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首页> 外文会议>2016 5th International Conference on Informatics, Electronics and Vision >Solution of the problem of biological rehabilitation of shallow waters on multiprocessor computer system
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Solution of the problem of biological rehabilitation of shallow waters on multiprocessor computer system

机译：在多处理器计算机系统上解决浅水区生物修复问题

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The paper covers problems of solving tasks of water ecology on multiprocessor computer system (MCS). We proposed a new model of biological rehabilitation of shallow waters in view the factors that have a significant influence on the water quality. Its discretization was performed with using the balance method and the implicit scheme with central differences. The proposed numerical method for the solution of the model problem is most common and is suitable to the study of hydrobiological processes occurring in shallow waters. Since it allows to correctly design computational algorithms on the boundary between the integration domain and environments. One of the objectives of the work is reducing the calculation time and saving the accuracy of the results of solving problem of biological rehabilitation of shallow waters by using a multiprocessor computer system. Two algorithms have been developed in the implementation of the parallel algorithm for solving problem on the MCS for the distribution of data between the processors. There is the algorithm on the basis of the k-means method, based on the minimization of the functional of the total sample variance of scatter elements about the center of gravity of the subdomains, which allows increasing the efficiency of the parallel algorithm of the hydrobiology problem of shallow water. Using the MCS can significantly reduce the calculation time while saving the accuracy of the solution. The latter fact provides the fast and qualitative interpretation of hydrobiological data.
机译：本文涵盖了在多处理器计算机系统（MCS）上解决水生态学任务的问题。鉴于影响水质的因素，我们提出了一种新的浅水生物修复模型。使用平衡法和具有中心差异的隐式方案进行离散化。所提出的解决模型问题的数值方法是最常用的，并且适合于研究浅水区发生的水生生物过程。因为它允许在集成域和环境之间的边界上正确设计计算算法。该工作的目的之一是减少计算时间并通过使用多处理器计算机系统来节省解决浅水生物修复问题的结果的准确性。在并行算法的实现中已经开发了两种算法，用于解决MCS上处理器之间数据分配的问题。有一种基于k均值方法的算法，该算法基于最小化散布元素围绕子域重心的总样本方差的函数，从而可以提高水生生物学并行算法的效率浅水问题。使用MCS可以大大减少计算时间，同时节省了解决方案的准确性。后一个事实为水生生物学数据提供了快速而定性的解释。

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