• 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

    Scaling Distributed Machine Learning with In-Network Aggregation

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1903.06701.pdf
    Size:
    995.6Kb
    Format:
    PDF
    Description:
    Preprint
    Download
    Type
    Preprint
    Authors
    Sapio, Amedeo cc
    Canini, Marco cc
    Ho, Chen-Yu cc
    Nelson, Jacob
    Kalnis, Panos cc
    Kim, Changhoon
    Krishnamurthy, Arvind
    Moshref, Masoud
    Ports, Dan R. K.
    Richtarik, Peter cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extreme Computing Research Center
    Date
    2019-02-22
    Permanent link to this record
    http://hdl.handle.net/10754/653105
    
    Metadata
    Show full item record
    Summary

    This record has been merged with an existing record at: http://hdl.handle.net/10754/631179.

    Abstract
    Training complex machine learning models in parallel is an increasinglyimportant workload. We accelerate distributed parallel training by designing acommunication primitive that uses a programmable switch dataplane to execute akey step of the training process. Our approach, SwitchML, reduces the volume ofexchanged data by aggregating the model updates from multiple workers in thenetwork. We co-design the switch processing with the end-host protocols and MLframeworks to provide a robust, efficient solution that speeds up training byup to 300%, and at least by 20% for a number of real-world benchmark models.
    Publisher
    arXiv
    arXiv
    1903.06701
    Additional Links
    https://arxiv.org/abs/1903.06701
    https://arxiv.org/pdf/1903.06701
    Collections
    Preprints; Extreme Computing Research Center; Computer Science 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.