IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0074113.html
   My bibliography  Save this article

Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters

Author

Listed:
  • Ali Dashti
  • Ivan Komarov
  • Roshan M D’Souza

Abstract

This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs) and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU). The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible -NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Suggested Citation

  • Ali Dashti & Ivan Komarov & Roshan M D’Souza, 2013. "Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0074113
    DOI: 10.1371/journal.pone.0074113
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074113
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074113&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0074113?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Shamsul Arefin & Carlos Riveros & Regina Berretta & Pablo Moscato, 2012. "GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ivan Komarov & Ali Dashti & Roshan M D'Souza, 2014. "Fast k-NNG Construction with GPU-Based Quick Multi-Select," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-9, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0074113. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.