  In the present paper we identify a rigorous property of a number of
tempering-based Monte Carlo sampling methods, including parallel tempering as
well as partial and infinite swapping. Based on this property we develop a
variety of performance measures for such rare-event sampling methods that are
broadly applicable, informative, and straightforward to implement. We
illustrate the use of these performance measures with a series of applications
involving the equilibrium properties of simple Lennard-Jones clusters,
applications for which the performance levels of partial and infinite swapping
approaches are found to be higher than those of conventional parallel
tempering.

Statistical Mechanics;Materials Science