Exploring Spatial Indexing for Accelerated Feature Retrieval in HPC

Despite the critical role that range queries play in analysis and visualization for HPC applications, there has been no comprehensive analysis of indices that are designed to accelerate range queries and the extent to which they are viable in HPC. In this paper we present the first such evaluation, examining 20 open-source C and C++ libraries that support range queries. Contributions of this paper include answering the following questions: which of the implementations are viable in HPC, how do these libraries compare in terms of build time, query time, memory usage, and scalability, what are other trade-offs between these implementations, is there a single overall best solution, and when does a brute force solution offer the best performance? We also share key insights learned during this process that can assist both HPC application scientists and spatial index developers. While we find that there is no single best solution, three libraries, Boost, CGAL and R-tree, offer some of the best performance, scalability, memory overheads, and support for different mesh types. We find several areas where the spatial indices could be substantially improved: better performance when there are a large number of query matches, reduced memory overheads, and better support for GPUs or other accelerators.