MPI parallel map and cluster scheduling

The pyina package provides several basic tools to make MPI-based high-performance computing more accessable to the end user. The goal of pyina is to allow the user to extend their own code to MPI-based high-performance 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. Further, in pyina.mpi 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 mpirun or 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 map standard
• 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: https://pypi.org/project/pyina

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.

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

Probably the best way to get started is to look at a few of the examples provided within pyina. See pyina.examples 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 internals of pyina. The source code is also generally well documented, so further questions may be resolved by inspecting the code itself. Please also 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).