Key Laboratory of Engineering Geomechanics, Institute of Geology and Geophysics, Chinese Academy of Sciences, P.O. BOX 9825, Beijing, China.
Ground Water. 2013 Mar;51(2):293-7. doi: 10.1111/j.1745-6584.2012.00967.x. Epub 2012 Jul 23.
Stochastic modeling is a rapidly evolving, popular approach to the study of the uncertainty and heterogeneity of groundwater systems. However, the use of Monte Carlo-type simulations to solve practical groundwater problems often encounters computational bottlenecks that hinder the acquisition of meaningful results. To improve the computational efficiency, a system that combines stochastic model generation with MODFLOW-related programs and distributed parallel processing is investigated. The distributed computing framework, called the Java Parallel Processing Framework, is integrated into the system to allow the batch processing of stochastic models in distributed and parallel systems. As an example, the system is applied to the stochastic delineation of well capture zones in the Pinggu Basin in Beijing. Through the use of 50 processing threads on a cluster with 10 multicore nodes, the execution times of 500 realizations are reduced to 3% compared with those of a serial execution. Through this application, the system demonstrates its potential in solving difficult computational problems in practical stochastic modeling.
随机建模是一种研究地下水系统不确定性和异质性的快速发展的流行方法。然而，使用蒙特卡罗类型的模拟来解决实际的地下水问题，通常会遇到计算瓶颈，阻碍了有意义的结果的获取。为了提高计算效率，研究了一种将随机模型生成与 MODFLOW 相关程序和分布式并行处理相结合的系统。该分布式计算框架称为 Java 并行处理框架，被集成到系统中，以允许在分布式和并行系统中批量处理随机模型。作为一个例子，该系统被应用于北京平谷盆地的井捕获区的随机划分。通过在具有 10 个多核节点的集群上使用 50 个处理线程，与串行执行相比，500 次实现的执行时间减少了 3%。通过这个应用，系统展示了它在解决实际随机建模中困难的计算问题方面的潜力。
