• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Straggler mitigation through unequal error protection for distributed approximate matrix multiplication

      Thumbnail
      View / Download
      2.4 Mb
      Author(s)
      Tegin, Büşra
      Hernandez, Eduin E.
      Rini, Stefano
      Duman, Tolga Mete
      Date
      2022-02-01
      Source Title
      IEEE Journal on Selected Areas in Communications
      Print ISSN
      07338716
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      40
      Issue
      2
      Pages
      468 - 483
      Language
      English
      Type
      Article
      Item Usage Stats
      3
      views
      1
      downloads
      Abstract
      Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for the computations at the agents is affected by the availability of local resources and/or poor channel conditions, thus giving rise to the “straggler problem.” In this paper, we address this problem for distributed approximate matrix multiplication. In particular, we employ Unequal Error Protection (UEP) codes to obtain an approximation of the matrix product to provide higher protection for the blocks with a higher effect on the multiplication outcome. We characterize the performance of the proposed approach from a theoretical perspective by bounding the expected reconstruction error for matrices with uncorrelated entries. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep Neural Network (DNN) for an image classification task in the evaluation of the gradients. Our numerical experiments show that it is indeed possible to obtain significant improvements in the overall time required to achieve DNN training convergence by producing approximation of matrix products using UEP codes in the presence of stragglers.
      Keywords
      Distributed computation
      Approximate matrix multiplication
      Stragglers
      Unequal error protection
      Permalink
      http://hdl.handle.net/11693/111760
      Published Version (Please cite this version)
      https://dx.doi.org/10.1109/JSAC.2021.3118350
      Collections
      • Department of Electrical and Electronics Engineering 4011
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy