distributed and parallel python
ppft is a friendly fork of Parallel Python (pp). ppft extends Parallel Python to provide packaging and distribution with pip and setuptools, support for python 3, and enhanced serialization using dill.source. ppft uses Parallel Python to provide mechanisms for the parallel execution of python code on SMP (systems with multiple processors or cores) and clusters (computers connected via network).
Software written in python finds applications in a broad range of the categories including business logic, data analysis, and scientific calculations. This together with wide availability of SMP computers (multi-processor or multi-core) and clusters (computers connected via network) on the market create the demand in parallel execution of python code.
The most common way to write parallel applications for SMP computers is to use threads. However, the python interpreter uses the GIL (Global Interpreter Lock) for internal bookkeeping, where the GIL only allows one python byte-code instruction to execute at a time, even on an SMP computer. Parallel Python overcomes this limitation, and provides a simple way to write parallel python applications. Internally, processes and IPC (Inter Process Communications) are used to organize parallel computations. Parallel Python is written so that the details and complexity of IPC are handled internally, and the calling application just submits jobs and retrieves the results. Software written with Parallel Python works in parallel on many computers connected via a local network or the Internet. Cross-platform portability and dynamic load-balancing allows Parallel Python to parallelize computations efficiently even on heterogeneous and multi-platform clusters. Visit http://www.parallelpython.com for further information on Parallel Python.
ppft is part of pathos, a python framework for heterogeneous computing. ppft is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/ppft/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
parallel execution of python code on SMP and clusters
easy-to-understand job-based parallelization
automatic detection of the number of effective processors
dynamic processor allocation (at runtime)
low overhead for jobs with the same function (through transparent caching)
dynamic load balancing (jobs are distributed at runtime)
fault-tolerance (if a node fails, tasks are rescheduled on the others)
auto-discovery of computational resources
dynamic allocation of computational resources
SHA based authentication for network connections
enhanced serialization, using dill.source
The latest released version of ppft is available from:
ppft is distributed under a 3-clause BSD license, and is a fork of pp-1.6.6.
You can get the latest development version with all the shiny new features at:
If you have a new contribution, please submit a pull request.
ppft can be installed with pip:
$ pip install ppft
To include enhanced serialization, using dill.source, install:
$ pip install ppft[dill]
If Parallel Python is already installed, it should be uninstalled before ppft is installed – otherwise, import pp may point to the original and not to the ppft fork.
python (or pypy), >=3.7
Probably the best way to get started is to look at the documentation at http://ppft.rtfd.io. Also, you can see a set of example scripts in ppft.tests. You can run the test suite with python -m ppft.tests. ppft will create and execute jobs on local workers (automatically created using python -u -m ppft). Additionally, remote servers can be created with ppserver (or python -m ppft.server), and then jobs can be distributed to remote workers. See --help for more details on how to configure a server. Please feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns). If you would like to share how you use ppft in your work, please send an email (to mmckerns at uqfoundation dot org).
If you use ppft to do research that leads to publication, we ask that you acknowledge use of ppft 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- ; https://uqfoundation.github.io/project/pathos
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