DocumentCode :
2705442
Title :
Memory-efficient implementation of a graphics processor-based cluster detection algorithm for large spatial databases
Author :
Thapa, Rajeev J. ; Trefftz, Christian ; Wolffe, Greg
Author_Institution :
Grand Valley State Univ., Allendale, MI, USA
fYear :
2010
fDate :
20-22 May 2010
Firstpage :
1
Lastpage :
5
Abstract :
Numerous approaches have been proposed for detecting clusters, groups of data in spatial databases. Of these, the algorithm known as Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a recent approach which has proven efficient for larger databases. Graphical Processing Units (GPUs), used originally to aid in the processing of high intensity graphics, have been found to be highly effective as general purpose parallel computing platforms. In this project, a GPU-based DBSCAN program has been implemented: the enhancement in this program allows for better memory scalability for use with very large databases. Algorithm performance, as compared to the original sequential program and to an initial GPU implementation, is investigated and analyzed.
Keywords :
computer graphic equipment; coprocessors; parallel processing; pattern clustering; visual databases; density based spatial clustering; graphic processor based cluster detection algorithm; graphical processing unit; large spatial database; memory efficient implementation; memory scalability; parallel computing; Algorithm design and analysis; Clustering algorithms; Graphics processing unit; Instruction sets; Kernel; Spatial databases; DBSCAN; Data Mining; GPU; Parallel Computing; Spatial Databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electro/Information Technology (EIT), 2010 IEEE International Conference on
Conference_Location :
Normal, IL
ISSN :
2154-0357
Print_ISBN :
978-1-4244-6873-7
Type :
conf
DOI :
10.1109/EIT.2010.5612134
Filename :
5612134
Link To Document :
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