|What is PETSc
||PETSc is a suite of data structures and routines for the scalable (parallel)
solution of scientific applications modeled by partial differential equations.
It employs the MPI standard for all message-passing communication. The home
page for PETSc is located at
PETSc is intended for use in large-scale application
projects, and several ongoing computational science projects
are built around the PETSc libraries. With strict attention to component interoperability,
PETSc facilitates the integration of independently developed application modules, which
often most naturally employ different coding styles and data structures.
PETSc is easy
to use for beginners. Moreover, its careful design allows advanced users to have detailed
control over the solution process. PETSc includes an expanding suite of parallel linear
and nonlinear equation solvers that are easily used in application codes written in C,
C++, and Fortran. PETSc provides many of the mechanisms needed within parallel application
codes, such as simple parallel matrix and vector assembly routines that allow the overlap
distributed arrays. Features include:
Other features are as follows:
- Parallel vectors
- Parallel matrices
- several sparse storage formats
- easy, efficient assembly.
- Scalable parallel preconditioners
- Krylov subspace methods
- Parallel Newton-based nonlinear solvers
- Parallel timestepping (ODE) solvers
- Complete documentation
- Automatic profiling of floating point and memory usage
- Consistent interface
- Intensive error checking
- Portable to UNIX and Windows
- Over one hundred examples
- PETSc is supported and will be actively enhanced for the next several years.
PETSc components provide the functionality required for many
parallel solutions of PDEs.
- Provides the vector operations required for setting up and solving large-scale linear
and nonlinear problems. Includes easy-to-use parallel scatter and gather operations, as
well as special-purpose code for handling ghost points for regular data structures.
- A large suite of data structures and code for the manipulation of parallel sparse
matrices. Includes four different parallel matrix data structures, each appropriate for a
different class of problems.
- A collection of sequential and parallel preconditioners, including (sequential) ILU(k),
LU, and (both sequential and parallel) block Jacobi, overlapping additive Schwarz methods
and (through BlockSolve95) ILU(0) and ICC(0).
- Parallel implementations of many popular Krylov subspace iterative methods, including
GMRES, CG, CGS, Bi-CG-Stab, two variants of TFQMR, CR, and LSQR. All are coded so that
they are immediately usable with any preconditioners and any matrix data structures,
including matrix-free methods.
- Data-structure-neutral implementations of Newton-like methods for nonlinear systems.
Includes both line search and trust region techniques with a single interface. Employs by
default the above data structures and linear solvers. Users can set custom monitoring
routines, convergence criteria, etc.
- Code for the time evolution of solutions of PDEs. In addition, provides pseudo-transient
continuation techniques for computing steady-state solutions.