Open AccessA Sparse Matrix Library with Automatic Selection of Iterative Solvers and Preconditioners
Author(s) -
Takao Sakurai,
Takahiro Katagiri,
Hisayasu Kuroda,
Ken Naono,
Mitsuyoshi Igai,
Satoshi Ohshima
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.05.300
Subject(s) - preconditioner , computer science , solver , matrix (chemical analysis) , iterative method , sparse matrix , selection (genetic algorithm) , computation , function (biology) , matrix free methods , algorithm , mathematical optimization , mathematics , artificial intelligence , gaussian , materials science , physics , quantum mechanics , evolutionary biology , biology , composite material , programming language
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 run- time 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
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