A hybrid parallelization approach for high resolution operational flood forecasting
Published in 20th Annual International Conference on High Performance Computing, 2013
Recommended citation: Singhal, S., Real, L. V., George, T., Aneja, S., & Sabharwal, Y. (2013). " A hybrid parallelization approach for high resolution operational flood forecasting. " In 20th Annual International Conference on High Performance Computing (pp. 405-414). IEEE. https://ieeexplore.ieee.org/document/6799142
Abstract
Accurate and timely flood forecasts are becoming highly essential due to the increased incidence of flood related disasters over the last few years. Such forecasts require a high resolution integrated flood modeling approach. In this paper, we present an integrated flood forecasting system with an automated workflow over the weather modeling, surface runoff estimation and water routing components. We primarily focus on the water routing process which is the most compute intensive phase and present two parallelization strategies to scale it up to large grid sizes. Specifically, we employ nature-inspired decomposition of a simulation domain into watershed basins and propose a master slave model of parallelization for distributed processing of the basins. We also propose an intra-basin shared memory parallelization approach using OpenMP. Empirical evaluation of the proposed parallelization strategies indicates a potential for high speedups for certain types of scenarios (e.g., speedup of 13× with 16 threads using OpenMP parallelization for the large Rio de Janeiro basin).