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VDBSCAN+: Performance Optimization Based on GPU Parallelism

Carlos Roberto Valencio, Guilherme Priolli Daniel, Camila Alves de Medeiros, Adriano Mauro Cansian
Sao Paulo State University, Departamento de Ciencias de Computacao e Estatistica, Sao Jose do Rio Preto, Sao Paulo, Brazil
14’th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’13), 2013
@article{valencio2013vdbscan+,

   title={VDBSCAN+: Performance Optimization Based on GPU Parallelism},

   author={Val{^e}ncio, Carlos Roberto and Daniel, Guilherme Pri{‘o}lli and de Medeiros, Camila Alves and Cansian, Adriano Mauro},

   year={2013}

}

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Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this area. This work proposes a new algorithm referred to as VDBSCAN+, which derived from the algorithm VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise) and focuses on the use of parallelism techniques in GPU (Graphics Processing Unit), obtaining a significant performance improvement, by increasing the runtime by 95% in comparison with VDBSCAN.
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