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 107 articles , 1 standard article )

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  1. Bienz, Amanda; Falgout, Robert D.; Gropp, William; Olson, Luke N.; Schroder, Jacob B.: Reducing parallel communication in algebraic multigrid through sparsification (2016)
  2. Gander, Martin J.; Neumüller, Martin: Analysis of a new space-time parallel multigrid algorithm for parabolic problems (2016)
  3. Huber, Markus; Gmeiner, Björn; Rüde, Ulrich; Wohlmuth, Barbara: Resilience for massively parallel multigrid solvers (2016)
  4. Kalchev, D.Z.; Lee, C.S.; Villa, U.; Efendiev, Y.; Vassilevski, P.S.: Upscaling of mixed finite element discretization problems by the spectral AMGe method (2016)
  5. Kestyn, James; Polizzi, Eric; Tang, Ping Tak Peter: Feast eigensolver for non-Hermitian problems (2016)
  6. Klawonn, Axel; Lanser, Martin; Rheinbach, Oliver: A nonlinear FETI-DP method with an inexact coarse problem (2016)
  7. Kuchta, Miroslav; Nordaas, Magne; Verschaeve, Joris C.G.; Mortensen, Mikael; Mardal, Kent-Andre: Preconditioners for saddle point systems with trace constraints coupling 2D and 1D domains (2016)
  8. Lange, Michael; Mitchell, Lawrence; Knepley, Matthew G.; Gorman, Gerard J.: Efficient mesh management in firedrake using PETSc DMPlex (2016)
  9. Lee, Barry: Parallel preconditioners and multigrid solvers for stochastic polynomial chaos discretizations of the diffusion equation at the large scale. (2016)
  10. Liu, Hui; Wang, Kun; Chen, Zhangxin: A family of constrained pressure residual preconditioners for parallel reservoir simulations. (2016)
  11. Zampini, Stefano: PCBDDC: a class of robust dual-primal methods in petsc (2016)
  12. Casoni, E.; Jérusalem, A.; Samaniego, C.; Eguzkitza, B.; Lafortune, P.; Tjahjanto, D.D.; Sáez, X.; Houzeaux, G.; Vázquez, M.: Alya: computational solid mechanics for supercomputers (2015)
  13. Hoover, Alexander; Miller, Laura: A numerical study of the benefits of driving jellyfish bells at their natural frequency (2015)
  14. Klawonn, Axel; Lanser, Martin; Rheinbach, Oliver: Toward extremely scalable nonlinear domain decomposition methods for elliptic partial differential equations (2015)
  15. Naumov, M.; Arsaev, M.; Castonguay, P.; Cohen, J.; Demouth, J.; Eaton, J.; Layton, S.; Markovskiy, N.; Reguly, I.; Sakharnykh, N.; Sellappan, V.; Strzodka, R.: AmgX: a library for GPU accelerated algebraic multigrid and preconditioned iterative methods (2015)
  16. Xiao, Yingxiong; Zhou, Zhiyang; Shu, Shi: An efficient algebraic multigrid method for quadratic discretizations of linear elasticity problems on some typical anisotropic meshes in three dimensions. (2015)
  17. Zhao, Lian; Zhao, Yonghua; Chi, Xuebin: Sparse matrix-vector multiply algorithm based on overlapping computation and communication and application in AMG (2015)
  18. Fu, Zhisong; Lewis, T.James; Kirby, Robert M.; Whitaker, Ross T.: Architecting the finite element method pipeline for the GPU (2014)
  19. Vassilevski, Panayot S.; Villa, Umberto: A mixed formulation for the Brinkman problem (2014)
  20. Vassilevski, Panayot S.; Yang, Ulrike Meier: Reducing communication in algebraic multigrid using additive variants. (2014)

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