  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 non-equilibrium
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.
