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.
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Cao, F., Tung, A.K.H., Zhou, A. (2006). Scalable Clustering Using Graphics Processors. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_32
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DOI: https://doi.org/10.1007/11775300_32
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