PARDISO

The package PARDISO is a thread-safe, high-performance, robust, memory efficient and easy to use software for solving large sparse symmetric and unsymmetric linear systems of equations on shared-memory and distributed-memory multiprocessors. The solver has has been licensed to thousands of researchers at international scientific laboratories and universities since its first release in 2004. Features of the library version: Unsymmetric, structurally symmetric or symmetric systems, real or complex, positive definite or indefinite, hermitian. LU with complete pivoting. Parallel on SMPs and Cluster of SMPs. Automatic combination of iterative and direct solver algorithms to accelerate the solution process for very large three-dimensional systems.


References in zbMATH (referenced in 161 articles , 1 standard article )

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  1. Jandron, Michael A.; Ruffa, Anthony A.; Baglama, James: An asynchronous direct solver for banded linear systems (2017)
  2. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  3. Neto, D.M.; Oliveira, M.C.; Menezes, L.F.: Surface smoothing procedures in computational contact mechanics (2017)
  4. Ruthotto, Lars; Treister, Eran; Haber, Eldad: jInv -- a flexible Julia package for PDE parameter estimation (2017)
  5. Suñagua, Porfirio; Oliveira, Aurelio R.L.: A new approach for finding a basis for the splitting preconditioner for linear systems from interior point methods (2017)
  6. Zhu, Yao; Sameh, Ahmed H.: $\textPSPIKE+$: A family of parallel hybrid sparse linear system solvers (2017)
  7. Badia, Santiago; Martín, Alberto F.; Principe, Javier: Multilevel balancing domain decomposition at extreme scales (2016)
  8. Bolukbasi, Ercan Selcuk; Manguoglu, Murat: A multithreaded recursive and nonrecursive parallel sparse direct solver (2016)
  9. Cannataro, Begüm Şenses; Rao, Anil V.; Davis, Timothy A.: State-defect constraint pairing graph coarsening method for Karush-Kuhn-Tucker matrices arising in orthogonal collocation methods for optimal control (2016)
  10. Chiang, Nai-Yuan; Zavala, Victor M.: An inertia-free filter line-search algorithm for large-scale nonlinear programming (2016)
  11. De Corato, M.; Slot, J.J.M.; Hütter, M.; D’Avino, G.; Maffettone, P.L.; Hulsen, M.A.: Finite element formulation of fluctuating hydrodynamics for fluids filled with rigid particles using boundary fitted meshes (2016)
  12. Everdij, Frank P.X.; Lloberas-Valls, Oriol; Simone, Angelo; Rixen, Daniel J.; Sluys, Lambertus J.: Domain decomposition and parallel direct solvers as an adaptive multiscale strategy for damage simulation in quasi-brittle materials (2016)
  13. Fiebach, André; Glitzky, Annegret; Linke, Alexander: Convergence of an implicit Voronoi finite volume method for reaction-diffusion problems (2016)
  14. Halpern, F.D.; Ricci, P.; Jolliet, S.; Loizu, J.; Morales, J.; Mosetto, A.; Musil, F.; Riva, F.; Tran, T.M.; Wersal, C.: The GBS code for tokamak scrape-off layer simulations (2016)
  15. Harbrecht, Helmut; Loos, Florian: Optimization of current carrying multicables (2016)
  16. Kestyn, James; Polizzi, Eric; Tang, Ping Tak Peter: Feast eigensolver for non-Hermitian problems (2016)
  17. Krause, Rolf; Rigazzi, Alessandro; Steiner, Johannes: A parallel multigrid method for constrained minimization problems and its application to friction, contact, and obstacle problems (2016)
  18. Lungten, S.; Schilders, W.H.A.; Maubach, J.M.L.: Sparse block factorization of saddle point matrices (2016)
  19. Maurer, Daniel; Wieners, Christian: A scalable parallel factorization of finite element matrices with distributed Schur complements. (2016)
  20. Paszyński, Maciej: Fast solvers for mesh-based computations (2016)

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Further publications can be found at: http://www.pardiso-project.org/#references