Grid Computing for Optimization and Sensitivity Analysis in Groundwater Engineering

In the field of groundwater engineering complex models are used to simulate environmental processes and to enable measurement planning. To improve model quality and reliability and to enable more efficient environmental planning often large numbers of model runs are required during different model lifecycles. The paper describes an approach how to use grid technology to speedup model design as well as operational tasks like modelbased optimization. Beside the background of groundwater engineering problems, a Globus Toolkit based architecture to solve such tasks is introduced and initial results are presented. I. Introduction EGARDING the field of numerical groundwater modeling (typically based on Finite-Element methods), increasingly large areas and / or complicated hydrogeological structures have to be simulated and are often represented by complex 3-dimensional FEM meshes. Concurrently the performance increase of traditional single CPU systems is beginning to decelerate. Therefore, new techniques have to be used to solve complex groundwater modeling tasks on parallel computing resources. Two principal ways to speed up groundwater simulation can be used: applying fine-grained parallelism in the core of the simulation software itself or to execute several simulations simultaneously, thus utilizing parallelism on a more coarse-grained level. The former can be used to potentially speed up any simulation, but requires changes to the solver code. The latter can be applied, whenever several simulations would have to be run consecutively in a traditional, sequential setup and the input of one simulation does not depend on the result of another. This paper will concentrate on the second parallelization approach. During the groundwater modeling workflow, series of model applications are common at different lifecycles of the model. These include the calibration of model parameters, sensitivity analysis of parameters which have to be estimated, and technical model based optimization. A typical example for the last application field is to minimize operational costs of pumping stations while still satisfying constraints like a minimum allowed depth to water table or to meet certain groundwater quality thresholds. Each of the above tasks can lead to a large number of model simulation runs, which require no interconnects apart from modifying the parameters and returning the simulation results. Hence, these tasks are perfectly suited to be executed in parallel. In the past, mainly parallel computing in groundwater modeling was rare. If it took place, networks of workstations or compute clusters were the architecture of choice. The main disadvantage of these cluster-based