Distributed Parallel Computing in Stochastic Modeling of Groundwater Systems

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 mea...

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Bibliographic Details
Published in:Ground water Vol. 51; no. 2; pp. 293 - 297
Main Authors: Dong, Yanhui, Li, Guomin, Xu, Haizhen
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.03.2013
Ground Water Publishing Company
Subjects:
basins
batch systems
China
Computation
Computer Simulation
Distributed processing
Encounters
Groundwater
Heterogeneity
hydrologic models
Indonesia
Java (programming language)
Models, Theoretical
Monte Carlo Method
Monte Carlo simulation
Parallel processing
Stochastic models
Stochastic Processes
Stochasticity
uncertainty
Beijing China
China
China, People's Rep., Beijing
Indonesia
ISSN:0017-467X, 1745-6584, 1745-6584
Online Access:Get full text
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Summary: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.
Bibliography:ark:/67375/WNG-ZBR5BD4P-4
istex:16B7B89DE8ABC5D66BA5020E8E06505A4A743841
ArticleID:GWAT967
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ISSN:0017-467X
1745-6584
1745-6584
DOI:10.1111/j.1745-6584.2012.00967.x