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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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
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Abstract	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.
AbstractList	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.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. 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. 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. [PUBLICATION ABSTRACT]
Author	Xu, Haizhen Li, Guomin Dong, Yanhui
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References	Franssen, H. J. H., and W. Kinzelbach. 2008. Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resources Research 44, W09408. DOI: 10.1029/2007WR006505. Feyen, L., A.M. Dessalegn, F. De Smedt, S. Gebremeskel, and O. Batelaan. 2004. Application of a Bayesian approach to stochastic delineation of capture zones. Ground Water 42, no. 4: 542-551. Zheng, C., and P.P. Wang. 1999. MT3DMS-A Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Ground-Water Systems. Documentation and User's Guide. Jacksonville, Florida: U.S. Army Corps of Engineers. Neuman, S.P. 2004. Stochastic groundwater models in practice. Stochastic Environmental Research and Risk Assessment 18, no. 4: 268-270. Deutsch, C.V., and A.G. Journel. 1998. GSLIB: Geostatistical Software Library and User's Guide, 2nd ed. New York: Oxford University Press. van Leeuwen, M., C.B.M. te Stroet, A.P. Butler, and J.A. Tompkins. 1999. Stochastic determination of the Wierden (Netherlands) capture zones. Ground Water 37, no. 1: 8-17. Li, S.G., D. McLaughlin, and H.S. Liao. 2003. A computationally practical method for stochastic groundwater modeling. Advances in Water Resources 26, no. 11: 1137-1148. Dong, Y.H., G. Li, and H.Z. Xu. 2012. An areal recharge and discharge simulating method for MODFLOW. Computers & Geosciences 42: 203-205. Zhang, Y., D.A. Benson, and B. Baeumer. 2007. Predicting the tails of breakthrough curves in regional-scale alluvial systems. Ground Water 45, no. 4: 473-484. Vassolo, S., W. Kinzelbach, and W. Schäfer. 1998. Determination of well head protection zone by stochastic inverse modeling. Journal of Hydrology 206, no. 3/4: 268-280. Pollock, D.W. 1988. Semianalytical computation of path lines for finite-difference models. Ground Water 26, no. 6: 743-750. Yeh, T.J., and J. Simunek. 2002. Stochastic fusion of information for characterizing and monitoring the Vadose Zone. Vadose Zone Journal 1, no. 2: 207-221. Dong, Y.H., and G. Li. 2009. A Parallel PCG Solver for MODFLOW. Ground Water 47, no. 6: 845-850. Lemke, L.D., W.A. Barrack, L.M. Abriola, and P. Goovaerts. 2004. Matching solute breakthrough with deterministic and stochastic aquifer models. Ground Water 42, no. 6: 920-934. 2009; 47 2004; 42 1990 2004; 18 2000 2011 1988; 26 1999; 37 1998 2002; 1 1998; 206 2003; 26 2008; 44 2007; 45 2012; 42 1999 e_1_2_7_5_1 e_1_2_7_4_1 Deutsch C.V. (e_1_2_7_2_1) 1998 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 Franssen H. J. H. (e_1_2_7_6_1) 2008; 44 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_15_1 e_1_2_7_14_1 Zheng C. (e_1_2_7_18_1) 1999 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1
References_xml	– reference: Pollock, D.W. 1988. Semianalytical computation of path lines for finite-difference models. Ground Water 26, no. 6: 743-750. – reference: Zheng, C., and P.P. Wang. 1999. MT3DMS-A Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Ground-Water Systems. Documentation and User's Guide. Jacksonville, Florida: U.S. Army Corps of Engineers. – reference: Deutsch, C.V., and A.G. Journel. 1998. GSLIB: Geostatistical Software Library and User's Guide, 2nd ed. New York: Oxford University Press. – reference: Feyen, L., A.M. Dessalegn, F. De Smedt, S. Gebremeskel, and O. Batelaan. 2004. Application of a Bayesian approach to stochastic delineation of capture zones. Ground Water 42, no. 4: 542-551. – reference: Franssen, H. J. H., and W. Kinzelbach. 2008. Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem. Water Resources Research 44, W09408. DOI: 10.1029/2007WR006505. – reference: Yeh, T.J., and J. Simunek. 2002. Stochastic fusion of information for characterizing and monitoring the Vadose Zone. Vadose Zone Journal 1, no. 2: 207-221. – reference: Li, S.G., D. McLaughlin, and H.S. Liao. 2003. A computationally practical method for stochastic groundwater modeling. Advances in Water Resources 26, no. 11: 1137-1148. – reference: Zhang, Y., D.A. Benson, and B. Baeumer. 2007. Predicting the tails of breakthrough curves in regional-scale alluvial systems. Ground Water 45, no. 4: 473-484. – reference: Lemke, L.D., W.A. Barrack, L.M. Abriola, and P. Goovaerts. 2004. Matching solute breakthrough with deterministic and stochastic aquifer models. Ground Water 42, no. 6: 920-934. – reference: Neuman, S.P. 2004. Stochastic groundwater models in practice. 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SubjectTerms	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
Title	Distributed Parallel Computing in Stochastic Modeling of Groundwater Systems
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Volume	51
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