Abstract
In this paper we present the results of a parallelization strategy to reduce the execution time for applying Monte Carlo simulation with a large number of realizations obtained using a groundwater flow and transport model. We develop a script in Python using mpi4py, in order to execute GWMC and related programs in parallel, applying the MPI library. Our approach is to calculate the initial inputs for each realization, and run groups of these realizations in separate processors and afterwards to calculate the mean vector and the covariance matrix of them. This strategy was applied to the study of a simplified aquifer in a rectangular domain of a single layer. We report the results of speedup and efficiency for 1000, 2000 and 4000 realizations for different number of processors. Efficiencies of 0.70, 0.76 and 0.75 were obtained for 64, 64 and 96 processors, respectively. We observe a slightly improvement of the performance as the number of realizations is increased.
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