Residual Replacement in Mixed-Precision Iterative Refinement for Sparse Linear Systems
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We investigate the solution of sparse linear systems via iterative methods based on Krylov subspaces. Concretely, we combine the use of extended precision in the outer iterative refinement with a reduced precision in the inner Conjugate Gradient solver. This method is additionally enhanced with different residual replacement strategies that aim to avoid the pitfalls due to the divergence between the actual residual and the recurrence formula for this parameter computed during the iteration. Our experiments using a significant part of the SuiteSparse Matrix Collection illustrate the potential benefits of this technique from the point of view, for example, of energy and performance.
KeywordsSparse linear systems Krylov solvers Iterative refinement Mixed precision Residual replacement Performance and energy modelling
This research was partially sponsored by the EU H2020 project 732631 OPRECOMP and the CICYT project TIN2017-82972-R of the MINECO and FEDER.
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