Iterative-method performance evaluation for multiple vectors associated with a large-scale sparse matrix

Seigo Imamura, Kenji Ono, Mitsuo Yokokawa

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    Ensemble computing, which is an instance of capacity computing, is an effective computing scenario for exascale parallel supercomputers. In ensemble computing, there are multiple linear systems associated with a common coefficient matrix. We improve the performance of iterative solvers for multiple vectors by solving them at the same time, that is, by solving for the product of the matrices. We implemented several iterative methods and compared their performance. The maximum performance on Sparc VIIIfx was 7.6 times higher than that of a naïve implementation. Finally, to deal with the different convergence processes of linear systems, we introduced a control method to eliminate the calculation of already converged vectors.

    Original languageEnglish
    Pages (from-to)395-401
    Number of pages7
    JournalInternational Journal of Computational Fluid Dynamics
    Volume30
    Issue number6
    DOIs
    Publication statusPublished - Jul 2 2016

    All Science Journal Classification (ASJC) codes

    • Computational Mechanics
    • Aerospace Engineering
    • Condensed Matter Physics
    • Energy Engineering and Power Technology
    • Mechanics of Materials
    • Mechanical Engineering

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