a friendly fork of pp
ppft is a fork of Parallel Python, and is developed as part of pathos: https://github.com/uqfoundation/pathos
Parallel Python module (PP) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. PP module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Visit http://www.parallelpython.com for further information.
Pathos is a python framework for heterogeneous computing. Pathos is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of known issues is maintained at http://trac.mystic.cacr.caltech.edu/project/pathos/query, with a public ticket list at https://github.com/uqfoundation/pathos/issues.
NOTE: ppft installs as pp. If pp is installed, it should be uninstalled before ppft is installed -- otherwise, "import pp" will likely not find the ppft fork.
- pip and setuptools support
- support for python 3
- enhanced serialization, using dill.source
This version is a fork of pp-1.6.4.
The latest stable release of ppft is available from:: https://github.com/uqfoundation/ppft/releases
or:: https://pypi.python.org/pypi/ppft
PP and ppft are distributed under a BSD-like license.
You can get the latest development version with all the shiny new features at:: https://github.com/uqfoundation
If you have a new contribution, please submit a pull request.
Probably the best way to get started is to look at the examples that are provided within PP. See pp.examples for a set of scripts. Please feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns).
Pathos is an active research tool. There are a growing number of publications and presentations that discuss real-world examples and new features of pathos in greater detail than presented in the user's guide. If you would like to share how you use pathos in your work, please post a link or send an email (to mmckerns at caltech dot edu).
If you use pathos to do research that leads to publication, we ask that you acknowledge use of pathos by citing the following in your publication::
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;
http://arxiv.org/pdf/1202.1056
Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;
http://trac.mystic.cacr.caltech.edu/project/pathos
Please see http://trac.mystic.cacr.caltech.edu/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.