Analysing Metric Data Structures Thinking of an Efficient GPU Implementation

  • Roberto Uribe-Paredes
  • Enrique Arias
  • Diego Cazorla
  • José Luis Sánchez
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)

Abstract

Similarity search is becoming a field of interest because it can be applied to different areas in science and engineering. In real applications, when large volumes of data are processing, query response time can be quite high. In this case, it is necessary to apply mechanisms to significantly reduce the average query response time. For that purpose, modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, the authors make a comparative study of the most popular pivot selection methods in order to stablish a set of attractive features from the point of view of future GPU implementations.

Keywords

Clustering-based methods Comparative study Data structures Metric spaces Pivot-based methods Range queries Similarity search. 

Notes

Acknowledgments

This work has been supported by the Ministerio de Ciencia e Innovación, project SATSIM (Ref: CGL2010-20787-C02-02), Spain and Research Center, University of Magallanes, Chile. Also, this work has been partially supported by CAPAP-H3 Network (TIN2010-12011-E).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Roberto Uribe-Paredes
    • 1
  • Enrique Arias
    • 2
  • Diego Cazorla
    • 2
  • José Luis Sánchez
    • 2
  1. 1.Computer Engineering DepartmentUniversity of MagallanesPunta ArenasChile
  2. 2.Computing Systems DepartmentUniversity of Castilla-La ManchaAlbaceteSpain

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