Scalable Clustering Using Graphics Processors

  • Feng Cao
  • Anthony K. H. Tung
  • Aoying Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proc. of VLDB (2003)Google Scholar
  2. 2.
    Babcock, B., Datar, M., Motwani, R., O’Callaghan, L.: Maintaining variance and k-medians over data stream windows. In: Proc. of PODS (2003)Google Scholar
  3. 3.
    Baciu, G., Wong, S., Sun, H.: Recode: An image-based collision detection algorithm. Visualization and Computer Animation 10(4), 181–192 (1999)CrossRefGoogle Scholar
  4. 4.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm fordiscovering clusters in large spatial databases with noise. In: Proc. of KDD (1996)Google Scholar
  5. 5.
    Govindaraju, N.K., Lloyd, B., Wang, W., Lin, M., et al.: Fast computation of database operations using graphics processors. In: Proc. of SIGMOD (2004)Google Scholar
  6. 6.
    Govindaraju, N.K., Raghuvanshi, N., Manocha, D.: Fast and approximate stream mining of quantiles and frequencies using graphics processors. In: Proc. Of SIGMOD (2005)Google Scholar
  7. 7.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams:theory and practice. In: IEEE TKDE, pp. 515–528 (2003)Google Scholar
  8. 8.
    Guha, S., Rastogi, R., Shim, K.: Cure: An efficient clustering algorithm for large databases. In: Proc. of SIGMOD, pp. 73–84 (1998)Google Scholar
  9. 9.
    Hall, J.D., Hart, J.C.: Gpu acceleration of iterative clustering. In: Proc. Of SIGGRAPH poster (2004)Google Scholar
  10. 10.
    Hoff III, K.E., Keyser, J., Lin, M., Manocha, D., Culver, T.: Fast computation of generalized voronoi diagrams using graphics hardware. In: Proc. of SIGGRAPH, pp. 277–286 (1999)Google Scholar
  11. 11.
    Jain, A., Dubes, R.: Algorithms for clustering data. New Jersey (1998)Google Scholar
  12. 12.
    Larsen, E.S., McAllister, D.K.: Fast matrix multiplies using graphics hardware. In: Proc. of IEEE Supercomputing (2001)Google Scholar
  13. 13.
    Sun, C., Agrawal, D., Abbadi, A.E.: Hardware acceleration for spatial selections and joins. In: Proc. of SIGMOD, pp. 455–466 (2003)Google Scholar
  14. 14.
    Venkatasubramanian, S.: The graphics card as a stream computer. In: SIGMOD Workshop on Management and Processing of Data Streams (2003)Google Scholar
  15. 15.
    Thompson, C.J., Hahn, S., Oskin, M.: Using modern graphics architectures for general-purpose computing: A framework and analysis. In: Proc. of IEEE/ACM International Symposium on Microarchitectures, pp. 306–317 (2002)Google Scholar
  16. 16.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An efficient data clustering method for very large databases. In: Proc. of SIGMOD, pp. 103–114 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Feng Cao
    • 1
  • Anthony K. H. Tung
    • 2
  • Aoying Zhou
    • 1
  1. 1.Dept. of Computer Science and EngineeringFudan UniversityChina
  2. 2.School of ComputingNational University of SingaporeSingapore

Personalised recommendations