Title :
VDBSCAN+: Performance Optimization Based on GPU Parallelism
Author :
Valencio, Carlos Roberto ; Priolli Daniel, Guilherme ; Alves de Medeiros, Camila ; Mauro Cansian, Adriano ; Baida, Luiz Carlos ; Ferrari, Federico
Author_Institution :
Dept. de Cienc. de Comput. e Estatistica, Sao Paulo State Univ., São José do Rio Preto, Brazil
Abstract :
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.
Keywords :
graphics processing units; knowledge acquisition; parallel processing; pattern clustering; performance evaluation; visual databases; GPU parallelism techniques; VDBSCAN+; graphic processing unit; knowledge extraction; performance improvement; performance optimization; spatial databases; varied density based spatial clustering of application with noise; Algorithm design and analysis; Clustering algorithms; Data mining; Graphics processing units; Kernel; Runtime; Spatial databases; GPU (Graphics Processing Unit); VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise); spatial clustering; spatial data mining;
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2013 International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4799-2418-9
DOI :
10.1109/PDCAT.2013.11
