From massively parallel algorithms and fluctuating time horizons to nonequilibrium surface growth
We study the asymptotic scaling properties of a massively parallel algorithm for discrete-event simulations where the discrete events are Poisson arrivals. The evolution of the simulated time horizon is analogous to a nonequilibrium surface. Monte Carlo simulations and a coarse-grained approximation indicate that the macroscopic landscape in the steady state is governed by the Edwards-Wilkinson Hamiltonian. Since the efficiency of the algorithm corresponds to the density of local minima in the associated surface, our results imply that the algorithm is asymptotically scalable.
Korniss
G
Supercomputer Computations Research Institute, Florida State University, Tallahassee, Florida 32306-4130, USA.
Toroczkai
Z
Novotny
MA
Rikvold
PA