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 230 articles , 1 standard article )

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  1. Bello-Maldonado, Pedro D.; Fischer, Paul F.: Scalable low-order finite element preconditioners for High-order spectral element Poisson solvers (2019)
  2. Bochkov, Daniil; Gibou, Frederic: Solving Poisson-type equations with Robin boundary conditions on piecewise smooth interfaces (2019)
  3. Bootland, Niall; Bentley, Alistair; Kees, Christopher; Wathen, Andrew: Preconditioners for two-phase incompressible Navier-Stokes flow (2019)
  4. Cerveny, Jakub; Dobrev, Veselin; Kolev, Tzanio: Nonconforming mesh refinement for high-order finite elements (2019)
  5. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  6. Frantzis, C.; Grigoriadis, D. G. E.: An efficient method for two-fluid incompressible flows appropriate for the immersed boundary method (2019)
  7. 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)
  8. Harbrecht, Helmut; Zaspel, Peter: On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems (2019)
  9. 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)
  10. Hu, Xiukun; Douglas, Craig C.: Performance and scalability analysis of a coupled dual porosity Stokes model implemented with FEniCS (2019)
  11. Kuchta, Miroslav; Mardal, Kent-Andre; Mortensen, Mikael: Preconditioning trace coupled (3d-1d) systems using fractional Laplacian (2019)
  12. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  13. Manteuffel, Thomas A.; MüNzenmaier, Steffen; Ruge, John; Southworth, Ben: Nonsymmetric reduction-based algebraic multigrid (2019)
  14. Neumüller, Martin; Smears, Iain: Time-parallel iterative solvers for parabolic evolution equations (2019)
  15. Paludetto Magri, Victor A.; Franceschini, Andrea; Janna, Carlo: A novel algebraic multigrid approach based on adaptive smoothing and prolongation for ill-conditioned systems (2019)
  16. Pimenta, F.; Alves, M. A.: A coupled finite-volume solver for numerical simulation of electrically-driven flows (2019)
  17. Roberts, Nathan V.: Camellia: a rapid development framework for finite element solvers (2019)
  18. 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)
  19. Yu, Xiangming; Hendrickson, Kelli; Campbell, Bryce K.; Yue, Dick K. P.: Numerical investigation of shear-flow free-surface turbulence and air entrainment at large Froude and Weber numbers (2019)
  20. Adler, James H.; Lashuk, Ilya; MacLachlan, Scott P.: Composite-grid multigrid for diffusion on the sphere. (2018)

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