• Login
    View Item 
    •   Home
    • Research
    • Preprints
    • View Item
    •   Home
    • Research
    • Preprints
    • 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 LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    ExaGeoStatR: A Package for Large-Scale Geostatistics in R

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    1.153Mb
    Format:
    PDF
    Description:
    Pre-print
    Download
    Type
    Preprint
    Authors
    Abdulah, Sameh
    Li, Yuxiao cc
    Cao, Jian cc
    Ltaief, Hatem cc
    Keyes, David E. cc
    Genton, Marc G. cc
    Sun, Ying cc
    KAUST Department
    Extreme Computing Research Center
    Statistics Program
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Office of the President
    Date
    2019-07-23
    Permanent link to this record
    http://hdl.handle.net/10754/660678
    
    Metadata
    Show full item record
    Abstract
    Parallel computing in Gaussian process calculation becomes a necessity for avoiding computational and memory restrictions associated with Geostatistics applications. The evaluation of the Gaussian log-likelihood function requires O(n^2) storage and O(n^3) operations where n is the number of geographical locations. In this paper, we present ExaGeoStatR, a package for large-scale Geostatistics in R that supports parallel computation of the maximum likelihood function on shared memory, GPU, and distributed systems. The parallelization depends on breaking down the numerical linear algebra operations into a set of tasks and rendering them for a task-based programming model. ExaGeoStatR supports several maximum likelihood computation variants such as exact, Diagonal Super Tile (DST), and Tile Low-Rank (TLR) approximation besides providing a tool to generate large-scale synthetic datasets which can be used to test and compare different approximations methods. The package can be used directly through the R environment without any C, CUDA, or MPIknowledge. Here, we demonstrate the ExaGeoStatR package by illustrating its implementation details, analyzing its performance on various parallel architectures, and assessing its accuracy using both synthetic datasets and a sea surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). The data application was developed on the base of preliminary results obtained by J. Scott Berdahl in a class project on spatial statistics. 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 Eduardo Gonzalez Fisher, the KAUSTECRC system administrator for his support, and Samuel Kortas and Bilel Hadri from the KAUST Supercomputing Laboratory (KSL) for their valuable help.
    Publisher
    arXiv
    arXiv
    1908.06936
    Additional Links
    https://arxiv.org/pdf/1908.06936
    Collections
    Preprints; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    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.