PETSc

The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself.


References in zbMATH (referenced in 663 articles , 2 standard articles )

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  1. Baiges, Joan; Bayona, Camilo: RefficientLib: an efficient load-rebalanced adaptive mesh refinement algorithm for high-performance computational physics meshes (2017)
  2. Beirão da Veiga, L.; Pavarino, L.F.; Scacchi, S.; Widlund, O.B.; Zampini, S.: Adaptive selection of primal constraints for isogeometric BDDC deluxe preconditioners (2017)
  3. Besse, Christophe; Xing, Feng: Schwarz waveform relaxation method for one-dimensional Schrödinger equation with general potential (2017)
  4. Chang, J.; Karra, S.; Nakshatrala, K.B.: Large-scale optimization-based non-negative computational framework for diffusion equations: parallel implementation and performance studies (2017)
  5. Cher, Yuri; Simpson, Gideon; Sulem, Catherine: Local structure of singular profiles for a derivative nonlinear Schrödinger equation (2017)
  6. Henríquez, Fernando; Jerez-Hanckes, Carlos; Altermatt, Fernando: Boundary integral formulation and semi-implicit scheme coupling for modeling cells under electrical stimulation (2017)
  7. Kaiser, Klaus; Schütz, Jochen; Schöbel, Ruth; Noelle, Sebastian: A new stable splitting for the isentropic Euler equations (2017)
  8. Ulrich Wilbrandt, Clemens Bartsch, Naveed Ahmed, Najib Alia, Felix Anker, Laura Blank, Alfonso Caiazzo, Sashikumaar Ganesan, Swetlana Giere, Gunar Matthies, Raviteja Meesala, Abdus Shamim, Jagannath Venkatesan, Volker John: ParMooN - a modernized program package based on mapped finite elements (2017) arXiv
  9. Yang, Haijian; Cai, Xiao-Chuan: Two-level space-time domain decomposition methods for flow control problems (2017)
  10. Alvarez Laguna, A.; Lani, A.; Deconinck, H.; Mansour, N.N.; Poedts, S.: A fully-implicit finite-volume method for multi-fluid reactive and collisional magnetized plasmas on unstructured meshes (2016)
  11. Ambruş, Victor E.; Sofonea, Victor: Lattice Boltzmann models based on half-range Gauss-Hermite quadratures (2016)
  12. Augustin, Christoph M.; Neic, Aurel; Liebmann, Manfred; Prassl, Anton J.; Niederer, Steven A.; Haase, Gundolf; Plank, Gernot: Anatomically accurate high resolution modeling of human whole heart electromechanics: A strongly scalable algebraic multigrid solver method for nonlinear deformation (2016)
  13. Badia, Santiago; Martín, Alberto F.; Principe, Javier: Multilevel balancing domain decomposition at extreme scales (2016)
  14. Bangerth, Wolfgang; Davydov, Denis; Heister, Timo; Heltai, Luca; Kanschat, Guido; Kronbichler, Martin; Maier, Matthias; Turcksin, Bruno; Wells, David: The deal.II library, version 8.4 (2016)
  15. Bauman, Paul T.; Stogner, Roy H.: GRINS: a multiphysics framework based on the libMesh finite element library (2016) ioport
  16. Beilina, Larisa; Hosseinzadegan, Samar: An adaptive finite element method in reconstruction of coefficients in Maxwell’s equations from limited observations. (2016)
  17. Berger-Vergiat, Luc; McAuliffe, Colin; Waisman, Haim: Parallel preconditioners for monolithic solution of shear bands (2016)
  18. Bull, Jonathan R.; Jameson, Antony: Explicit filtering and exact reconstruction of the sub-filter stresses in large eddy simulation (2016)
  19. Campos, Carmen; Roman, Jose E.: Parallel Krylov solvers for the polynomial eigenvalue problem in SLEPc (2016)
  20. Campos, Carmen; Roman, Jose E.: Restarted Q-Arnoldi-type methods exploiting symmetry in quadratic eigenvalue problems (2016)

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