# A Scalable, Linear-Time Dynamic Cutoff Algorithm for Molecular Dynamics

## TL;DR

This paper introduces a linear-time, scalable dynamic cutoff algorithm for molecular dynamics that improves performance on large systems and supercomputers by efficiently detecting interfaces without global communication.

## Contribution

The paper presents a novel dynamic cutoff method with linear-time algorithms for interface detection, enabling efficient large-scale molecular dynamics simulations on massively parallel systems.

## Key findings

- Comparable accuracy to PPPM algorithm
- Significant performance improvements for large systems
- Nearly perfect scaling on supercomputers

## Abstract

Recent results on supercomputers show that beyond 65K cores, the efficiency of molecular dynamics simulations of interfacial systems decreases significantly. In this paper, we introduce a dynamic cutoff method (DCM) for interfacial systems of arbitrarily large size. The idea consists in adopting a cutoff-based method in which the cutoff is cho- sen on a particle-by-particle basis, according to the distance from the interface. Computationally, the challenge is shifted from the long-range solvers to the detection of the interfaces and to the computation of the particle-interface distances. For these tasks, we present linear-time algorithms that do not rely on global communication patterns. As a result, the DCM algorithm is suited for large systems of particles and mas- sively parallel computers. To demonstrate its potential, we integrated DCM into the LAMMPS open-source molecular dynamics package, and simulated large liquid/vapor systems on two supercomputers: SuperMuc and JUQUEEN. In all cases, the accuracy of DCM is comparable to the traditional particle-particle particle-mesh (PPPM) algorithm, while the performance is considerably superior for large numbers of particles. For JUQUEEN, we provide timings for simulations running on the full system (458, 752 cores), and show nearly perfect strong and weak scaling.