In this paper, a shared-memory parallel simulation of flood modeling is presented. The model used has second-order spatial and temporal accuracy, where the Monotonic Upwind Scheme for Conservation Laws (MUSCL) method is applied for spatial discretization and the Runge-Kutta second-order method is employed for temporal discretization. A cell-centered finite-volume model is used and solved in an edge-based data structure. The model is well-balanced and able to efficiently simulate flood cases on complex topography with wet–dry problems. A cell–edge reordering strategy is designed to ease vectorization and parallelization of the code. To tackle load imbalances among threads due to wet–dry problems, a novel weighted-dynamic load balancing is proposed. The model shows accurate results, and the strategy proposed shows very good parallel efficiencies for problems of different sizes (up to 6.4 million cells/12.8  million edges) on varying numbers of cores (up to 64 cores). As such, this load balancing technique could become a promising strategy for efficient parallel simulations of real flood cases.     «

In this paper, a shared-memory parallel simulation of flood modeling is presented. The model used has second-order spatial and temporal accuracy, where the Monotonic Upwind Scheme for Conservation Laws (MUSCL) method is applied for spatial discretization and the Runge-Kutta second-order method is employed for temporal discretization. A cell-centered finite-volume model is used and solved in an edge-based data structure. The model is well-balanced and able to efficiently simulate flood cases on c...     »