kNN query processing in metric spaces using GPUs

  • Authors:

  • Ricardo J. Barrientos;José I. Gómez;Christian Tenllado;Manuel Prieto Matias;Mauricio Marin

  • Affiliations:

  • Architecture Department of Computers and Automatic, ArTeCS Group, Complutense University of Madrid, Madrid, España;Architecture Department of Computers and Automatic, ArTeCS Group, Complutense University of Madrid, Madrid, España;Architecture Department of Computers and Automatic, ArTeCS Group, Complutense University of Madrid, Madrid, España;Architecture Department of Computers and Automatic, ArTeCS Group, Complutense University of Madrid, Madrid, España;Yahoo! Research Latin America, Santiago, Chile

  • Venue:
  • Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
  • Year:
  • 2011

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Abstract

Information retrieval from large databases is becoming crucial for many applications in different fields such as content searching in multimedia objects, text retrieval or computational biology. These databases are usually indexed off-line to enable an acceleration of on-line searches. Furthermore, the available parallelism has been exploited using clusters to improve query throughput. Recently some authors have proposed the use of Graphic Processing Units (GPUs) to accelerate bruteforce searching algorithms for metric-space databases. In this work we improve existing GPU brute-force implementations and explore the viability of GPUs to accelerate indexing techniques. This exploration includes an interesting discussion about the performance of both bruteforce and indexing-based algorithms that takes into account the intrinsic dimensionality of the element of the database.