Parallel distributed computing using Python

► We present two packages for parallel distributed computing with Python. ► MPI for Python (mpi4py) provides bindings for the MPI standard. ► PETSc for Python (petsc4py) provides bindings for PETSc libraries. ► MPI and PETSc for Python target large-scale scientific application development. ► Perform...

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Published in:	Advances in water resources Vol. 34; no. 9; pp. 1124 - 1139
Main Authors:	Dalcin, Lisandro D., Paz, Rodrigo R., Kler, Pablo A., Cosimo, Alejandro
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2011
Subjects:	Algorithms aquifers Computation Computer programs computer software Data structures Distributed processing engineering infrastructure Mathematical models Message passing MPI PETSc Python Software streams PETSc MPI Python
ISSN:	0309-1708, 1872-9657
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Abstract	► We present two packages for parallel distributed computing with Python. ► MPI for Python (mpi4py) provides bindings for the MPI standard. ► PETSc for Python (petsc4py) provides bindings for PETSc libraries. ► MPI and PETSc for Python target large-scale scientific application development. ► Performance tests confirm that the Python layer introduces acceptable overhead. This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python. MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering. MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions.
AbstractList	This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python.MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering.MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions. ► We present two packages for parallel distributed computing with Python. ► MPI for Python (mpi4py) provides bindings for the MPI standard. ► PETSc for Python (petsc4py) provides bindings for PETSc libraries. ► MPI and PETSc for Python target large-scale scientific application development. ► Performance tests confirm that the Python layer introduces acceptable overhead. This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python. MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering. MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions.
Author	Dalcin, Lisandro D. Kler, Pablo A. Cosimo, Alejandro Paz, Rodrigo R.
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Snippet	► We present two packages for parallel distributed computing with Python. ► MPI for Python (mpi4py) provides bindings for the MPI standard. ► PETSc for Python... This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming...
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SubjectTerms	Algorithms aquifers Computation Computer programs computer software Data structures Distributed processing engineering infrastructure Mathematical models Message passing MPI PETSc Python Software streams
Title	Parallel distributed computing using Python
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