Ubuntu Manpage: petsc4py - PETSc for Python

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NAME

       petsc4py - PETSc for Python

       Author Lisandro Dalcin

       Contact
              dalcinl@gmail.com

       Web Site
              https://bitbucket.org/petsc/petsc4py

       Date   May 01, 2016

   Abstract
       This document describes petsc4py, a Python port to the PETSc libraries.

       PETSc  (the  Portable,  Extensible  Toolkit for Scientific Computation) 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.

       This package provides an important subset of  PETSc  functionalities  and  uses  NumPy  to
       efficiently manage input and output of array data.

       A good friend of petsc4py is:

          • mpi4py: Python bindings for MPI, the Message Passing Interface.

       Other projects depends on petsc4py:

          • slepc4py:  Python  bindings  for  SLEPc,  the Scalable Library for Eigenvalue Problem
            Computations.

Overview
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.

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.F. Adams, J. Brown, P. Brune, K. Buschelman, L. Dalcin, V.
Eijkhout, W.D. Gropp, D. Karpeyev, D. Kaushik, M.G. Knepley, L. Curfman McInnes, K.
Rupp, B.F. Smith, S. Zampini, H. Zhang, and H. Zhang, PETSc Users Manual, ANL-95/11 -
Revision 3.7, 2016. http://www.mcs.anl.gov/petsc/petsc-current/docs/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.

Installation
Using pip or easy_install
You can use pip to install petsc4py and its dependencies (mpi4py is optional but highly
recommended):

$ pip install [--user] numpy mpi4py
$ pip install [--user] petsc petsc4py

Alternatively, you can use easy_install (deprecated):

$ easy_install petsc4py

If you already have a working PETSc installation, set environment variables PETSC_DIR and
PETSC_ARCH to appropriate values and next use pip:

$ export PETSC_DIR=/path/to/petsc
$ export PETSC_ARCH=arch-linux2-c-opt
$ pip install petsc4py

Using distutils
Requirements
You need to have the following software properly installed in order to build PETSc for
Python:

• Any MPI implementation [1] (e.g., MPICH or Open MPI), built with shared libraries.

• A matching version of PETSc built with shared libraries.

• NumPy package.

[1] Unless you have appropiatelly configured and built PETSc without MPI (configure
option --with-mpi=0).

[2] You may need to use a parallelized version of the Python interpreter with some MPI-1
implementations (e.g. MPICH1).

Downloading
The PETSc for Python package is available for download at the project website generously
hosted by Bitbucket. You can use curl or wget to get a release tarball.

• Using curl:

$ curl -O https://bitbucket.org/petsc/petsc4py/petsc4py-X.Y.tar.gz

• Using wget:

$ wget https://bitbucket.org/petsc/petsc4py/petsc4py-X.Y.tar.gz

Building
After unpacking the release tarball:

$ tar -zxf petsc4py-X.Y.tar.gz
$ cd petsc4py-X.Y

the distribution is ready for building.

NOTE:
Mac OS X users employing a Python distribution built with universal binaries may need
to set the environment variables MACOSX_DEPLOYMENT_TARGET, SDKROOT, and ARCHFLAGS to
appropriate values. As an example, assume your Mac is running Snow Leopard on a 64-bit
Intel processor and you want to override the hard-wired cross-development SDK in Python
configuration, your environment should be modified like this:

$ export MACOSX_DEPLOYMENT_TARGET=10.6
$ export SDKROOT=/
$ export ARCHFLAGS='-arch x86_64'

Some environment configuration is needed to inform the location of PETSc. You can set
(using setenv, export or what applies to you shell or system) the environment variables
PETSC_DIR, and PETSC_ARCH indicating where you have built/installed PETSc:

$ export PETSC_DIR=/usr/local/petsc
$ export PETSC_ARCH=arch-linux2-c-opt

Alternatively, you can edit the file setup.cfg and provide the required information below
the [config] section:

[config]
petsc_dir = /usr/local/petsc
petsc_arch = arch-linux2-c-opt
...

Finally, you can build the distribution by typing:

$ python setup.py build

Installing
After building, the distribution is ready for installation.

If you have root privileges (either by log-in as the root user of by using sudo) and you
want to install PETSc for Python in your system for all users, just do:

$ python setup.py install

The previous steps will install the petsc4py package at standard location
prefix/lib/pythonX.X/site-packages.

If you do not have root privileges or you want to install PETSc for Python for your
private use, just do:

$ python setup.py install --user

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, Parallel Distributed Computing using Python,
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.F. Adams, J. Brown, P. Brune, K. Buschelman, L. Dalcin, V.
Eijkhout, W.D. Gropp, D. Karpeyev, D. Kaushik, M.G. Knepley, L. Curfman McInnes, K.
Rupp, B.F. Smith, S. Zampini, H. Zhang, and H. Zhang, PETSc Users Manual, ANL-95/11 -
Revision 3.7, 2016. http://www.mcs.anl.gov/petsc/petsc-current/docs/manual.pdf

AUTHOR

       Lisandro Dalcin

COPYRIGHT

       2016, Lisandro Dalcin

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