1.3. Writing new parallel analysis

At the core of PMDA is the idea that a common interface makes it easy to create code that can be easily parallelized, especially if the analysis can be split into independent work over multiple trajectory segments (or “slices”) and a final step in which all data from the trajectory slices is combined.

1.3.1. Wrapping existing analysis functions

If you already have a function that

  1. takes one or more AtomGroup instances as input

  2. analyzes one frame in a trajectory and returns the result for this frame

then you can use the helper functions in pmda.custom to rapidly create a parallel analysis class that follows the common PMDA API.

1.3.1.1. Example: Parallelizing radius of gyration

For example, we want to calculate the radius of gyration of a protein. We first create the protein AtomGroup:

import MDAnalysis as mda
u = mda.Universe(topology, trajectory)
protein = u.select_atoms("protein")

The the following function calculates the radius of gyration of a protein given in AtomGroup ag :

def rgyr(ag):
    return ag.radius_of_gyration()

We can wrap rgyr() in pmda.custom.AnalysisFromFunction

import pmda.custom
parallel_rgyr = pmda.custom.AnalysisFromFunction(rgyr, u, protein)

Run the analysis on 8 cores and show the timeseries that was collected in the results attribute:

parallel_rgyr.run(n_jobs=8)
print(parallel_rgyr.results)

1.3.2. Building PMDA analysis classes

With the help of pmda.parallel.ParallelAnalysisBase one can write new analysis functions that automatically parallelize. This approach provides more freedom than Wrapping existing analysis functions described above.

  1. Define the single frame analysis function, i.e., how to compute the observable for a single time step from a given AtomGroup.

  2. Derive a class from ParallelAnalysisBase that uses the single frame function.

As an example, we show how one can parallelize the RMSF function (from MDAnalysis.analysis.rms.RMSF):

  • TODO

  • more TODO

  • other example?