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Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework

Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework

Jonathan Cazalas, Ratan K. Guha
Copyright: © 2012 |Volume: 4 |Issue: 3 |Pages: 22
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466612334|DOI: 10.4018/jghpc.2012070104
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MLA

Cazalas, Jonathan, and Ratan K. Guha. "Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework." IJGHPC vol.4, no.3 2012: pp.63-84. http://doi.org/10.4018/jghpc.2012070104

APA

Cazalas, J. & Guha, R. K. (2012). Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework. International Journal of Grid and High Performance Computing (IJGHPC), 4(3), 63-84. http://doi.org/10.4018/jghpc.2012070104

Chicago

Cazalas, Jonathan, and Ratan K. Guha. "Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework," International Journal of Grid and High Performance Computing (IJGHPC) 4, no.3: 63-84. http://doi.org/10.4018/jghpc.2012070104

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Abstract

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.

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