Add time:07/17/2019         Source:sciencedirect.com

Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects. In the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. Although many run-time and pre-processing-based heuristics have been proposed to alleviate this problem, the CPU cost still remains the bottleneck. In this paper, we propose a novel approach to address this problem using the efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and is applicable to both intersection and distance predicates. We evaluate this approach by comparing the performance with leading software solutions. The results show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins. We integrated this hardware/software co-processing technique into a popular database to evaluate its performance in the presence of indexes, pre-processing and other proprietary optimizations. Extensive experimentation with real-world data sets show that the hardware-accelerated technique not only outperforms the run-time software solutions but also performs as well if not better than pre-processing-assisted techniques.

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