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Processing moving object trajectories arises in many application domains andhas been addressed by practitioners in the spatiotemporal database andGeographical Information System communities. In this work, we focus on atrajectory similarity search, the distance threshold query, which finds alltrajectories within a given distance d of a search trajectory over a timeinterval. We demonstrate the performance of a multithreaded implementationwhich features the use of an R-tree index and which has high parallelefficiency (78%-90%). We introduce a GPGPU implementation which avoids the useof index-trees, and instead features a GPU-friendly indexing method. We comparethe performance of the multithreaded and GPU implementations, and show that aspeedup can be obtained using the latter. We propose two classes of algorithms,SetSplit and GreedySetSplit, to create efficient query batches that reducememory pressure and computational cost on the GPU. However, we find that usingfixed-size batches is sufficiently efficient in practice. We develop anempirical performance model for our GPGPU implementation that can be used topredict the response time of the distance threshold query. This model can beused to pick a good query batch size.

Technical Report: Parallel Distance Threshold Query Processing for Spatiotemporal Trajectory Databases on the GPU