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The ability to timely process significant amounts of continuously updatedspatial data is mandatory for an increasing number of applications. Parallelismenables such applications to face this data-intensive challenge and allows thedevised systems to feature low latency and high scalability. In this paper wefocus on a specific data-intensive problem, concerning the repeated processingof huge amounts of range queries over massive sets of moving objects, where thespatial extents of queries and objects are continuously modified over time. Totackle this problem and significantly accelerate query processing we devise ahybrid CPU/GPU pipeline that compresses data output and save query processingwork. The devised system relies on an ad-hoc spatial index leading to a problemdecomposition that results in a set of independent data-parallel tasks. Theindex is based on a point-region quadtree space decomposition and allows totackle effectively a broad range of spatial object distributions, even thosevery skewed. Also, to deal with the architectural peculiarities and limitationsof the GPUs, we adopt non-trivial GPU data structures that avoid the need oflocked memory accesses and favour coalesced memory accesses, thus enhancing theoverall memory throughput. To the best of our knowledge this is the first workthat exploits GPUs to efficiently solve repeated range queries over massivesets of continuously moving objects, characterized by highly skewed spatialdistributions. In comparison with state-of-the-art CPU-based implementations,our method highlights significant speedups in the order of 14x-20x, dependingon the datasets, even when considering very cheap GPUs.

Manycore processing of repeated range queries over massive moving objects observations