A sparse matrix library with automatic selection of iterative solvers and preconditioners

Takao Sakurai, Takahiro Katagiri, Hisayasu Kuroda, Ken Naono, Mitsuyoshi Igai, Satoshi Ohshima

    Research output: Contribution to journalConference articlepeer-review

    5 Citations (Scopus)


    Many iterative solvers and preconditioners have recently been proposed for linear iterative matrix libraries. Currently, library users have to manually select the solvers and preconditioners to solve their target matrix. However, if they select the wrong combination of the two, they have to spend a lot of time on calculations or they cannot obtain the solution. Therefore, an approach for the automatic selection of solvers and preconditioners is needed. We have developed a function that automatically selects an effective solver/preconditioner combination by referencing the history of relative residuals at runtime to predict whether the solver will converge or stagnate. Numerical evaluation with 50 Florida matrices showed that the proposed function can select effective combinations in all matrices. This suggests that our function can play a significant role in sparse iterative matrix computations.

    Original languageEnglish
    Pages (from-to)1332-1341
    Number of pages10
    JournalProcedia Computer Science
    Publication statusPublished - 2013
    Event13th Annual International Conference on Computational Science, ICCS 2013 - Barcelona, Spain
    Duration: Jun 5 2013Jun 7 2013

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)

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