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 325 articles , 1 standard article )

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  1. Ahmed, Zaheer; Izbassarov, Daulet; Costa, Pedro; Muradoglu, Metin; Tammisola, Outi: Turbulent bubbly channel flows: effects of soluble surfactant and viscoelasticity (2020)
  2. Aithal, Abhiram B.; Ferrante, Antonino: A fast pressure-correction method for incompressible flows over curved walls (2020)
  3. Alberto Paganini, Florian Wechsung: Fireshape: a shape optimization toolbox for Firedrake (2020) arXiv
  4. Bochkov, Daniil; Gibou, Frederic: Solving elliptic interface problems with jump conditions on Cartesian grids (2020)
  5. Bui, Quan M.; Osei-Kuffuor, Daniel; Castelletto, Nicola; White, Joshua A.: A scalable multigrid reduction framework for multiphase poromechanics of heterogeneous media (2020)
  6. Cremon, Matthias A.; Castelletto, Nicola; White, Joshua A.: Multi-stage preconditioners for thermal-compositional-reactive flow in porous media (2020)
  7. Çuğu, İlke; Manguoğlu, Murat: A parallel multithreaded sparse triangular linear system solver (2020)
  8. Dassi, F.; Scacchi, S.: Parallel block preconditioners for three-dimensional virtual element discretizations of saddle-point problems (2020)
  9. Deborde, Julien; Milcent, Thomas; Lubin, Pierre; Glockner, Stéphane: Numerical simulations of the interaction of solitary waves and elastic structures with a fully Eulerian method (2020)
  10. Farrell, P. E.; Gazca-Orozco, P. A.: An augmented Lagrangian preconditioner for implicitly constituted non-Newtonian incompressible flow (2020)
  11. Fernandez, Felipe; Barker, Andrew T.; Kudo, Jun; Lewicki, James P.; Swartz, Kenneth; Tortorelli, Daniel A.; Watts, Seth; White, Daniel A.; Wong, Jonathan: Simultaneous material, shape and topology optimization (2020)
  12. Gratien, Jean-Marc: A robust and scalable multi-level domain decomposition preconditioner for multi-core architecture with large number of cores (2020)
  13. Klawonn, Axel; Köhler, Stephan; Lanser, Martin; Rheinbach, Oliver: Computational homogenization with million-way parallelism using domain decomposition methods (2020)
  14. Kolahdouz, Ebrahim M.; Bhalla, Amneet Pal Singh; Craven, Brent A.; Griffith, Boyce E.: An immersed interface method for discrete surfaces (2020)
  15. Konshin, I. N.; Terekhov, K. M.; Vassilevski, Yu. V.: Numerical modelling via INMOST software platform (2020)
  16. Köstler, Harald; Heisig, Marco; Kohl, Nils; Kuckuk, Sebastian; Bauer, Martin; Rüde, Ulrich: Code generation approaches for parallel geometric multigrid solvers (2020)
  17. Kulkarni, Tejas; Buttay, Romain; Kasbaoui, M. Houssem; Attili, Antonio; Bisetti, Fabrizio: Reynolds number scaling of burning rates in spherical turbulent premixed flames (2020)
  18. 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)
  19. Massimo Bernaschi, Pasqua D’Ambra, Dario Pasquini: BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs (2020) not zbMATH
  20. Offermans, N.; Peplinski, A.; Marin, O.; Merzari, E.; Schlatter, P.: Performance of preconditioners for large-scale simulations using Nek5000 (2020)

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