Efficiency of linked cell algorithms

TL;DR

This paper introduces a general evaluation method for linked cell algorithms in molecular simulations and proposes a combined approach that improves efficiency across various setups.

Contribution

It develops a parameter-independent efficiency evaluation method and suggests a new combined algorithm for better performance.

Findings

The evaluation method is broadly applicable to different simulation parameters.

The combined linked cell reordering and interaction sorting algorithm improves efficiency.

The proposed method reduces CPU time spent on particle interaction identification.

Abstract

The linked cell list algorithm is an essential part of molecular simulation software, both molecular dynamics and Monte Carlo. Though it scales linearly with the number of particles, there has been a constant interest in increasing its efficiency, because a large part of CPU time is spent to identify the interacting particles. Several recent publications proposed improvements to the algorithm and investigated their efficiency by applying them to particular setups. In this publication we develop a general method to evaluate the efficiency of these algorithms, which is mostly independent of the parameters of the simulation, and test it for a number of linked cell list algorithms. We also propose a combination of linked cell reordering and interaction sorting that shows a good efficiency for a broad range of simulation setups.