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 176 articles , 1 standard article )

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  1. Adler, James H.; Lashuk, Ilya; MacLachlan, Scott P.: Composite-grid multigrid for diffusion on the sphere. (2018)
  2. Gibou, Frederic; Fedkiw, Ronald; Osher, Stanley: A review of level-set methods and some recent applications (2018)
  3. Huber, Markus; Rüde, Ulrich; Waluga, Christian; Wohlmuth, Barbara: Surface couplings for subdomain-wise isoviscous gradient based Stokes finite element discretizations (2018)
  4. Liu, Cheng; Hu, Changhong: An adaptive multi-moment FVM approach for incompressible flows (2018)
  5. Wang, Kun; Liu, Hui; Luo, Jia; Chen, Zhangxin: Efficient CPR-type preconditioner and its adaptive strategies for large-scale parallel reservoir simulations (2018)
  6. Allen, Jeffery; Leibs, Chris; Manteuffel, Tom; Rajaram, Harihar: A fluidity-based first-order system least-squares method for ice sheets (2017)
  7. Bauer, S.; Mohr, M.; Rüde, U.; Weismüller, J.; Wittmann, M.; Wohlmuth, B.: A two-scale approach for efficient on-the-fly operator assembly in massively parallel high performance multigrid codes (2017)
  8. Bui, Quan M.; Elman, Howard C.; Moulton, David J.: Algebraic multigrid preconditioners for multiphase flow in porous media (2017)
  9. Carsten Burstedde, Jose A. Fonseca, Stefan Kollet: Enhancing speed and scalability of the ParFlow simulation code (2017) arXiv
  10. Chávez, Gustavo; Turkiyyah, George; Keyes, David E.: A direct elliptic solver based on hierarchically low-rank Schur complements (2017)
  11. Hoover, Alexander P.; Griffith, Boyce E.; Miller, Laura A.: Quantifying performance in the medusan mechanospace with an actively swimming three-dimensional jellyfish model (2017)
  12. Jomo, John N.; Zander, Nils; Elhaddad, Mohamed; Özcan, Ali; Kollmannsberger, Stefan; Mundani, Ralf-Peter; Rank, Ernst: Parallelization of the multi-level $hp$-adaptive finite cell method (2017)
  13. Koblitz, A.R.; Lovett, S.; Nikiforakis, N.; Henshaw, W.D.: Direct numerical simulation of particulate flows with an overset grid method (2017)
  14. Kong, Fande; Cai, Xiao-Chuan: A scalable nonlinear fluid-structure interaction solver based on a Schwarz preconditioner with isogeometric unstructured coarse spaces in 3D (2017)
  15. la Cour Christensen, Max; Villa, Umberto; Engsig-Karup, Allan P.; Vassilevski, Panayot S.: Numerical multilevel upscaling for incompressible flow in reservoir simulation: an element-based algebraic multigrid (amge) approach (2017)
  16. Maddison, J.R.; Hiester, H.R.: Optimal constrained interpolation in mesh-adaptive finite element modeling (2017)
  17. Manteuffel, Thomas A.; Olson, Luke N.; Schroder, Jacob B.; Southworth, Ben S.: A root-node-based algebraic multigrid method (2017)
  18. Mardal, Kent-André; Nielsen, Bjørn Fredrik; Nordaas, Magne: Robust preconditioners for PDE-constrained optimization with limited observations (2017)
  19. Meierbachtol, Collin S.; Svyatskiy, Daniil; Delzanno, Gian Luca; Vernon, Louis J.; Moulton, J.David: An electrostatic particle-in-cell code on multi-block structured meshes (2017)
  20. Miao, Sha; Hendrickson, Kelli; Liu, Yuming: Computation of three-dimensional multiphase flow dynamics by fully-coupled immersed flow (FCIF) solver (2017)

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