Abstract
This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system solvers. Such a preconditioner reveals an efficient accelerator of both Conjugate gradient and BiCGstab iterative methods in the parallel solution of large linear systems arising from the discretization of the advection-diffusion equation. The resulting message passing code allows the solution of large problems leading to a very cost-effective algorithm for the solution of large and sparse linear systems.
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Bergamaschi, L., Martínez, Á. (2005). Parallel Acceleration of Krylov Solvers by Factorized Approximate Inverse Preconditioners. In: Daydé, M., Dongarra, J., Hernández, V., Palma, J.M.L.M. (eds) High Performance Computing for Computational Science - VECPAR 2004. VECPAR 2004. Lecture Notes in Computer Science, vol 3402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11403937_47
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DOI: https://doi.org/10.1007/11403937_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25424-9
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