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Electrical Engineering and Applied Computing pp 323–336Cite as

High Performance Computing Applied to the False Nearest Neighbors Method: Box-Assisted and kd-Tree Approaches

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 90))

Abstract

In different fields of science and engineering (medicine, economics, oceanography, biological systems, etc.) the false nearest neighbors (FNN) method has a special relevance. In some of these applications, it is important to provide the results in a reasonable time scale, thus the execution time of the FNN method has to be reduced. To achieve this goal, a multidisciplinary group formed by computer scientists and physicists are collaborative working on developing High Performance Computing implementations of one of the most popular algorithms that implement the FNN method: based on box-assisted algorithm and based on kd-tree data structure. In this paper, a comparative study of the distributed memory architecture implementations carried out in the framework of this collaboration is presented. As a result, two parallel implementations for box-assisted algorithm and one parallel implementation for the kd-tree structure are compared in terms of execution time, speed-up and efficiency. In terms of execution time, the approaches presented here are from 2 to 16 times faster than the sequential implementation, and the kd-tree approach is from 3 to 7 times faster than the box-assisted approaches.

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Acknowledgments

This work has been supported by National Projects CGL2007-66440-C04-03 and CGL2008-05688-C02-01/CLI. A short version was presented in [22]. In this version, we have introduced the algorithmic notation by the parallel implementations.

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Authors and Affiliations

  1. Albacete Research Institute of Informatics, University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain

    Julio J. Águila & Enrique Arias

  2. Applied Physics Department, University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain

    Ismael Marín, María del Mar Artigao & Juan J. Miralles

  3. Depto. Ingeniería en Computación, Universidad de Magallanes, Avda. Bulnes, 01855, Punta Arenas, Chile

    Julio J. Águila

Authors
  1. Julio J. Águila
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  2. Ismael Marín
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  3. Enrique Arias
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  4. María del Mar Artigao
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  5. Juan J. Miralles
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Corresponding author

Correspondence to Julio J. Águila .

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Editors and Affiliations

  1. The University of Hong Kong, Hung To Road, Hong Kong, 3739, China, People's Republic

    Sio-Iong Ao

  2. School of Engineering, Applied Mathematics and Computing, Cranfield University, Cranfield, MK43 0AL, Montserrat

    Len Gelman

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Águila, J.J., Marín, I., Arias, E., del Mar Artigao, M., Miralles, J.J. (2011). High Performance Computing Applied to the False Nearest Neighbors Method: Box-Assisted and kd-Tree Approaches. In: Ao, SI., Gelman, L. (eds) Electrical Engineering and Applied Computing. Lecture Notes in Electrical Engineering, vol 90. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1192-1_27

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  • DOI: https://doi.org/10.1007/978-94-007-1192-1_27

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-1191-4

  • Online ISBN: 978-94-007-1192-1

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