GEDS: GPU execution of spatio-temporal queries over spatio-temporal data streams

Abstract

Much research exists for the efficient processing of spatio-temporal data streams. However, all methods ultimately rely on an ill-equipped processor [22], namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous queries over spatio-temporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs.