General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case
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Otros documentos de la autoría: Iakymchuk, Roman; Graillat, Stef; Aliaga Estellés, José Ignacio
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Título
General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab CaseFecha de publicación
2023Editor
SpringerISBN
978-3-031-30441-5; 978-3-031-30442-2Cita bibliográfica
IAKYMCHUK, Roman; GRAILLAT, Stef; ALIAGA, José I. General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case. En International Conference on Parallel Processing and Applied Mathematics. Cham: Springer International Publishing, 2022. p. 16-29.Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
https://link.springer.com/chapter/10.1007/978-3-031-30442-2_2Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Parallel implementations of Krylov subspace algorithms often help to accelerate the procedure to find the solution of a linear system. However, from the other side, such parallelization coupled with
asynchronous and ... [+]
Parallel implementations of Krylov subspace algorithms often help to accelerate the procedure to find the solution of a linear system. However, from the other side, such parallelization coupled with
asynchronous and out-of-order execution often enlarge the non-associativity
of floating-point operations. This results in non-reproducibility on the
same or different settings. This paper proposes a general framework for
deriving reproducible and accurate variants of a Krylov subspace algorithm. The proposed algorithmic strategies are reinforced by programmability suggestions to assure deterministic and accurate executions. The
framework is illustrated on the preconditioned BiCGStab method for the
solution of non-symmetric linear systems with message-passing. Finally,
we verify the two reproducible variants of PBiCGStab on a set matrices
from the SuiteSparse Matrix Collection and a 3D Poisson’s equation. [-]
Publicado en
International Conference on Parallel Processing and Applied MathematicsEntidad financiadora
Agencia Estatal de Investigación | European Union’s Horizon 2020
Código del proyecto o subvención
PID2020- 113656RB-C21 | MCIN/AEI/10.13039/501100011033 | ANR-20-CE46-0009 | info:eu-repo/grantAgreement/EC/H2020/842528
Derechos de acceso
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
R. Wyrzykowski et al. (Eds.): PPAM 2022, LNCS 13826, pp. 16–29, 2023.
https://doi.org/10.1007/978-3-031-30442-2_2
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