Ready to use analysis and buildings blocks to write parallel analysis algorithms using MDAnalysis with dask.

For example, run a rmsd analysis on all available cores:

import MDAnalysis as mda
from pmda import rms

u = mda.Universe(top, traj)
ref = mda.Universe(top, traj)

rmsd_ana = rms.RMSD(u.atoms, ref.atoms).run(n_jobs=-1)

print(rmsd_ana.rmsd)

By default PMDA use the multiprocessing scheduler of dask. This is sufficient if you want to run your simulation on a single machine. If your analysis takes a very long time (>30 min) you can also spread it to several nodes using the distributed scheduler. To do this you can pass a scheduler keyword argument to the run method.

To write your own parallel algorithms you can subclass the pmda.parallel.ParallelAnalysisBase class.

PMDA is released under the GNU General Public License, version 2 (see the files AUTHORS and LICENSE for details).

Source code is available in the public GitHub repository https://github.com/MDAnalysis/pmda/.

The latest release is available from https://pypi.org/project/pmda/ and can be installed with pip

pip install --upgrade pmda

To install the latest development version from source, run

git clone [email protected]:MDAnalysis/pmda.git
cd pmda
python setup.py install

Help is also available through the MDAnalysis mailing list

https://groups.google.com/group/mdnalysis-discussion

Please report bugs and feature requests for PMDA through the Issue Tracker.

PMDA welcomes new contributions. Please drop by the MDAnalysis developer mailing list to discuss and ask questions.

To contribute code, submit a pull request against the master branch in the PMDA repository.

If you use PMDA in published work please cite [Fan2019].