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Accelerating Petri-Net simulations using NVIDIA graphics processing units

Yianni, Panayioti C.; Neves, Luis C.; Rama, Dovile; Andrews, John D.

Authors

Panayioti C. Yianni

Luis C. Neves

Dovile Rama

John D. Andrews



Abstract

Stochastic Petri-Nets (PNs) are combined with General-Purpose Graphics Processing Unit (GPGPUs) to develop a fast and low cost framework for PN modelling. GPGPUs are composed of many smaller, parallel compute units which has made them ideally suited to highly parallelised computing tasks. Monte Carlo (MC) simulation is used to evaluate the probabilistic performance of the system. The high computational cost of this approach is mitigated through parallelisation. The efficiency of different approaches to parallelisation of the problem is evaluated. The developed framework is then used on a PN model example which supports decision-making in the field of infrastructure asset management. The model incorporates deterioration, inspection and maintenance into a complete decision-support tool. The results obtained show that this method allows the combination of complex PN modelling with rapid computation in a desktop computer.

Citation

Yianni, P. C., Neves, L. C., Rama, D., & Andrews, J. D. (2018). Accelerating Petri-Net simulations using NVIDIA graphics processing units. European Journal of Operational Research, 265(1), https://doi.org/10.1016/j.ejor.2017.06.068

Journal Article Type Article
Acceptance Date Jun 30, 2017
Online Publication Date Jul 8, 2017
Publication Date Feb 16, 2018
Deposit Date Jul 20, 2017
Publicly Available Date Mar 29, 2024
Journal European Journal of Operational Research
Print ISSN 0377-2217
Electronic ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 265
Issue 1
DOI https://doi.org/10.1016/j.ejor.2017.06.068
Keywords CUDA; GPU; Petri-Net; Parallel Asset management
Public URL https://nottingham-repository.worktribe.com/output/911682
Publisher URL http://www.sciencedirect.com/science/article/pii/S0377221717306276?via%3Dihub

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