A Versatile and Efficient GPU Data Structure for Spatial Indexing

J. Schneider, P. Rautek
IEEE Transactions on Visualization and Computer Graphics / IEEE Vis Week, (2016)

A Versatile and Efficient GPU Data Structure for Spatial Indexing

Keywords

GPU-based Data Structures, Binary Index Trees, Sparse Data

Abstract

In this paper we present a novel GPU-based data structure for spatial indexing. Based on Fenwick trees—a special type of binary indexed trees—our data structure allows construction in linear time. Updates and prefixes can be computed in logarithmic time, whereas point queries require only constant time on average. Unlike competing data structures such as summed-area tables and spatial hashing, our data structure requires a constant amount of bits for each data element, and it offers unconstrained point queries. This property makes our data structure ideally suited for applications requiring unconstrained indexing of large data, such as block-storage of large and block-sparse volumes. Finally, we provide asymptotic bounds on both run-time and memory requirements, and we show applications for which our new data structure is useful.​​

This paper is copyrighted material of the IEEE.

Code

10.1109/TVCG.2016.2599043

Sources

Website PDF