structure with four layers: model layer, computing layer, control layer, and application layer from bottom to top. For the convenience of parallel programming, we also develop a Binary Tree River Encoding Method (BTREM), which transfers all streams in a watershed into a binary tree and use non-fixed-length binary code to indicate them, POUP is implemented with MPICH2 in the master-slave pattern while the massive data are stored in an Oracle database on the server. The result of our experiments on the simulation of sediment-laden and coarse sand producing region of the Yellow River, shows that POUP facilitates the control of parallel computing process, raises the computing speed and gives good results that coincides with the actual data. However, mainly because of the restriction in the read-write capability of our database, the parallel efficiency of POUP decays when the number of computing nodes adds up to a certain threshold.
机译:大型流域系统对计算速度的要求很高,因此我们采用并行计算方法,并建立了并行操作统一平台(POUP)来控制计算过程。 POUP具有一个由四层组成的层次结构:模型层,计算层,控制层和应用程序层,从下到上。为了方便并行编程,我们还开发了一种二叉树河编码方法(BTREM),该方法将分水岭中的所有流都转移到二叉树中,并使用非固定长度的二进制代码来表示它们,POUP在MPICH2中实现。主从模式,而海量数据存储在服务器上的Oracle数据库中。我们对黄河含沙量大的产沙区进行模拟的实验结果表明,POUP有利于并行计算过程的控制,提高了计算速度,并与实际数据相吻合。但是,主要是由于我们数据库的读写能力的限制,当计算节点的数量总计达到一定阈值时,POUP的并行效率会下降。

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