Scaling properties of a parallel implementation of the multicanonical algorithm

The multicanonical method has been proven powerful for statistical investigations of lattice and offlattice systems throughout the last two decades. We discuss an intuitive but very efficient parallel implementation of this algorithm and analyze its scaling properties for discrete energy systems, namely the Ising model and the 8-state Potts model. The parallelization relies on independent equilibrium simulations in each iteration with identical weights, merging their statistics in order to obtain estimates for the successive weights. With good care, this allows faster investigations of large systems, because it distributes the time-consumingweight-iteration procedure and allows parallel production runs.We show that the parallel implementation scales verywell for the simple Isingmodel, while the performance of the 8-state Potts model, which exhibits a first-order phase transition, is limited due to emerging barriers and the resulting large integrated autocorrelation times. The quality of estimates in parallel production runs remains of the same order at the same statistical cost. © 2012 Elsevier B.V. All rights reserved.