Parallelization of Implicit CCHE2D Model using CUDA Programming Techniques

Applications of Computational Fluids Dynamics (CFD) analysis are strongly limited by computing efficiency particularly for large-scaled and long-term problems. In order to alleviate the efficiency problem, parallel computing, decomposing a large problem into multiple small problems, and solving them concurrently or simultaneously (in parallel), is increasingly adopted. In recent years, Graphics Processing Units, GPU, and particularly General-Purpose computation on Graphics Processing Units, GPGPU, have been successfully used for parallel computing in the areas of medical imaging, environmental science, and CFD, etc. The availability of a large number of processors greatly speeds up the efficiency of parallel computing. This study is aimed at parallelizing the implicit CCHE2D model (Jia et al., 2002). CCHE2D is a general depth integrated hydrodynamic model with sediment transport, water quality evaluation, chemical spill, and flood modeling capabilities. The flow model of CCHE2D is paralleled on GPU with CUDA Fortran programming techniques to improve its computational efficiency in personal computers (PC). Comparisons of numerical results and computing efficiency between the original sequential model and the parallelized model have shown that the parallelized CCHE2D model is consistent with the original model in producing reasonable results with significantly higher efficiency.

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