High Performance Computing of Data for a New Sensitivity Analysis Algorithm, Applied in an Air Pollution Model
Variance-based sensitivity analysis approach has been proposed for studying of input parameters’ error contribution into the output results of a large-scale air pollution model, the Danish Eulerian Model, in its unified software implementation, called UNI-DEM. A three-stage sensitivity analysis algorithm, based on analysis of variances technique (ANOVA) for calculating Sobol global sensitivity indices and computationally efficient Monte Carlo integration techniques, has recently been developed and successfully used for sensitivity analysis study of UNI-DEM with respect to several chemical reaction rate coefficients.
As a first stage it is necessary to carry out a set of computationally expensive numerical experiments and to extract the necessary multidimensional sets of sensitivity analysis data. A specially adapted for that purpose version of the model, called SA-DEM, was created, implemented and run on an IBM Blue Gene/P supercomputer, the most powerful parallel machine in Bulgaria. Its capabilities have been extended to be able to perturb the 4 different input data sets with anthropogenic emissions by regularly modified perturbation coefficients. This is a complicated and challenging computational problem even for such a powerful supercomputer like IBM BlueGene/P. Its efficient numerical solution required optimization of the parallelization strategy and improvements in the memory management. Performance results of some numerical experiments on the IBM BlueGene/P machine will be presented and analyzed.
KeywordsHigh Performance Computing Single Instruction Multiple Data Global Sensitivity Analysis Parallelization Strategy Sequential Splitting
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- 2.Dimov, I.T., Georgieva, R., Ostromsky, T., Zlatev, Z.: Sensitivity Studies of Pollutant Concentrations Calculated by UNI-DEM with Respect to the Input Emissions. Central European Journal of Mathematics, “Numerical Methods for Large Scale Scientific Computing” 11, 1531–1545 (2013)Google Scholar
- 3.Dimov, I., Ostromsky, T., Zlatev, Z.: Challenges in using splitting techniques for large-scale environmental modeling. In: Faragó, I., Georgiev, K., Havasi, Á. (eds.) Advances in Air Pollution Modeling for Environmental Security. NATO Science Series, vol. 54, pp. 115–132. Springer (2005)Google Scholar
- 4.Marchuk, G.I.: Mathematical modeling for the problem of the environment. Studies in Mathematics and Applications, vol. 16. North-Holland, Amsterdam (1985)Google Scholar
- 8.Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Halsted Press (2004)Google Scholar
- 10.Sobol, I.M.: Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation 55(1-3), 271–280 (2001)Google Scholar
- 11.WEB-site of DEM: http://www.dmu.dk/AtmosphericEnvironment/DEM