Parallel Processing and Multiprocessing in Python

A number of Python-related libraries exist for the programming of solutions either employing multiple CPUs or multicore CPUs in a [http://en.wikipedia.org/wiki/Symmetric_multiprocessing symmetric multiprocessing (SMP)] or shared memory environment, or potentially huge numbers of computers in a cluster or grid environment. This page seeks to provide references to the different libraries and solutions available.

Symmetric Multiprocessing

Some libraries, often to preserve some similarity with more familiar concurrency models (such as Python's threading API), employ parallel processing techniques which limit their relevance to SMP-based hardware, mostly due to the usage of process creation functions such as the UNIX fork system call. However, a technique called process migration may permit such libraries to be useful in certain kinds of computational clusters as well, notably single-system image cluster solutions ([http://openmosix.sourceforge.net/ OpenMosix] being one such example).

• [http://www.python.org/pypi/parallel parallel/pprocess] - fork-based process creation with asynchronous channel-based communications

• [http://www.parallelpython.com/ ppsmp] - process-based, job-oriented solution (source code not available, has restrictive licence)

• [http://www.python.org/pypi/processing processing] - fork-based process creation (using threads on other platforms), implementing an API like the standard library's threading API and providing familiar objects such as queues and semaphores through the use of a manager process

• [http://www.python.org/pypi/remoteD remoteD] - fork-based process creation with a dictionary-based communications paradigm

Advantages of such approaches include convenient process creation and the ability to share resources. Indeed, the fork system call permits efficient sharing of common read-only data structures on modern UNIX-like operating systems.

Cluster Computing

Unlike SMP architectures and especially in contrast to thread-based concurrency, cluster (and grid) architectures offer high scalability due to the relative absence of shared resources, although this can make the programming paradigms seem somewhat alien to uninitiated developers. In this domain, some overlap with other distributed computing technologies may be observed.

• [http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm#pycluster pycluster] - binding for the [http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/ Cluster] software (apparently oriented towards bioinformatics tasks)

• [http://www-users.cs.york.ac.uk/~aw/pylinda/ PyLinda] - distributed computing using tuple spaces

• [http://pyMPI.sourceforge.net/ pyMPI] - MPI-based solution

• [http://datamining.anu.edu.au/~ole/pypar/ pypar] - Numeric Python and MPI-based solution

• [http://pypvm.sourceforge.net/ pypvm] - PVM-based solution

• [http://www.cimec.org.ar/python/ "Resources for Parallel Computing in Python"]

  • [http://www.cimec.org.ar/python/mpi4py.html "MPI for Python"] - MPI-based solution

  • [http://www.cimec.org.ar/python/python.html "Parallelized Python Interpreter"] - interactive, parallelized version of the Python interpreter

• [http://dirac.cnrs-orleans.fr/ScientificPython/ ScientificPython] contains three subpackages for parallel computing:

  • Scientific.DistributedComputing.MasterSlave implements a master-slave model in which a master process requests computational tasks that are executed by an arbitrary number of slave processes. The strong points are ease of use and the possibility to work with a varying number of slave process. It is less suited for the construction of large, modular parallel applications. Ideal for parallel scripting. Uses [http://pyro.sourceforge.net/ "Pyro"].

  • Scientific.BSP is an object-oriented implementation of the [http://www.bsp-worldwide.org/ "Bulk Synchronous Parallel (BSP)"] model for parallel computing, whose main advantages over message passing are the impossibility of deadlocks and the possibility to evaluate the computational cost of an algorithm as a function of machine parameters. The Python implementation of BSP features parallel data objects, communication of arbitrary Python objects, and a framework for defining distributed data objects implementing parallelized methods.

  • Scientific.MPI is an interface to MPI that emphasizes the possibility to combine Python and C code, both using MPI. Contrary to pypar and pyMPI, it does not support the communication of arbitrary Python objects, being instead optimized for Numeric/NumPy arrays.

Grid Computing

• [http://grail.sdsc.edu/projects/peg/ PEG] - Python Extensions for the Grid

• [http://www.python.org/pypi/pyGlobus pyGlobus] - see the [http://dev.globus.org/wiki/Python_Core Python Core] project for related software

Related Projects

• [http://content.cs.luc.edu/projects/comp412/hydrafs Hydra File System] - a distributed file system

Editorial Notes

The above lists should be arranged in ascending alphabetical order - please respect this when adding new frameworks or tools.