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Parallel Kinetic Monte Carlo (KMC) is a potent tool to simulate stochasticparticle systems efficiently. However, despite literature on quantifying domaindecomposition errors of the particle system for this class of algorithms in theshort and in the long time regime, no study yet explores and quantifies theloss of time-reversibility in Parallel KMC. Inspired by concepts fromnon-equilibrium statistical mechanics, we propose the entropy production perunit time, or entropy production rate, given in terms of an observable and acorresponding estimator, as a metric that quantifies the loss of reversibility.Typically, this is a quantity that cannot be computed explicitly for ParallelKMC, which is why we develop a posteriori estimators that have good scalingproperties with respect to the size of the system. Through these estimators, wecan connect the different parameters of the scheme, such as the communicationtime step of the parallelization, the choice of the domain decomposition, andthe computational schedule, with its performance in controlling the loss ofreversibility. From this point of view, the entropy production rate can be seenboth as an information criterion to compare the reversibility of differentparallel schemes and as a tool to diagnose reversibility issues with aparticular scheme. As a demonstration, we use Sandia Lab's SPPARKS software tocompare different parallelization schemes and different domain (lattice)decompositions.

Information Criteria for quantifying loss of reversibility in parallelized KMC