A parallel multiselection greedy method for the radial basis function-based mesh deformation

Greedy algorithm has been widely adopted for the point selection procedure of radial basis function-based mesh deformation. However, in large deformation simulations with thousands of points selected, the greedy point selection will be too expensive and thus become a performance bottleneck. To improve the efficiency of the point selection procedure, a parallel multiselection greedy method has been developed in this paper. Multiple points are selected at each step to accelerate the convergence speed of the greedy algorithm. In addition, 2 strategies are presented to determine the specific selecting number. The parallelization of the greedy point selection is realized on the basis of a master-slave model, and a hybrid decomposition algorithm is proposed to address the load imbalance problem. Numerical benchmarks show that both our multiselection method and the parallelization could obviously improve the point selection efficiency. Specifically, total speedups of 20 and 55 are separately obtained for the 3D undulating fish with 10(6) cell mesh and the 3D rotating hydrofoil with 11 million cell mesh.