Implementing the conjugate gradient algorithm on multi-core systems

W.A. Wiggers, V. Bakker, A.B.J. Kokkeler, G.J.M. Smit

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    18 Citations (Scopus)
    144 Downloads (Pure)


    In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an important kernel. Due to the sparseness of the matrices, the solver runs relatively slow. For digital optical tomography (DOT), a large set of linear equations have to be solved which currently takes in the order of hours on desktop computers. Our goal was to speed up the conjugate gradient solver. In this paper we present the results of applying multiple optimization techniques and exploiting multi-core solutions offered by two recently introduced architectures: Intel’s Woodcrest general purpose processor and NVIDIA’s G80 graphical processing unit. Using these techniques for these architectures, a speedup of a factor three has been achieved.
    Original languageEnglish
    Title of host publicationProceedings of the International Symposium on System-on-Chip (SoC 2007)
    EditorsJ. Nurmi, J. Takala, O. Vainio
    Place of PublicationPiscataway, NJ
    Number of pages4
    ISBN (Print)978-1-4244-1367-6, 978-1-4244-1368-3 (CD)
    Publication statusPublished - 20 Nov 2007
    EventInternational Symposium on System-on-Chip, SoC 2007 - Tampere, Finland
    Duration: 19 Nov 200721 Nov 2007


    ConferenceInternational Symposium on System-on-Chip, SoC 2007
    Abbreviated titleSoC


    • CAES-EEA: Efficient Embedded Architectures


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