OverviewPETSc 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.
PETSc is intended for use in large-scale application projects [petsc-efficient], 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 [petsc-user-ref]. 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 of communication and computation.
[petsc-user-ref]
S. Balay, S. Abhyankar, M. Adams, S. Benson, J. Brown, P. Brune, K. Buschelman, E. Constantinescu,
L. Dalcin, A. Dener, V. Eijkhout, J. Faibussowitsch, W. Gropp, V. Hapla, T. Isaac, P. Jolivet, D.
Karpeyev, D. Kaushik, M. Knepley, F. Kong, S. Kruger, D. May, L. Curfman McInnes, R. Mills, L.
Mitchell, T. Munson, J. Roman, K. Rupp, P. Sanan, J Sarich, B. Smith, S. Zampini, H. Zhang, and H.
Zhang, J. Zhang, PETSc/TAOUsersManual, ANL-21/39 - Revision 3.19, 2023.
http://dx.doi.org/10.2172/1968587, https://petsc.org/release/docs/manual/manual.pdf
[petsc-efficient]
Satish Balay, Victor Eijkhout, William D. Gropp, Lois Curfman McInnes and Barry F. Smith. Efficient
Management of Parallelism in Object Oriented Numerical Software Libraries. Modern Software Tools in
Scientific Computing. E. Arge, A. M. Bruaset and H. P. Langtangen, editors. 163--202. Birkhauser
Press. 1997.
Components
PETSc is designed with an object-oriented style. Almost all user-visible types are abstract interfaces
with implementations that may be chosen at runtime. Those objects are managed through handles to opaque
data structures which are created, accessed and destroyed by calling appropriate library routines.
PETSc consists of a variety of components. Each component manipulates a particular family of objects and
the operations one would like to perform on these objects. These components provide the functionality
required for many parallel solutions of PDEs.
Vec 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.
Mat 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.
PC 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).
KSP 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.
SNES 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.
TS Code for the time evolution of solutions of PDEs. In addition, provides pseudo-transient
continuation techniques for computing steady-state solutions.
InstallationUsingpip
You can use pip to install petsc4py and its dependencies (mpi4py is optional but highly recommended):
$ python -m pip install [--user] numpy mpi4py (or pip install [--user] numpy mpi4py)
$ python -m pip install [--user] petsc petsc4py (or pip install [--user] petsc petsc4py)
Usingsetuptools
You can also install dependencies manually and then invoke setuptools from the petsc4py source directory:
$ python setup.py build $ python setup.py install
You may use the --install-lib argument to the install command to alter the site-packages directory where
the package is to be installed.
If you are cross-compiling, and the numpy module cannot be loaded on your build host, then before
invoking setup.py, set NUMPY_INCLUDE environment variable to the path that would be returned by importnumpy;numpy.get_include():
$ export NUMPY_INCLUDE=/usr/lib/pythonX/site-packages/numpy/core/include
FromPETScsource
If you already have downloaded PETSc source and have installed the dependencies of petsc4py, then to
build the petsc4py module along with PETSc, add the --with-petsc4py=1 argument to the configure command
when building PETSc:
$ ./configure --with-petsc4py=1 $ make $ make install
This will install PETSc and the petsc4py module into the PETSc directory under the prefix specified to
the PETSc configure command.
If you wish to make the module importable without having to set the PYTHONPATH environment variable, you
may add a shortcut to the system-wide site-packages directory creating a special .pth file with exactly
one line of Python code. This can be done by the following command, where the system-wide path is assumed
to be /usr/lib/pythonX/site-packages (replace X with your python version):
$echo "import sys, os;" "p = os.getenv('PETSC_DIR');" "a = os.getenv('PETSC_ARCH') or '';" "p = p and
os.path.join(p, a, 'lib');" "p and (p in sys.path or sys.path.append(p))" >
/usr/lib/pythonX/site-packages/petsc4py.pth
If you are cross-compiling, and numpy cannot be loaded on your build host, then pass --have-numpy=1--with-numpy-include=PATH, where PATH is the path that would be returned by importnumpy;print(numpy.get_include()). This will suppress autodetection of the include path on the build host.
Tutorial
XXX To be written ... Any contribution welcome!
Citations
If PETSc for Python been significant to a project that leads to an academic publication, please
acknowledge that fact by citing the project.
• L. Dalcin, P. Kler, R. Paz, and A. Cosimo, ParallelDistributedComputingusingPython, Advances in
Water Resources, 34(9):1124-1139, 2011. http://dx.doi.org/10.1016/j.advwatres.2011.04.013
• S. Balay, S. Abhyankar, M. Adams, S. Benson, J. Brown, P. Brune, K. Buschelman, E. Constantinescu, L.
Dalcin, A. Dener, V. Eijkhout, J. Faibussowitsch, W. Gropp, V. Hapla, T. Isaac, P. Jolivet, D.
Karpeyev, D. Kaushik, M. Knepley, F. Kong, S. Kruger, D. May, L. Curfman McInnes, R. Mills, L.
Mitchell, T. Munson, J. Roman, K. Rupp, P. Sanan, J Sarich, B. Smith, S. Zampini, H. Zhang, and H.
Zhang, J. Zhang, PETSc/TAOUsersManual, ANL-21/39 - Revision 3.19, 2023.
http://dx.doi.org/10.2172/1968587, https://petsc.org/release/docs/manual/manual.pdf
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