Parallel spatial query processing on GPUs using R-trees

Abstract

R-Trees are popular spatial indexing techniques that have been widely adopted in many geospatial applications. As commodity GPUs (Graphics Processing Units) are increasingly becoming available on personal workstations and cluster computers, there are considerable research interests in applying the massive data parallel GPGPU (General Purpose computing on GPUs) technologies to index and query large-scale geospatial data on GPUs using R-Trees. In this study, we aim at evaluating the potentials of accelerating both R-Tree bulk loading and spatial window query processing on GPUs using R-Trees. In addition to designing an efficient data layout schema for R-Trees on GPUs, we have implemented several parallel spatial window query processing techniques on GPUs using both dynamically generated R-Trees constructed on CPUs and bulk loaded R-Trees constructed on GPUs. Extensive experiments using both synthetic and real-world datasets have shown that our GPU based parallel query processing techniques using R-Trees can achieve about 10X speedups on average over 8-core CPU parallel implementations by effectively utilizing large numbers of processors and high memory bandwidth on GPUs.