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 925 articles , 2 standard articles )

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  1. Akkerman, I.; ten Eikelder, M. F. P.: Toward free-surface flow simulations with correct energy evolution: an isogeometric level-set approach with monolithic time-integration (2019)
  2. Cotter, Colin; Crisan, Dan; Holm, Darryl D.; Pan, Wei; Shevchenko, Igor: Numerically modeling stochastic Lie transport in fluid dynamics (2019)
  3. 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)
  4. Jackaman, James; Papamikos, Georgios; Pryer, Tristan: The design of conservative finite element discretisations for the vectorial modified KdV equation (2019)
  5. Mezzadri, Francesco; Galligani, Emanuele: Splitting methods for a class of horizontal linear complementarity problems (2019)
  6. Tian, Rong; Zhou, Mozhen; Wang, Jingtao; Li, Yang; An, Hengbin; Xu, Xiaowen; Wen, Longfei; Wang, Lixiang; Xu, Quan; Leng, Juelin; Xu, Ran; Zhang, Bingyin; Liu, Weijie; Mo, Zeyao: A challenging dam structural analysis: large-scale implicit thermo-mechanical coupled contact simulation on Tianhe. II. (2019)
  7. Williams, David M.: An analysis of discontinuous Galerkin methods for the compressible Euler equations: entropy and (L_2) stability (2019)
  8. Xiao, D.; Heaney, C. E.; Fang, F.; Mottet, L.; Hu, R.; Bistrian, D. A.; Aristodemou, E.; Navon, I. M.; Pain, C. C.: A domain decomposition non-intrusive reduced order model for turbulent flows (2019)
  9. Alzetta, Giovanni; Arndt, Daniel; Bangerth, Wolfgang; Boddu, Vishal; Brands, Benjamin; Davydov, Denis; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Kormann, Katharina; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 9.0 (2018)
  10. Anuprienko, D. V.; Kapyrin, I. V.: Modeling groundwater flow in unconfined conditions: numerical model and solvers’ efficiency (2018)
  11. Araujo-Cabarcas, Juan Carlos; Engström, Christian; Jarlebring, Elias: Efficient resonance computations for Helmholtz problems based on a Dirichlet-to-Neumann map (2018)
  12. Ata, Kayhan; Sahin, Mehmet: An integral equation approach for the solution of the Stokes flow with Hermite surfaces (2018)
  13. Aulisa, Eugenio; Bnà, Simone; Bornia, Giorgio: A monolithic ALE Newton-Krylov solver with multigrid-Richardson-Schwarz preconditioning for incompressible fluid-structure interaction (2018)
  14. Badia, Santiago; Martín, Alberto F.; Principe, Javier: \textttFEMPAR: an object-oriented parallel finite element framework (2018)
  15. Barajas-Solano, David A.; Tartakovsky, Alexandre M.: Probability and cumulative density function methods for the stochastic advection-reaction equation (2018)
  16. Beaude, Laurence; Beltzung, Thibaud; Brenner, Konstantin; Lopez, Simon; Masson, Roland; Smai, Farid; Thebault, Jean-Frédéric; Xing, Feng: Parallel geothermal numerical model with fractures and multi-branch wells (2018)
  17. Beilina, L.; Cristofol, M.; Li, S.; Yamamoto, M.: Lipschitz stability for an inverse hyperbolic problem of determining two coefficients by a finite number of observations (2018)
  18. Bilgen, Carola; Kopaničáková, Alena; Krause, Rolf; Weinberg, Kerstin: A phase-field approach to conchoidal fracture (2018)
  19. Brauss, K. D.; Meir, A. J.: On a parallel, 3-dimensional, finite element solver for viscous, resistive, stationary magnetohydrodynamics equations: velocity-current formulation (2018)
  20. Burstedde, Carsten; Fonseca, Jose A.; Kollet, Stefan: Enhancing speed and scalability of the ParFlow simulation code (2018)

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