Statistical properties of the simulated time horizon in conservative parallel discrete-event simulations

We investigate the universal characteristics of the simulated time horizon of the basic conservative parallel algorithm when implemented on regular lattices. This technique [1, 2] is generically applicable to various physical, biological, or chemical systems where the underlying dynamics is asynchronous. Employing direct simulations, and using standard tools and the concept of dynamic scaling from non-equilibrium surface/interface physics, we identify the universality class of the time horizon and determine its implications for the asymptotic scalability of the basic conservative scheme. Our main finding is that while the simulation converges to an asymptotic nonzero rate of progress, the statistical width of the time horizon diverges with the number of PEs in a power law fashion. This is in contrast with the findings of Ref. [3]. This information can be very useful, e.g., we utilize it to understand optimizing the size of a moving "time window" to enforce memory constraints.

[1]  K. Mani Chandy,et al.  Distributed Simulation: A Case Study in Design and Verification of Distributed Programs , 1979, IEEE Transactions on Software Engineering.

[2]  Boris D. Lubachevsky,et al.  Efficient parallel simulations of dynamic Ising spin systems , 1988 .

[3]  Jeffrey S. Steinman Discrete-event simulation and the event horizon part 2: event list management , 1996, Workshop on Parallel and Distributed Simulation.

[4]  Zoltán Toroczkai,et al.  From Massively Parallel Algorithms and Fluctuating Time Horizons to Non-equilibrium Surface Growth , 2000, Physical review letters.

[5]  S Raychaudhuri,et al.  Maximal height scaling of kinetically growing surfaces. , 2001, Physical review letters.

[6]  Extremal-point densities of interface fluctuations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[7]  Kurt Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[8]  Tamás Vicsek,et al.  Scaling of the active zone in the Eden process on percolation networks and the ballistic deposition model , 1985 .

[9]  David M. Nicol,et al.  Performance bounds on parallel self-initiating discrete-event simulations , 1990, TOMC.

[10]  Zhang,et al.  Dynamic scaling of growing interfaces. , 1986, Physical review letters.

[11]  Jeff S. Steinman Discrete-event simulation and the event horizon , 1994, PADS '94.

[12]  Peter M. A. Sloot,et al.  Spatio-temporal correlations and rollback distributions in optimistic simulations , 2001, Proceedings 15th Workshop on Parallel and Distributed Simulation.

[13]  K. Mani Chandy,et al.  Asynchronous distributed simulation via a sequence of parallel computations , 1981, CACM.

[14]  Leonard Kleinrock,et al.  Bounds and approximations for self-initiating distributed simulation without lookahead , 1991, TOMC.

[15]  M. A. Novotny,et al.  Parallelization of a Dynamic Monte Carlo Algorithm: a Partially Rejection-Free Conservative Approach , 1998, ArXiv.

[16]  P. Meakin,et al.  Universal finite-size effects in the rate of growth processes , 1990 .

[17]  Boris D. Lubachevsky,et al.  Efficient Parallel Simulations of Asynchronous Cellular Arrays , 2005, Complex Syst..

[18]  David M. Nicol,et al.  Parallel simulation today , 1994, Ann. Oper. Res..

[19]  G. Vojta,et al.  Fractal Concepts in Surface Growth , 1996 .

[20]  G Korniss,et al.  Dynamic phase transition, universality, and finite-size scaling in the two-dimensional kinetic Ising model in an oscillating field. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  鈴木 増雄 Time-Dependent Statistics of the Ising Model , 1965 .

[22]  Peter M. A. Sloot,et al.  Self-Organized Criticality in Optimistic Simulation of Correlated Systems , 2001, Scalable Comput. Pract. Exp..

[23]  David P. Landau,et al.  Computer Simulation Studies in Condensed-Matter Physics XIII , 2001 .

[24]  K. Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[25]  Yicheng Zhang,et al.  Kinetic roughening phenomena, stochastic growth, directed polymers and all that. Aspects of multidisciplinary statistical mechanics , 1995 .

[26]  Zoltán Toroczkai,et al.  Non-equilibrium Surface Growth and Scalability of Parallel Algorithms for Large Asynchronous Systems , 2000, ArXiv.

[27]  R. M. Fujimoto,et al.  Parallel discrete event simulation , 1989, WSC '89.

[28]  Jeff S. Steinman,et al.  Breathing Time Warp , 1993, PADS '93.

[29]  B. J. Overeinder,et al.  Distributed Event-driven Simulation - Scheduling Strategies and Resource Management , 2000 .

[30]  Alexander L. Stolyar,et al.  Asynchronous updates in large parallel systems , 1996, SIGMETRICS '96.

[31]  J. Krug Origins of scale invariance in growth processes , 1997 .