A communication-avoiding parallel algorithm for the symmetric eigenvalue problem

Edgar Solomonik, Grey Ballard, James Demmel, Torsten Hoefler

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

Abstract

Many large-scale scientific computations require eigenvalue solvers in a scaling regime where efficiency is limited by data movement. We introduce a parallel algorithm for computing the eigenvalues of a dense symmetric matrix, which performs asymptotically less communication than previously known approaches. We provide analysis in the Bulk Synchronous Parallel (BSP) model with additional consideration for communication between a local memory and cache. Given sufficient memory to store c copies of the symmetric matrix, our algorithm requires Θ( √c) less interprocessor communication than previously known algorithms, for any c ≤ p1/3 when using p processors. The algorithm first reduces the dense symmetric matrix to a banded matrix with the same eigenvalues. Subsequently, the algorithm employs successive reduction to O(log p) thinner banded matrices.We employ two newparallel algorithms that achieve lower communication costs for the full-to-band and band-to-band reductions. Both of these algorithms leverage a novel QR factorization algorithm for rectangular matrices.

Original languageEnglish (US)
Title of host publicationSPAA 2017 - Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures
PublisherAssociation for Computing Machinery
Pages111-121
Number of pages11
ISBN (Electronic)9781450345934
DOIs
StatePublished - Jul 24 2017
Event29th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2017 - Washington, United States
Duration: Jul 24 2017Jul 26 2017

Publication series

NameAnnual ACM Symposium on Parallelism in Algorithms and Architectures
VolumePart F129316

Other

Other29th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2017
CountryUnited States
CityWashington
Period7/24/177/26/17

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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  • Cite this

    Solomonik, E., Ballard, G., Demmel, J., & Hoefler, T. (2017). A communication-avoiding parallel algorithm for the symmetric eigenvalue problem. In SPAA 2017 - Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 111-121). (Annual ACM Symposium on Parallelism in Algorithms and Architectures; Vol. Part F129316). Association for Computing Machinery. https://doi.org/10.1145/3087556.3087561