A Unifying Data Structure for Hierarchical Methods

Abstract

We present a data structure for supporting the access patterns required by most scientific applications that employ hierarchical methods. The data structure, termed the Distribution Independent Adaptive Tree, efficiently supports both grid-based and particle-based methods. We present efficient algorithms for most access patterns encountered in such applications: particle insertion/deletion/splitting, grid cell insertion/deletion, nearest neighbor queries, spherical region queries and computing long-range interactions. Apart from being an efficient data structure for an individual hierarchical method, the data structure is useful in applications that involve simultaneous application of multiple methods.