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The VLDB Journal — The International Journal on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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The VLDB Journal — The International Journal on Very Large Data Bases
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CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
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ACM Transactions on Database Systems (TODS)
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ACM Computing Surveys (CSUR)
GEDS: GPU Execution of Continuous Queries on Spatio-Temporal Data Streams
EUC '10 Proceedings of the 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing
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ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
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The efficient processing of spatio-temporal data streams is an area of intense research. However, all methods rely on an unsuitable processor Govindaraju, 2004, namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents a performance model of the execution of spatio-temporal queries over the authors' GEDS framework Cazalas & Guha, 2010. GEDS is a scalable, Graphics Processing Unit GPU-based framework, employing computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal queries over spatio temporal data streams. Experimental evaluation shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments and demonstrates that, despite the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. To move beyond the analysis of specific algorithms over the GEDS framework, the authors developed an abstract performance model, detailing the relationship of the CPU and the GPU. From this model, they are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based applications.