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 1007 articles , 2 standard articles )

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  1. Abbate, Emanuela; Iollo, Angelo; Puppo, Gabriella: An asymptotic-preserving all-speed scheme for fluid dynamics and nonlinear elasticity (2019)
  2. Abduljabbar, Mustafa; Farhan, Mohammed Al; Al-Harthi, Noha; Chen, Rui; Yokota, Rio; Bagci, Hakan; Keyes, David: Extreme scale FMM-accelerated boundary integral equation solver for wave scattering (2019)
  3. 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)
  4. Badia, Santiago; Martín, Alberto F.; Nguyen, Hieu: Physics-based balancing domain decomposition by constraints for multi-material problems (2019)
  5. Berger-Vergiat, Luc; Chen, Xiaocui; Waisman, Haim: Explicit and implicit methods for shear band modeling at high strain rates (2019)
  6. Bochkov, Daniil; Gibou, Frederic: Solving Poisson-type equations with Robin boundary conditions on piecewise smooth interfaces (2019)
  7. Brown, Jed; He, Yunhui; Maclachlan, Scott: Local Fourier analysis of balancing domain decomposition by constraints algorithms (2019)
  8. Chien, Yu-Tse; Hwang, Feng-Nan: A Markov chain-based multi-elimination preconditioner for elliptic PDE problems (2019)
  9. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in python using sfepy (2019)
  10. Colli-Franzone, P.; Gionti, V.; Pavarino, L. F.; Scacchi, S.; Storti, C.: Role of infarct scar dimensions, border zone repolarization properties and anisotropy in the origin and maintenance of cardiac reentry (2019)
  11. Cotter, Colin; Crisan, Dan; Holm, Darryl D.; Pan, Wei; Shevchenko, Igor: Numerically modeling stochastic Lie transport in fluid dynamics (2019)
  12. Crockatt, Michael M.; Christlieb, Andrew J.; Garrett, C. Kristopher; Hauck, Cory D.: Hybrid methods for radiation transport using diagonally implicit Runge-Kutta and space-time discontinuous Galerkin time integration (2019)
  13. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  14. Drzisga, Daniel; Keith, Brendan; Wohlmuth, Barbara: The surrogate matrix methodology: a priori error estimation (2019)
  15. Farrell, Patrick E.; Mitchell, Lawrence; Wechsung, Florian: An augmented Lagrangian preconditioner for the 3D stationary incompressible Navier-Stokes equations at High Reynolds number (2019)
  16. Gesenhues, Linda; Camata, José J.; Côrtes, Adriano M. A.; Rochinha, Fernando A.; Coutinho, Alvaro L. G. A.: Finite element simulation of complex dense granular flows using a well-posed regularization of the (\mu(I))-rheology (2019)
  17. Gibson, Thomas H.; McRae, Andrew T. T.; Cotter, Colin J.; Mitchell, Lawrence; Ham, David A.: Compatible finite element methods for geophysical flows. Automation and implementation using Firedrake (2019)
  18. Gjerde, Ingeborg G.; Kumar, Kundan; Nordbotten, Jan M.; Wohlmuth, Barbara: Splitting method for elliptic equations with line sources (2019)
  19. Gong, Shihua; Cai, Xiao-Chuan: A nonlinear elimination preconditioned inexact Newton method for heterogeneous hyperelasticity (2019)
  20. Green, Kevin R.; Spiteri, Raymond J.: Extended \textttBACOLI: solving one-dimensional multiscale parabolic PDE systems with error control (2019)

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