|
|||||
|
|
Grid-Enabled Simulation-Optimization Framework for Environmental Characterization |
|
| Authors: | Baha Y Mirghani Michael E Tryby Ranji S Ranjithan Nicholas T Karonis Kumar G Mahinthakumar |
| Affiliation: | 1Engineer, Brown and Caldwell, 6955 Union Park Center, Midvale, UT 84047; formerly, Ph.D. Candidate, North Carolina State Univ., Raleigh, NC. E-mail: bahamirghani@gmail.com 2Environmental Engineer, Ecosystems Research Division, U.S. EPA, 960 College Station Rd., Athens, GA 30605; formerly, Ph.D. Candidate, North Carolina State Univ., Raleigh, NC. E-mail: trybymichael@epa.gov 3Professor, Dept. of Civil, Construction and Environmental Engineering, North Carolina State Univ., Raleigh, NC 27695-7908. E-mail: ranji@ncsu.edu 4Professor, Dept. of Computer Science, Northern Illinois Univ., DeKalb, IL 60115-2854; and, Resident Associate Guest, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439. E-mail: karonis@niu.edu; karonis@mcs.anl.gov 5Associate Professor, Dept. of Civil, Construction and Environmental Engineering, North Carolina State Univ., Raleigh, NC 27695-7908 (corresponding author). E-mail: gmkumar@ncsu.edu |
| Abstract: | Many engineering and environmental problems that involve the determination of unknown system characteristics from observation data can be categorized as inverse problems. A common approach undertaken to solve such problems is the simulation-optimization approach where simulation models are coupled with optimization or search methods. Simulation-optimization approaches, particularly in environmental characterization involving natural systems, are computationally expensive due to the complex three-dimensional simulation models required to represent these systems and the large number of such simulations involved. Emerging grid computing environments (e.g., TeraGrid) show promise for improving the computational tractability of these approaches. However, harnessing grid resources for most computational applications is a nontrivial problem due to the complex hierarchy of heterogeneous and geographically distributed resources involved in a grid. This paper reports and discusses the development and evaluation of a grid-enabled simulation-optimization framework for solving environmental characterization problems. The framework is designed in a modular fashion that simplifies coupling with simulation model executables, allowing application of simulation-optimization approaches across problem domains. The framework architecture utilizes standard communications protocols and the message passing interface with an application programming interface to establish a connection between a centralized search application and simulation models running on TeraGrid resources. Sets of performance and scalability results for solving a groundwater source history reconstruction (SHR) problem are presented. The results show that for a given set of resources, parameters controlling the granularity at various levels of parallelism play an important role in the overall parallel performance. A production run for solving the SHR problem using three geographically distributed grid resources indicates that even in a cross-site grid environment a factor of 90 speedup is possible using 140 computer processors. |
| Keywords: | Simulation models Optimization models Computation |