hypre

hypre is a software library for the solution of large, sparse linear systems on massively parallel computers. Its emphasis is on modern powerful and scalable preconditioners. hypre provides various conceptual interfaces to enable application users to access the library in the way they naturally think about their problems. This paper presents the conceptual interfaces in hypre. An overview of the preconditioners that are available in hypre is given, including some numerical results that show the efficiency of the library


References in zbMATH (referenced in 256 articles , 1 standard article )

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  1. Bui, Quan M.; Osei-Kuffuor, Daniel; Castelletto, Nicola; White, Joshua A.: A scalable multigrid reduction framework for multiphase poromechanics of heterogeneous media (2020)
  2. Çuğu, İlke; Manguoğlu, Murat: A parallel multithreaded sparse triangular linear system solver (2020)
  3. Gratien, Jean-Marc: A robust and scalable multi-level domain decomposition preconditioner for multi-core architecture with large number of cores (2020)
  4. Liu, Ju; Yang, Weiguang; Dong, Melody; Marsden, Alison L.: The nested block preconditioning technique for the incompressible Navier-Stokes equations with emphasis on hemodynamic simulations (2020)
  5. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  6. Sashikumaar Ganesan, Manan Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods (2020) arXiv
  7. Wathen, Michael; Greif, Chen: A scalable approximate inverse block preconditioner for an incompressible magnetohydrodynamics model problem (2020)
  8. Bello-Maldonado, Pedro D.; Fischer, Paul F.: Scalable low-order finite element preconditioners for high-order spectral element Poisson solvers (2019)
  9. Bochkov, Daniil; Gibou, Frederic: Solving Poisson-type equations with Robin boundary conditions on piecewise smooth interfaces (2019)
  10. Bootland, Niall; Bentley, Alistair; Kees, Christopher; Wathen, Andrew: Preconditioners for two-phase incompressible Navier-Stokes flow (2019)
  11. Cerveny, Jakub; Dobrev, Veselin; Kolev, Tzanio: Nonconforming mesh refinement for high-order finite elements (2019)
  12. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  13. Dunbar, Oliver R. A.; Lam, Kei Fong; Stinner, Björn: Phase field modelling of surfactants in multi-phase flow (2019)
  14. Franceschini, Andrea; Paludetto Magri, Victor A.; Mazzucco, Gianluca; Spiezia, Nicolò; Janna, Carlo: A robust adaptive algebraic multigrid linear solver for structural mechanics (2019)
  15. Frantzis, C.; Grigoriadis, D. G. E.: An efficient method for two-fluid incompressible flows appropriate for the immersed boundary method (2019)
  16. Ganis, Benjamin; Pencheva, Gergina; Wheeler, Mary F.: Adaptive mesh refinement with an enhanced velocity mixed finite element method on semi-structured grids using a fully coupled solver (2019)
  17. Goreinov, Sergei A.: A note on the fast direct method for discrete elliptic problems (2019)
  18. Harbrecht, Helmut; Zaspel, Peter: On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems (2019)
  19. Hoover, Alexander P.; Porras, Antonio J.; Miller, Laura A.: Pump or coast: the role of resonance and passive energy recapture in medusan swimming performance (2019)
  20. Hu, Xiukun; Douglas, Craig C.: Performance and scalability analysis of a coupled dual porosity Stokes model implemented with FEniCS (2019)

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