Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41615
Title: Scalable clustering using graphics processors
Authors: Cao, F.
Tung, A.K.H. 
Zhou, A.
Issue Date: 2006
Source: Cao, F.,Tung, A.K.H.,Zhou, A. (2006). Scalable clustering using graphics processors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4016 LNCS : 372-384. ScholarBank@NUS Repository.
Abstract: We present new algorithms for scalable clustering using graphics processors. Our basic approach is based on k-means. By changing the order of determining object labels, and exploiting the high computational power and pipeline of graphics processing units (GPUs) for distance computing and comparison, we speed up the k-means algorithm substantially. We introduce two strategies for retrieving data from the GPU, taking into account the low bandwidth from the GPU back to the main memory. We also extend our GPU-based approach to data stream clustering. We implement our algorithms in a PC with a Pentium IV 3.4G CPU and a NVIDIA GeForce 6800 GT graphics card. Our comprehensive performance study shows that the common GPU in desktop computers could be an efficient co-processor of CPU in traditional and data stream clustering. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41615
ISBN: 3540352252
ISSN: 03029743
Appears in Collections:Staff Publications

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