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

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dc.creatorKorniss, G.
dc.creatorNovotny, M. A.
dc.creatorRikvold, P. A.
dc.creatorGuclu, H.
dc.creatorToroczkai, Z.
dc.date2001-12-06
dc.date.accessioned2026-07-07T02:43:45Z
dc.date.available2026-07-07T02:43:45Z
dc.descriptionEfficient 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.
dc.descriptionto appear in the Proceedings of the MRS, Fall 2001
dc.identifierhttps://arxiv.org/abs/cond-mat/0112103
dc.identifierhttp://arxiv.org/abs/cond-mat/0112103
dc.identifierMaterials Research Society Symposium Proceedings Series Vol. 700, pp. 297-308, 2002
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/18623
dc.subjectStatistical Mechanics
dc.subjectMaterials Science
dc.subjectDistributed, Parallel, and Cluster Computing
dc.subjectPerformance
dc.subjectComputational Physics
dc.titleGoing through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability
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