On the Performance, Scalability and Sensitivity Analysis of a Large Air Pollution Model

Computationally efficient sensitivity analysis of a large-scale air pollution model is an important issue we focus on in this paper. Sensitivity studies play an important role for reliability analysis of the results of complex nonlinear models as those used in the air pollution modelling. There is a number of uncertainties in the input data sets, as well as in some internal coefficients, which determine the speed of the main chemical reactions in the chemical part of the model. These uncertainties are subject to our quantitative sensitivity study. Monte Carlo and quasi-Monte Carlo algorithms are used in this study. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A large number of numerical experiments were carried out with SA-DEM on the IBM MareNostrum III at BSC - Barcelona, helped us to identify a severe performance problem with an earlier version of the code and to resolve it successfuly. The improved implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results with performance and scalability analysis of these results are presented in the paper.