Parallel Distributed Computing with Python

DALCÍN LISANDRO; KLER, PABLO; STORTI M.; PAZ RODRIGO

Cluster Computing: Tools, Techniques and Applications

Nova Publishers

Lugar: Hauppauge, New York, USA; Año: 2009;

This work reports our attempts to facilitate the access to high- performance parallel computing resources within a Python program- ming environment. The outcome of this effort are two open source and public domain packages, MPI for Python (known in short as mpi4py) and PETSc for Python (known in short as petsc4py).MPI for Python [1, 2, 3], is an open-source software project that provides bindings of the Message Passing Interface (MPI) standard for the Python programming language and targets the development of parallel application codes in Python. Its facilities allow parallel Python programs to easily exploit multiple processors. MPI for Python employs any back-end MPI implementation, thus being immediately available on any parallel environment providing access to any MPI library.PETSc for Python [4] is an open-source software project that pro- vides access to the Portable, Extensible Toolkit for Scientific Compu- tation (PETSc) libraries within the Python programming language. Its facilities allow sequential and parallel Python applications to ex- ploit state of the art algorithms and data structures readily available in PETSc.