Accelerating Petri-Net simulations using NVIDIA graphics processing units

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

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: Jul 9, 2019
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 1872-6860
• 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
• Contract Date: Jul 20, 2017