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 1116 articles , 2 standard articles )

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  1. Ahrabi, Behzad R.; Mavriplis, Dimitri J.: An implicit block ILU smoother for preconditioning of Newton-Krylov solvers with application in high-order stabilized finite-element methods (2020)
  2. Balakrishna, Naveen; Mathew, Joseph; Samanta, Arnab: Inviscid and viscous global stability of vortex rings (2020)
  3. Bazilevs, Yuri; Kamensky, David; Moutsanidis, Georgios; Shende, Shaunak: Residual-based shock capturing in solids (2020)
  4. Bin Zubair Syed, H.; Farquharson, C.; MacLachlan, S.: Block preconditioning techniques for geophysical electromagnetics (2020)
  5. Brewster, Jack; Juniper, Matthew P.: Shape sensitivity of eigenvalues in hydrodynamic stability, with physical interpretation for the flow around a cylinder (2020)
  6. Campos, Carmen; Roman, Jose E.: A polynomial Jacobi-Davidson solver with support for non-monomial bases and deflation (2020)
  7. Casquero, Hugo; Wei, Xiaodong; Toshniwal, Deepesh; Li, Angran; Hughes, Thomas J. R.; Kiendl, Josef; Zhang, Yongjie Jessica: Seamless integration of design and Kirchhoff-Love shell analysis using analysis-suitable unstructured T-splines (2020)
  8. Chung, Hayoung; Amir, Oded; Kim, H. Alicia: Level-set topology optimization considering nonlinear thermoelasticity (2020)
  9. Çuğu, İlke; Manguoğlu, Murat: A parallel multithreaded sparse triangular linear system solver (2020)
  10. Damiani, Leonardo Hax; Kosakowski, Georg; Glaus, Martin A.; Churakov, Sergey V.: A framework for reactive transport modeling using FEniCS-Reaktoro: governing equations and benchmarking results (2020)
  11. Degen, Denise; Veroy, Karen; Wellmann, Florian: Certified reduced basis method in geosciences. Addressing the challenge of high-dimensional problems (2020)
  12. Denner, Fabian; Evrard, Fabien; van Wachem, Berend G. M.: Conservative finite-volume framework and pressure-based algorithm for flows of incompressible, ideal-gas and real-gas fluids at all speeds (2020)
  13. Egan, Raphael; Gibou, Frédéric: xGFM: recovering convergence of fluxes in the ghost fluid method (2020)
  14. Fabien, Maurice S.; Knepley, Matthew; Riviere, Beatrice: A high order hybridizable discontinuous Galerkin method for incompressible miscible displacement in heterogeneous media (2020)
  15. Farrell, Patrick E.; Gazca-Orozco, P. A.; Süli, Endre: Numerical analysis of unsteady implicitly constituted incompressible fluids: 3-field formulation (2020)
  16. Feppon, F.; Allaire, G.; Dapogny, C.; Jolivet, P.: Topology optimization of thermal fluid-structure systems using body-fitted meshes and parallel computing (2020)
  17. Ferrero, Andrea; Iollo, Angelo; Larocca, Francesco: Field inversion for data-augmented RANS modelling in turbomachinery flows (2020)
  18. Groen, Jeroen P.; Stutz, Florian C.; Aage, Niels; Bærentzen, Jakob A.; Sigmund, Ole: De-homogenization of optimal multi-scale 3D topologies (2020)
  19. Guo, Liwei; Vardakis, John C.; Chou, Dean; Ventikos, Yiannis: A multiple-network poroelastic model for biological systems and application to subject-specific modelling of cerebral fluid transport (2020)
  20. Hartwig Anzt, Terry Cojean, Yen-Chen Chen, Goran Flegar, Fritz Göbel, Thomas Grützmacher, Pratik Nayak, Tobias Ribizel, Yu-Hsiang Tsai: Ginkgo: A high performance numerical linear algebra library (2020) not zbMATH

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