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kNN-Borůvka-GPU: A Fast and Scalable MST Construction from kNN Graphs on GPU

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Computational Science and Its Applications – ICCSA 2012 (ICCSA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7333))

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Abstract

Computation of the minimum spanning tree (MST) is a common task in numerous fields of research, such as pattern recognition, computer vision, network design (telephone, electrical, hydraulic, cable TV, computer, road networks etc.), VLSI layout, to name a few. However, for a large-scale dataset when the graphs are complete, classical MST computation algorithms become unsuitable on general purpose computers. Interestingly, in such a case the k-nearest neighbor (kNN) structure can provide an efficient solution to this problem. Only a few attempts were found in the literature that focus on solving the problem using the kNNs. This paper briefs the state-of-the-art strategies for the MST problem and a fast and scalable solution combining the classical Borůvka’s MST algorithm and the kNN graph structure. The proposed algorithm is implemented for CUDA enabled GPUs kNN-Borůvka-GPU), but the basic approach is simple and adaptable to other available architectures. Speed-ups of 30-40 times compared with CPU implementation was observed for several large-scale synthetic and real world data sets.

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Author information

Authors and Affiliations

  1. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, School of EECS, FEBE, The University of Newcastle, Australia

    Ahmed Shamsul Arefin, Carlos Riveros, Regina Berretta & Pablo Moscato

Authors
  1. Ahmed Shamsul Arefin
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  2. Carlos Riveros
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  3. Regina Berretta
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  4. Pablo Moscato
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Editor information

Editors and Affiliations

  1. Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Cyber Security Science, Federal University of Technology, Gidan Kwano Campus, Minna, Nigeria

    Sanjay Misra

  4. Faculty of Engineering, Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rua Sao Francisco Xavier, 524, 50. andar, sala 5145-F, Maracana, 20, 550-013, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah

  5. Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

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Arefin, A.S., Riveros, C., Berretta, R., Moscato, P. (2012). kNN-Borůvka-GPU: A Fast and Scalable MST Construction from kNN Graphs on GPU. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31125-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-31125-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31124-6

  • Online ISBN: 978-3-642-31125-3

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