the `InterRDF_s` class is significantly slower than running multiple `InterRDF` instances sequentially

[gitzhangch]

Expected behavior

Recently, I have been conducting electrolyte simulation-related work using Gromacs 2025.1. Since I frequently use Python, I did not use gmx rdf. For multiple RDF calculations, I use InterRDF_s to accelerate.

Actual behavior

When analyzing multiple RDF pairs on a GROMACS 2025.1 trajectory, the InterRDF_s class is significantly slower than running multiple InterRDF instances sequentially, despite its design for batch processing.
I conducted an RDF analysis on the same group of molecules. When verbose=True is set, InterRDF_s shows a speed of 3.32 it/s while InterRDF shows a speed of 141.48 it/s. With the help of AI, I used cProfile for function-level analysis. The result showed that numpy.histogram was called too frequently, which affected the speed.

Code to reproduce the behavior

import MDAnalysis as mda
from MDAnalysis.analysis import rdf

u = mda.Universe("s4_pr.tpr", "s4_pr.trr")

li_sel = u.select_atoms("resname LI")
an_sel = u.select_atoms("resname AN")

rdf_calc = rdf.InterRDF(li_sel, an_sel,verbose=True)
rdf_calc.run(stop=50)

rdf_calcs = rdf.InterRDF_s(u, [[li_sel,an_sel]],verbose=True)
rdf_calcs.run(stop=50)

Current version of MDAnalysis

• Which version are you using? (run python -c "import MDAnalysis as mda; print(mda.__version__)")
  2.9.0
• Which version of Python (python -V)?
  python 3.11.11(numpy==2.2.0)
• Which operating system?
  ubuntu22

[gitzhangch]

I made some temporary modifications to the code and the processing speed became reasonable. The for loop in mdanalysis/package/MDAnalysis/analysis/rdf.py might not be the most reasonable design.

    def _single_frame(self):
        for i, (ag1, ag2) in enumerate(self.ags):
            pairs, dist = distances.capped_distance(
                ag1.positions,
                ag2.positions,
                self._maxrange,
                box=self._ts.dimensions,
            )

            # RAW
            # for j, (idx1, idx2) in enumerate(pairs):
            #     count, _ = np.histogram(dist[j], **self.rdf_settings)
            #     self.results.count[i][idx1, idx2, :] += count

            # modifications
            count, _ = np.histogram(dist, **self.rdf_settings)
            self.results.count[i][0, 0, :] += count


        if self.norm == "rdf":
            self.volume_cum += self._ts.volume

[orbeckst]

@gitzhangch thanks for looking into the code. Suggestions for performance improvements are always very welcome!

Could you create a PR with your changes? Then we can see if the tests pass and can discuss your proposed code in more detail.