AMGCL is a header-only C++ library for solving large sparse linear systems with algebraic multigrid (AMG) method. AMG is one of the most effective iterative methods for solution of equation systems arising, for example, from discretizing PDEs on unstructured grids [BrMH85], [Stue99], [TrOS01]. The method can be used as a black-box solver for various computational problems, since it does not require any information about the underlying geometry. AMG is often used not as a standalone solver but as a preconditioner within an iterative solver (e.g. Conjugate Gradients, BiCGStab, or GMRES). The library has minimal dependencies, and provides both shared-memory and distributed memory (MPI) versions of the algorithms. The AMG hierarchy is constructed on a CPU and then is transferred into one of the provided backends. This allows for transparent acceleration of the solution phase with help of OpenCL, CUDA, or OpenMP technologies. Users may provide their own backends which enables tight integration between AMGCL and the user code.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Nathan Bell, Luke N. Olson, Jacob Schroder: PyAMG: Algebraic Multigrid Solvers in Python (2022) not zbMATH
  2. Ryzhakov, P. B.; Marti, J.; Dialami, N.: A unified arbitrary Lagrangian-Eulerian model for fluid-structure interaction problems involving flows in flexible channels (2022)
  3. Demidov, D. E.: Partial reuse AMG setup cost amortization strategy for the solution of non-steady state problems (2021)
  4. Isaev, S. A.; Miau, J.-J.; Nikushchenko, D. V.; Sudakov, A. G.; Usachov, A. E.: Modeling the influence of wind shear on reducing the drag of an energy-efficient high-level structure using a throttle effect (2021)
  5. Jo, Gwanghyun; Kwak, Do Y.; Lee, Young-Ju: Locally conservative immersed finite element method for elliptic interface problems (2021)
  6. Khramchenkov, E.; Khramchenkov, M.; Demidov, D.; Garaeva, A.: Mathematical modeling and experimental study of erosion-deposition process in deformable porous media (2021)
  7. Rasmussen, Atgeirr Flø; Sandve, Tor Harald; Bao, Kai; Lauser, Andreas; Hove, Joakim; Skaflestad, Bård; Klöfkorn, Robert; Blatt, Markus; Rustad, Alf Birger; Sævareid, Ove; Lie, Knut-Andreas; Thune, Andreas: The open porous media flow reservoir simulator (2021)
  8. Thore, Carl-Johan: Topology optimization of Stokes flow with traction boundary conditions using low-order finite elements (2021)
  9. Xu, Kailai; Tartakovsky, Alexandre M.; Burghardt, Jeff; Darve, Eric: Learning viscoelasticity models from indirect data using deep neural networks (2021)
  10. Demidov, D.; Rossi, R.: Subdomain deflation combined with local AMG: a case study using AMGCL library (2020)
  11. Hashemi, Mohammad R.; Ryzhakov, Pavel B.; Rossi, Riccardo: An enriched finite element/level-set method for simulating two-phase incompressible fluid flows with surface tension (2020)
  12. Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling: PyMatting: A Python Library for Alpha Matting (2020) arXiv
  13. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  14. Isaev, Sergey; Baranov, Paul; Popov, Igor; Sudakov, Alexander; Usachov, Alexander; Guvernyuk, Sergey; Sinyavin, Alexei; Chulyunin, Alexei; Mazo, Alexander; Demidov, Dennis; Dekterev, Alexander; Gavrilov, Andrey; Shebelev, Alexander: Numerical simulation and experiments on turbulent air flow around the semi-circular profile at zero angle of attack and moderate Reynolds number (2019)
  15. Shojaei, Arman; Mossaiby, Farshid; Zaccariotto, Mirco; Galvanetto, Ugo: The meshless finite point method for transient elastodynamic problems (2017)