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Going Through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability

Published online by Cambridge University Press:  17 March 2011

G. Korniss
Affiliation:
Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180-3590, USA
M.A. Novotny
Affiliation:
Department of Physics and Astronomy and Engineering Research Center, Mississippi State University, Mississippi State, MS 39762-5167, USA
P.A. Rikvold
Affiliation:
School of Computational Science and Information Technology, Department of Physics, and Center for Materials Research and Technology, Florida State University, Tallahassee, FL 32306, USA
H. Guclu
Affiliation:
Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180-3590, USA
Z. Toroczkai
Affiliation:
Theoretical Division and Center for Nonlinear Studies, MS-B258 Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Abstract

Efficient and faithful parallel simulation of large asynchronous systems is a challenging computational problem. It requires using the concept of local simulated times and a synchronization scheme. We study the scalability of massively parallel algorithms for discrete-event simulations which employ conservative synchronization to enforce causality. We do this by looking at the simulated time horizon as a complex evolving system, and we identify its universal characteristics. We find that the time horizon for the conservative parallel discrete-event simulation scheme exhibits Kardar-Parisi-Zhang-like kinetic roughening. This implies that the algorithm is asymptotically scalable in the sense that the average progress rate of the simulation approaches a non-zero constant. It also implies, however, that there are diverging memory requirements associated with such schemes.

Type
Research Article
Copyright
Copyright © Materials Research Society 2002

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