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Accelerating Petri-Net simulations using NVIDIA Graphics Processing Units

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  • 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.

Suggested Citation

  • Yianni, Panayioti C. & Neves, Luis C. & Rama, Dovile & Andrews, John D., 2018. "Accelerating Petri-Net simulations using NVIDIA Graphics Processing Units," European Journal of Operational Research, Elsevier, vol. 265(1), pages 361-371.
    • Handle: RePEc:eee:ejores:v:265:y:2018:i:1:p:361-371
    DOI: 10.1016/j.ejor.2017.06.068
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Haoyuan & Marsh, D. William R, 2021. "Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    3. Chiachío, Manuel & Saleh, Ali & Naybour, Susannah & Chiachío, Juan & Andrews, John, 2022. "Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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