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

G. Korniss·M. A. Novotny·P. A. Rikvold·H. Guclu·Z. Toroczkai
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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.

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G. Korniss, M. A. Novotny, P. A. Rikvold, H. Guclu, Z. Toroczkai. 2001-12-06. Going through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability. https://arxiv.org/abs/cond-mat/0112103

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