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Two-dimensional (2D) models are increasingly used for inundation assesements in situations involving large domains of millions of computational elements and long-time scales of several months. Practical applications often involve a compromise between spatial accuracy and computational efficiency and to achieve the necessary spatial resolution, rather fine meshes become necessary requiring more data storage and very long computer times comparable to the real simulated process (e.g. 1 month of real-time simulation may require 1 month of computations approximately). Obviously, using conventional 2D non-parallelized models (CPU based) make simulations impractical in real project applications, but improving the performance of such complex models constitutes an important challenge not yet resolved. We present the newest developments of the RiverFLO-2D Plus model based on a fourth-generation finite volume numerical scheme on flexible triangular meshes that can run on highly efficient Graphical Processing Units (GPU’s). In order to reduce the computational load, we have implemented both OpenMP parallelization and GPU techniques. Since dealing with transient inundation flows the number of wet elements change during the simulation, we developed a dynamic task assignment to the processors that ensures a balanced their work load. Our method to control strict volume conservation (errors of Order 10-14 %) the numerical modeling of the wetting/drying fronts involves a correction step that is not fully local. This introduces an additional difficulty for the code parallelization. We present results that show that the proposed methods reducing computational time by more than 30 times in comparison to equivalent CPU implementations. We present performance tests using the latest GPU hardware technology, such as the NVIDIA K20m that show that the parallelization techniques implemented in RiverFLO-2D Plus can significantly reduce the Computational-Load/Hardware-Investment ratio by a factor of 200-300 allowing 2D model end-users to obtain the performance of a super computation infrastructure at a much lower cost.


Session R16, Model Development and Computation Technologies



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