Amesos2
Amesos2 and Belos: Direct and Iterative Solvers for Large Sparse Linear Systems. Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples the algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.
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References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
Sorted by year (- Jolivet, Pierre; Roman, Jose E.; Zampini, Stefano: KSPHPDDM and PCHPDDM: extending PETSc with advanced Krylov methods and robust multilevel overlapping Schwarz preconditioners (2021)
- D’Elia, M.; Phipps, E.; Rushdi, A.; Ebeida, M. S.: Surrogate-based ensemble grouping strategies for embedded sampling-based uncertainty quantification (2020)
- Fang, Rui; Kronbichler, Martin; Wurzer, Maximilian; Wall, Wolfgang A.: Parallel, physics-oriented, monolithic solvers for three-dimensional, coupled finite element models of Lithium-ion cells (2019)
- Thomas, S. J.; Ananthan, S.; Yellapantula, S.; Hu, J. J.; Lawson, M.; Sprague, M. A.: A comparison of classical and aggregation-based algebraic multigrid preconditioners for high-fidelity simulation of wind turbine incompressible flows (2019)
- Esmaily, M.; Jofre, L.; Mani, A.; Iaccarino, G.: A scalable geometric multigrid solver for nonsymmetric elliptic systems with application to variable-density flows (2018)
- Lin, P. T.; Shadid, J. N.; Hu, J. J.; Pawlowski, R. P.; Cyr, E. C.: Performance of fully-coupled algebraic multigrid preconditioners for large-scale VMS resistive MHD (2018)
- Pan, Wenxiao; Kim, Kyungjoo; Perego, Mauro; Tartakovsky, Alexandre M.; Parks, Michael L.: Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics (2017)
- Phipps, E.; D’Elia, M.; Edwards, H. C.; Hoemmen, M.; Hu, J.; Rajamanickam, S.: Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures (2017)
- Deparis, Simone; Forti, Davide; Grandperrin, Gwenol; Quarteroni, Alfio: Facsi: a block parallel preconditioner for fluid-structure interaction in hemodynamics (2016)
- Hamilton, Steven P.; Evans, Thomas M.: Efficient solution of the simplified (P_N) equations (2015)
- Trask, Nathaniel; Maxey, Martin; Kim, Kyungjoo; Perego, Mauro; Parks, Michael L.; Yang, Kai; Xu, Jinchao: A scalable consistent second-order SPH solver for unsteady low Reynolds number flows (2015)