"Swarm relaxation": Equilibrating a large ensemble of computer simulations⋆.

It is common practice in molecular dynamics and Monte Carlo computer simulations to run multiple, separately-initialized 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 [Formula: see text], we can monitor the swarm's relaxation to equilibrium, and confirm its attainment, within [Formula: see text], where [Formula: see text] 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 wall-clock 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 well suited to modern computing systems having thousands of processors, and is a viable strategy for simulation studies that need to produce high-precision results in a minimum of wall-clock time. We present results obtained by applying this approach to several test cases.