Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation models efficiently. Indeed, many new paradigms to effectively use the available underlying hardware have been proposed in the literature. Among these, the Share-Everything paradigm tackles massively-parallel shared-memory machines, in order to support speculative simulation by taking into account the limits and benefits related to this family of architectures. Previous results have shown how this paradigm outperforms traditional speculative strategies (such as data-separated Time Warp systems) whenever the granularity of executed events is small. In this paper, we show performance implications of this simulation-engine organization when the simulation models have a variable granularity. To this end, we have selected a traffic model, tailored for smart cities-oriented simulation. Our assessment illustrates the effects of the various tuning parameters related to the approach, opening to a higher understanding of this innovative paradigm.

Ianni, M., Marotta, R., Cingolani, D., Pellegrini, A., Quaglia, F. (2018). Optimizing simulation on Shared-Memory Platforms: the Smart Cities Case. In Proceedings of the 2018 Winter Simulation Conference (pp.1969-1980). IEEE.

Optimizing simulation on Shared-Memory Platforms: the Smart Cities Case

Alessandro Pellegrini;Francesco Quaglia
2018-12-01

Abstract

Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation models efficiently. Indeed, many new paradigms to effectively use the available underlying hardware have been proposed in the literature. Among these, the Share-Everything paradigm tackles massively-parallel shared-memory machines, in order to support speculative simulation by taking into account the limits and benefits related to this family of architectures. Previous results have shown how this paradigm outperforms traditional speculative strategies (such as data-separated Time Warp systems) whenever the granularity of executed events is small. In this paper, we show performance implications of this simulation-engine organization when the simulation models have a variable granularity. To this end, we have selected a traffic model, tailored for smart cities-oriented simulation. Our assessment illustrates the effects of the various tuning parameters related to the approach, opening to a higher understanding of this innovative paradigm.
2018 Winter Simulation Conference
Rilevanza internazionale
dic-2018
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
English
Intervento a convegno
Ianni, M., Marotta, R., Cingolani, D., Pellegrini, A., Quaglia, F. (2018). Optimizing simulation on Shared-Memory Platforms: the Smart Cities Case. In Proceedings of the 2018 Winter Simulation Conference (pp.1969-1980). IEEE.
Ianni, M; Marotta, R; Cingolani, D; Pellegrini, A; Quaglia, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/216353
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