MPI parallel map and cluster scheduling
pyina package provides several basic tools to make MPI-based
parallel computing more accessable to the end user. The goal of
is to allow the user to extend their own code to MPI-based parallel
computing with minimal refactoring.
The central element of
pyina is the parallel map algorithm.
pyina currently provides two strategies for executing the parallel-map,
where a strategy is the algorithm for distributing the work list of
jobs across the availble nodes. These strategies can be used "in-the-raw"
(i.e. directly) to provide the map algorithm to a user's own mpi-aware code.
pyina provides pipe and map implementations
(known as "easy map") that hide the MPI internals from the user. With the
"easy map", the user can launch their code in parallel batch mode -- using
standard python and without ever having to write a line of MPI code.
There are several ways that a user would typically launch their code in
parallel -- directly with
mpiexec, or through the use of a
scheduler such as torque or slurm.
pyina encapsulates several of these
"launchers", and provides a common interface to the different methods of
launching a MPI job.
pyina is part of
pathos, a python framework for heterogeneous computing.
pyina 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.html, with a public
ticket list at https://github.com/uqfoundation/pyina/issues.
pyina provides a highly configurable parallel map interface
to running MPI jobs, with:
- a map interface that extends the python
- the ability to submit batch jobs to a selection of schedulers
- the ability to customize node and process launch configurations
- the ability to launch parallel MPI jobs with standard python
- ease in selecting different strategies for processing a work list
The latest released version of
pyina is available at:
pyina is distributed under a 3-clause BSD 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 documentation at
http://pyina.rtfd.io. Also see
for a set of scripts that demonstrate the configuration and launching of
mpi-based parallel jobs using the "easy map" interface. Also see
pyina.examples_other for a set of scripts that test the more raw
pyina. You can run the tests with
python -m pyina.tests.
A script is included for querying, setting up, and tearing down an MPI
python -m pyina for more information. The source code
is generally well documented, so further questions may be resolved by
inspecting the code itself. 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
pyina in your work, please send
an email (to mmckerns at uqfoundation dot org).
If you use
pyina to do research that leads to publication, we ask that you
acknowledge use of
pyina 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.