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Manycore processing of repeated k-NN queries over massive moving objects observations

Francesco Lettich, Salvatore Orlando, Claudio Silvestri
Dipartimento di Scienze Ambientali, Informatica e Statistica, Universita Ca Foscari, Via Torino 155, Venice, Italy
arXiv:1412.6170 [cs.DC], (18 Dec 2014)

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The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. In this paper we focus on a specific data-intensive problem concerning the repeated processing of huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial extents of queries and the position of objects are continuously modified over time. In particular, we propose a novel hybrid CPU/GPU pipeline that significantly accelerate query processing thanks to a combination of ad-hoc data structures and non-trivial memory access patterns. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated k-NN queries over massive sets of continuously moving objects, even characterized by highly skewed spatial distributions. In comparison with state-of-the-art sequential CPU-based implementations, our method highlights significant speedups in the order of 10x-20x, depending on the datasets, even when considering cheap GPUs.
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