Resumen
Grid remapping is one of the most fundamental functions in Earth simulation systems, and is essentially a kind of data interpolation. The key to an efficient interpolation method is how to quickly find the relevant grid points required for interpolation. With the rise of unstructured grid models, the demand for general and efficient interpolation search algorithms is becoming stronger and stronger. KD (K-dimensional) tree has proven to be effective in dealing with unstructured grids. However, it is unable to tackle the cyclic boundary conditions in Earth simulation systems, which restricts the application of KD tree. Taking the nearest neighbor search as an example, this paper introduces two new KD tree-based multi-dimensional data search methods, which break through the limitations of the original method with regards to the cyclic boundary. One method is based on target points duplication, and the other method is based on source points duplication. Their time complexity and space complexity are analyzed and verified by carefully designed experiments. The results show that the method based on target points duplication generally performs better than that based on source points duplication when the data are basically evenly distributed.