A GPU-accelerated adaptive FSAI preconditioner for massively parallel simulations

نویسندگان

چکیده

The solution of linear systems equations is a central task in number scientific and engineering applications. In many cases the may take most simulation time thus representing major bottleneck further development technical software. For large scale simulations, nowadays accounting for several millions or even billions unknowns, it quite common to resort preconditioned iterative solvers exploiting their low memory requirements and, at least potential, parallelism. Approximate inverses have been shown be robust effective preconditioners various contexts. this work, we show how adaptive Factored Sparse Inverse (aFSAI), characterized by very high degree parallelism, can successfully implemented on distributed computer equipped with GPU accelerators. Taking advantage GPUs FSAI set-up not trivial task, nevertheless through an extensive numerical experimentation proposed approach outperforms more traditional results close-to-ideal behavior challenging algebra problems.

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ژورنال

عنوان ژورنال: International Journal of High Performance Computing Applications

سال: 2021

ISSN: ['1741-2846', '1094-3420']

DOI: https://doi.org/10.1177/10943420211017188