"Accelerating molecular dynamics simulations using fast Ewald summation with prolates" paper

An interesting paper in Nature Communications proposing a new way to do Ewald summation that appears better/faster/scales better than PME. They said they had implemented it in GROMACS - I wonder if developers have been in contact with them, and what the developers thought?

https://www.nature.com/articles/s41467-026-73232-8

“Accelerating molecular dynamics simulations using fast Ewald summation with prolates: The evaluation of long-range Coulomb interactions is a significant cost in molecular dynamics (MD), even when using Particle Mesh Ewald (PME) or Particle-Particle-Particle-Mesh (PPPM) methods, which rely on Ewald splitting and the fast Fourier transform to achieve near-linear scaling. We introduce ESP—Ewald summation with prolate spheroidal wave functions (PSWFs)—which leads to a more efficient Fourier representation and a reduction in the required grid size, global communication, and particle-grid operations, without loss of accuracy. We have integrated the ESP method into two widely-used open-source MD packages, LAMMPS and GROMACS, enabling rapid comparison and adoption. Relative to PME/PPPM baselines at error tolerances 10−3 to 10−4, ESP gives roughly a 3-fold acceleration of electrostatic interactions, and a 2.5-fold speed-up in the MD simulation when using about 103 compute cores. At high accuracy (10−5), these increase to 10-fold for the far-field electrostatics and 5-fold for MD simulation. Furthermore, we show that the accelerated codes have improved strong scaling with core count, and validate them in realistic long-time biological and material simulations. ESP thus offers a practical, drop-in path to reduce the time-to-solution and energy footprint of MD workflows.”

@hess is acknowledged, so the answer is yes.

The code is here: GitHub - lu1and10/Ewald-Splitting-with-Prolates · GitHub

2.5 to 5 times faster simulations? Sounds unbelievable…

Also, LJPME is not mentioned.

The method described in a related MLIP paper from the same group [2606.06617] Prolate spheroidal wave functions enable fast and exponent-aware long-range machine learning interatomic potentials can also be used for LJPME…

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Thanks for bringing this up. We have cleaned up the code a bit since the earlier version.

The current branch is here:

I think this is in better shape than the GitHub version, though I am still reluctant to open such a large MR.

Most of the diff is in nbnxm: roughly 93% of the total change, or 80,135 out of about 86k changed lines. This part is almost entirely additive, at +80,086/−49. The rest contains the main algorithmic implementation: about 2.9k lines in ewald for split/spread/solve, about 1.8k lines in math for PSWF support, and smaller changes in gmxpreprocess, tables, and fileio.

The nbnxm implementation uses compile-time specialization with runtime dispatch; in particular, the polynomial order is fixed as a compile-time constant.

There is still substantial work left, including parameter selection, rigorous accuracy/speed tests, and benchmarks.

Happy to discuss further if anyone is interested.

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