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Data Mining Using Graphics Processing Units

  • Christian Böhm
  • Robert Noll
  • Claudia Plant
  • Bianca Wackersreuther
  • Andrew Zherdin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5740)

Abstract

During the last few years, Graphics Processing Units (GPU) have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks such as rendering of 3D scenarios but can also be used for general numeric and symbolic computation tasks such as simulation and optimization. As major advantage, GPUs provide extremely high parallelism (with several hundred simple programmable processors) combined with a high bandwidth in memory transfer at low cost. In this paper, we propose several algorithms for computationally expensive data mining tasks like similarity search and clustering which are designed for the highly parallel environment of a GPU. We define a multidimensional index structure which is particularly suited to support similarity queries under the restricted programming model of a GPU, and define a similarity join method. Moreover, we define highly parallel algorithms for density-based and partitioning clustering. In an extensive experimental evaluation, we demonstrate the superiority of our algorithms running on GPU over their conventional counterparts in CPU.

Keywords

Index Structure Query Point Core Object Graphic Processor Similarity Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christian Böhm
    • 1
  • Robert Noll
    • 1
  • Claudia Plant
    • 2
  • Bianca Wackersreuther
    • 1
  • Andrew Zherdin
    • 2
  1. 1.University of MunichGermany
  2. 2.Technische Universität MünchenGermany

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