Developing Parallel, Discrete Event Simulations in Python - First Results and User Experiences with the SimX Library

Thulasidasan, Sunil; Kroc, Lukas; Eidenbenz, Stephan

doi:10.5220/0005042701880194

Cited by 4 publications

(2 citation statements)

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“…The application simulator presented here (SpecTADSim) was implemented using the SimianPie parallel DES platform, [33][34][35][36] a successor to the SimX parallel DES framework and SimCore, [37][38][39] all developed at Los Alamos National Laboratory, NM. (The open-source Simian software is available on Github at http://github.com/pujyam/ simian.)…”

Section: The Spectad Application Simulatormentioning

confidence: 99%

“…SpecTADSim's underlying engine, SimianPie, is the Python version of Simian, and is a descendant of the C ++ SimX framework. [37][38][39] Simian is a general-purpose, process-oriented, optionally just-in-time (JIT)-compiled, conservative parallel discrete event simulator (PDES). It is written in the scripting languages Lua and Python as separate but functionally equivalent implementations.…”

Section: The Simian Frameworkmentioning

confidence: 99%

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Discrete event performance prediction of speculatively parallel temperature-accelerated dynamics

et al. 2016

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Due to its unrivaled ability to predict the dynamical evolution of interacting atoms, molecular dynamics (MD) is a widely used computational method in theoretical chemistry, physics, biology, and engineering. Despite its success, MD is only capable of modeling timescales within several orders of magnitude of thermal vibrations, leaving out many important phenomena that occur at slower rates. The temperature-accelerated dynamics (TAD) method overcomes this limitation by thermally accelerating the state-to-state evolution captured by MD. Due to the algorithmically complex nature of the serial TAD procedure, implementations have yet to improve performance by parallelizing the concurrent exploration of multiple states. Here we utilize a discrete-event-based application simulator to introduce and explore a new speculatively parallel TAD (SpecTAD) method. We investigate the SpecTAD algorithm, without a full-scale implementation, by constructing an application simulator proxy (SpecTADSim). Following this method, we discover that a non-trivial relationship exists between the optimal SpecTAD parameter set and the number of CPU cores available at run-time. Furthermore, we find that a majority of the available SpecTAD boost can be achieved within an existing TAD application using relatively simple algorithm modifications.

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Section: The Spectad Application Simulatormentioning

confidence: 99%

Section: The Simian Frameworkmentioning

confidence: 99%