From Massively Parallel Algorithms and Fluctuating Time Horizons to Nonequilibrium Surface Growth

1999/09/30 by G. Korniss, Zoltán Toroczkai, Z. Toroczkai +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Stochastic processes and statistical mechanics #Theoretical and Computational Physics #cond-mat.stat-mech #cs.DC #physics.comp-ph

paper · pdf · doi:10.1103/physrevlett.84.1351

published as Phys. Rev. Lett. 84, 1351 (2000). · RevTex, 4 pages, 3 figures

arxiv created 2000/02/01 · openalex publication_date 2000/02/07 · arxiv updated 2009/11/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

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.

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