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An R-tree is a data structure for organizing and querying multi-dimensional non-uniform and overlapping data. Efficient parallelization of R-tree is an important problem due to societal applications such as geographic information systems (GIS), spatial database management systems, and VLSI layout which employ R-trees for spatial analysis tasks such as map-overlay. As graphics processing units (GPUs) have emerged as powerful computing platforms, these R-tree related applications demand efficient R-tree construction and search algorithms on GPUs. This problem is hard both due to (i) non-linear tree topology of the data structure itself and (ii) the unconventional single-instruction multiple-thread (SIMT) architecture of modern GPUs requiring careful engineering of a host of issues. Therefore, the current best parallelizations of R-tree on GPU has limited speedup of only about 20-fold. We present a space-efficient data structure design and a non-trivial bottom-up construction algorithm for R-tree on GPUs. This has yielded the first demonstrated 226-fold speedup in parallel construction of an R-tree on a GPU compared to one-core execution on a CPU. We also present innovative R-tree search algorithms that are designed to overcome GPU's architectural and resource limitations. The best of these algorithms gives a speed up of 91-fold to 180-fold on an R-tree with 16384 base objects for query sizes ranging from 2k to 16k.
Prasad et al. (Fri,) studied this question.