• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    08396303.pdf
    Size:
    2.529Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Abdulah, Sameh
    Ltaief, Hatem cc
    Sun, Ying cc
    Genton, Marc G. cc
    Keyes, David E. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Extreme Computing Research Center
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2018-06-26
    Online Publication Date
    2018-06-26
    Print Publication Date
    2018-12-01
    Permanent link to this record
    http://hdl.handle.net/10754/628384
    
    Metadata
    Show full item record
    Abstract
    We present ExaGeoStat, a high performance software for geospatial statistics in climate and environment modeling. In contrast to simulation based on partial differential equations derived from first-principles modeling, ExaGeoStat employs a statistical model based on the evaluation of the Gaussian log-likelihood function, which operates on a large dense covariance matrix. Generated by the parametrizable Matérn covariance function, the resulting matrix is symmetric and positive definite. The computational tasks involved during the evaluation of the Gaussian log-likelihood function become daunting as the number $n$ of geographical locations grows, as $O(n^{2})$ storage and $O(n^{3})$ operations are required. While many approximation methods have been devised from the side of statistical modeling to ameliorate these polynomial complexities, we are interested here in the complementary approach of evaluating the exact algebraic result by exploiting advances in solution algorithms and many-core computer architectures. Using state-of-the-art high performance dense linear algebra libraries associated with various leading edge parallel architectures (Intel KNLs, NVIDIA GPUs, and distributed-memory systems), ExaGeoStat raises the game for statistical applications from climate and environmental science. ExaGeoStat provides a reference evaluation of statistical parameters, with which to assess the validity of the various approaches based on approximation. The software takes a first step in the merger of large-scale data analytics and extreme computing for geospatial statistical applications, to be followed by additional complexity reducing improvements from the solver side that can be implemented under the same interface. Thus, a single uncompromised statistical model can ultimately be executed in a wide variety of emerging exascale environments.
    Citation
    Abdulah S, Ltaief H, Sun Y, Genton MM, Keyes D (2018) ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems. IEEE Transactions on Parallel and Distributed Systems: 1–1. Available: http://dx.doi.org/10.1109/tpds.2018.2850749.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank NVIDIA for hardware donations in the context of a GPU Research Center, Intel for support in the form of an Intel Parallel Computing Center award, Cray for support provided during the Center of Excellence award to the Extreme Computing Research Center at KAUST, and KAUST IT Research Computing for their hardware support on the GPU-based system. This research made use of the resources of the KAUST Supercomputing Laboratory. Finally, the authors would like to thank Alexander Litvinenko from the Extreme Computing Research Center for his valuable help.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Parallel and Distributed Systems
    DOI
    10.1109/tpds.2018.2850749
    arXiv
    1708.02835
    Additional Links
    https://ieeexplore.ieee.org/document/8396303/
    Relations
    Is Supplemented By:
    • [Dataset]
      Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., & Keyes, D. E. (2020). Large synthetic datasets for univariate geostatistical modeling [Data set]. KAUST Research Repository. https://doi.org/10.25781/KAUST-JJVCN. DOI: 10.25781/KAUST-JJVCN Handle: 10754/665000
    ae974a485f413a2113503eed53cd6c53
    10.1109/tpds.2018.2850749
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.