Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach

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dc.contributor.author Das, Anindya
dc.contributor.author Ramamoorthy, Aditya
dc.contributor.author Ramamoorthy, Aditya
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Mathematics
dc.date 2019-02-15T03:09:31.000
dc.date.accessioned 2020-06-30T02:02:48Z
dc.date.available 2020-06-30T02:02:48Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-01-01
dc.description.abstract <p>Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings.</p>
dc.description.comments <p>This is a pre-print of the article Das, Anindya Bijoy and Aditya Ramamoorthy. "Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach." <em>arXiv preprint arXiv:1901.08716</em> (2019). Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ece_pubs/206/
dc.identifier.articleid 1207
dc.identifier.contextkey 13740807
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ece_pubs/206
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/21034
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ece_pubs/206/2019_RamamoorthyAditya_DistributedMatrix.pdf|||Fri Jan 14 22:26:27 UTC 2022
dc.subject.disciplines Databases and Information Systems
dc.subject.disciplines Signal Processing
dc.subject.disciplines Systems and Communications
dc.title Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 8f4839bd-bc09-45dd-863a-1157465ec37a
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
relation.isOrgUnitOfPublication 82295b2b-0f85-4929-9659-075c93e82c48
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