Going through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability
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.
Cite
@article{arxiv.cond-mat/0112103,
title = {Going through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability},
author = {G. Korniss and M. A. Novotny and P. A. Rikvold and H. Guclu and Z. Toroczkai},
journal= {arXiv preprint arXiv:cond-mat/0112103},
year = {2007}
}
Comments
to appear in the Proceedings of the MRS, Fall 2001