Coded convolution for parallel and distributed computing within a deadline

Sanghamitra Dutta, Viveck Ramesh Cadambe, Pulkit Grover

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    23 Scopus citations

    Abstract

    We consider the problem of computing the convolution of two long vectors using parallel processors in the presence of 'stragglers'. Stragglers refer to the small fraction of faulty or slow processors that delays the entire computation in time-critical distributed systems. We first show that splitting the vectors into smaller pieces and using a linear code to encode these pieces provides improved resilience against stragglers than replication-based schemes under a simple, worst-case straggler analysis. We then demonstrate that under commonly used models of computation time, coding can dramatically improve the probability of finishing the computation within a target 'deadline' time. As opposed to the more commonly used technique of expected computation time analysis, we quantify the exponents of the probability of failure in the limit of large deadlines. Our exponent metric captures the probability of failing to finish before a specified deadline time, i.e., the behavior of the 'tail'. Moreover, our technique also allows for simple closed form expressions for more general models of computation time, e.g. shifted Weibull models instead of only shifted exponentials. Thus, through this problem of coded convolution, we establish the utility of a novel asymptotic failure exponent analysis for distributed systems.

    Original languageEnglish (US)
    Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2403-2407
    Number of pages5
    ISBN (Electronic)9781509040964
    DOIs
    StatePublished - Aug 9 2017
    Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
    Duration: Jun 25 2017Jun 30 2017

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    ISSN (Print)2157-8095

    Other

    Other2017 IEEE International Symposium on Information Theory, ISIT 2017
    CountryGermany
    CityAachen
    Period6/25/176/30/17

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    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Information Systems
    • Modeling and Simulation
    • Applied Mathematics

    Cite this

    Dutta, S., Cadambe, V. R., & Grover, P. (2017). Coded convolution for parallel and distributed computing within a deadline. In 2017 IEEE International Symposium on Information Theory, ISIT 2017 (pp. 2403-2407). [8006960] (IEEE International Symposium on Information Theory - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2017.8006960