• Login
    View Item 
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • View Item
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • 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

    Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Pearce, Roger
    Gokhale, Maya
    Amato, Nancy M.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2013-05
    Permanent link to this record
    http://hdl.handle.net/10754/599561
    
    Metadata
    Show full item record
    Abstract
    We present techniques to process large scale-free graphs in distributed memory. Our aim is to scale to trillions of edges, and our research is targeted at leadership class supercomputers and clusters with local non-volatile memory, e.g., NAND Flash. We apply an edge list partitioning technique, designed to accommodate high-degree vertices (hubs) that create scaling challenges when processing scale-free graphs. In addition to partitioning hubs, we use ghost vertices to represent the hubs to reduce communication hotspots. We present a scaling study with three important graph algorithms: Breadth-First Search (BFS), K-Core decomposition, and Triangle Counting. We also demonstrate scalability on BG/P Intrepid by comparing to best known Graph500 results. We show results on two clusters with local NVRAM storage that are capable of traversing trillion-edge scale-free graphs. By leveraging node-local NAND Flash, our approach can process thirty-two times larger datasets with only a 39% performance degradation in Traversed Edges Per Second (TEPS). © 2013 IEEE.
    Citation
    Pearce R, Gokhale M, Amato NM (2013) Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory. 2013 IEEE 27th International Symposium on Parallel and Distributed Processing. Available: http://dx.doi.org/10.1109/IPDPS.2013.72.
    Sponsors
    This work was partially performed under the auspices of the U.S. Department of Energy by Lawrence LivermoreNational Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-588232). Funding was partially provided byLDRD 11-ERD-008. Portions of experiments were performed at the Livermore Computing facility resources. Thisresearch used resources of the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory, whichis supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. ALCFresources provided through an INCITE 2012 award for the Fault-Oblivious Exascale Computing Environment project.This research supported in part by NSF awards CNS-0615267, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053,NSF/DNDO award 2008-DN-077-ARI018-02, by DOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363,B575366, by THECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by AwardKUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). Pearce is supported in partby a Lawrence Scholar fellowship at LLNL.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 IEEE 27th International Symposium on Parallel and Distributed Processing
    DOI
    10.1109/IPDPS.2013.72
    ae974a485f413a2113503eed53cd6c53
    10.1109/IPDPS.2013.72
    Scopus Count
    Collections
    Publications Acknowledging KAUST Support

    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.