"Swarm relaxation": Equilibrating a large ensemble of computer simulations
Abstract
It is common practice in molecular dynamics and Monte Carlo computer simulations to run multiple, separatelyinitialized simulations in order to improve the sampling of independent microstates. Here we examine the utility of an extreme case of this strategy, in which we run a large ensemble of $M$ independent simulations (a "swarm"), each of which is relaxed to equilibrium. We show that if $M$ is of order $10^3$, we can monitor the swarm's relaxation to equilibrium, and confirm its attainment, within $\sim 10\bar\tau$, where $\bar\tau$ is the equilibrium relaxation time. As soon as a swarm of this size attains equilibrium, the ensemble of $M$ final microstates from each run is sufficient for the evaluation of most equilibrium properties without further sampling. This approach dramatically reduces the wallclock time required, compared to a single long simulation, by a factor of several hundred, at the cost of an increase in the total computational effort by a small factor. It is also wellsuited to modern computing systems having thousands of processors, and is a viable strategy for simulation studies that need to produce highprecision results in a minimum of wallclock time. We present results obtained by applying this approach to several test cases.
 Publication:

arXiv eprints
 Pub Date:
 October 2017
 DOI:
 10.48550/arXiv.1710.10622
 arXiv:
 arXiv:1710.10622
 Bibcode:
 2017arXiv171010622M
 Keywords:

 Condensed Matter  Statistical Mechanics
 EPrint:
 12 pages. To appear in Eur. Phy. J. E, 2017