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A GPU Numerical Implementation of a 2D Simplified Wildfire Spreading Model

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High Performance Computing (CARLA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1887))

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Abstract

Wildfires are a latent problem worldwide that every year burns thousands of hectares, negatively impacting the environment. To mitigate the damage, there is software to support wildfire analysis. Many of these computational tools are based on different mathematical models, each with its own advantages and disadvantages. Unfortunately, only a few of the software are open source. This work aims to develop an open-source GPU implementation of a mathematical model for the spread of wildfires using CUDA. The algorithm is based on the Method of Lines, allowing it to work with a system of partial differential equations as a dynamical system. We present the advantages of a GPU versus C and an OpenMP multi-threaded CPU implementation for computing the outcome of several scenarios.

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References

  1. Alexandridis, A., Vakalis, D., Siettos, C., Bafas, G.: A cellular automata model for forest fire spread prediction: the case of the wildfire that swept through Spetses Island in 1990. Appl. Math. Comput. 204(1), 191–201 (2008). https://doi.org/10.1016/j.amc.2008.06.046

    Article  MathSciNet  Google Scholar 

  2. Almeida, R.M., Macau, E.E.N.: Stochastic cellular automata model for wildland fire spread dynamics. J. Phys: Conf. Ser. 285(1), 12038 (2011). https://doi.org/10.1088/1742-6596/285/1/012038

    Article  Google Scholar 

  3. Arganaraz, J., Lighezzolo, A., Clemoveki, K., Bridera, D., Scavuzzo, J., Bellis, L.: Operational meteo fire risk system based on space information for Chaco Serrano. IEEE Lat. Am. Trans. 16(3), 975–980 (2018). https://doi.org/10.1109/TLA.2018.8358681

    Article  Google Scholar 

  4. Asensio, M.I., Ferragut, L.: On a wildland fire model with radiation. Int. J. Numer. Meth. Eng. 54(1), 137–157 (2002). https://doi.org/10.1002/nme.420

    Article  MathSciNet  Google Scholar 

  5. Carrillo, C., Margalef, T., Espinosa, A., Cortés, A.: Accelerating wild fire simulator using GPU. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11540, pp. 521–527. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22750-0_46

    Chapter  Google Scholar 

  6. Carrillo, C., Cortés, A., Margalef, T., Espinosa, A., Cencerrado, A.: Applying GPU parallel technology to accelerate FARSITE forest fire simulator. In: Advances in Forest Fire Research, pp. 913–921 (2018). https://doi.org/10.14195/978-989-26-16-506_100

  7. Chopard, B., Droz, M.: Cellular automata model for the diffusion equation. J. Stat. Phys. 64(3), 859–892 (1991). https://doi.org/10.1007/BF01048321

    Article  MathSciNet  Google Scholar 

  8. CONAF: Incendios Forestales en Chile (2021). http://www.conaf.cl/incendios-forestales/incendios-forestales-en-chile/

  9. Denham, M., Laneri, K.: Using efficient parallelization in graphic processing units to parameterize stochastic fire propagation models. J. Comput. Sci. 25, 76–88 (2018). https://doi.org/10.1016/J.JOCS.2018.02.007

    Article  Google Scholar 

  10. Denham, M.M., Waidelich, S., Laneri, K.: Visualization and modeling of forest fire propagation in Patagonia. Environ. Model. Softw. 158, 105526 (2022). https://doi.org/10.1016/J.ENVSOFT.2022.105526

    Article  Google Scholar 

  11. D’Ambrosio, D., Gregorio, S.D., Filippone, G., Rongo, R., Spataro, W., Trunfio, G.A.: A Multi-GPU approach to fast wildfire hazard mapping. Adv. Intell. Syst. Comput. 256, 183–195 (2014). https://doi.org/10.1007/978-3-319-03581-9_13

    Article  Google Scholar 

  12. Eberle, S.: Modeling and simulation of forest fire spreading. In: Eulogio, P.I., Guardiola-Albert, Carolina, Javier, H., Luis, M.M., José, D.J., Antonio, V.G.J. (eds.) Mathematics of Planet Earth, pp. 811–814. Springer, Berlin Heidelberg, Berlin, Heidelberg (2014). https://doi.org/10.1007/978-3-642-32408-6_175

  13. Eberle, S., Freeden, W., Matthes, U.: Forest fire spreading. In: Freeden, W., Nashed, M.Z., Sonar, T. (eds.) Handbook of Geomathematics, pp. 1349–1385. Springer, Berlin Heidelberg, Berlin, Heidelberg (2015). https://doi.org/10.1007/978-3-642-54551-1_70

  14. Fernandez-Anez, N., Christensen, K., Rein, G.: Two-dimensional model of smouldering combustion using multi-layer cellular automaton: the role of ignition location and direction of airflow. Fire Saf. J. 91, 243–251 (2017). https://doi.org/10.1016/J.FIRESAF.2017.03.009

    Article  Google Scholar 

  15. Ferragut, L., Asensio, M.I., Cascón, J.M., Prieto, D.: A wildland fire physical model well suited to data assimilation. Pure Appl. Geophys. 172(1), 121–139 (2015). https://doi.org/10.1007/s00024-014-0893-9

    Article  Google Scholar 

  16. Ferragut, L., Asensio, M.I., Monedero, S.: Modelling radiation and moisture content in fire spread. Commun. Numer. Meth. Eng. 23, 819–833 (2006). https://doi.org/10.1002/cnm.927

    Article  MathSciNet  Google Scholar 

  17. Ferragut, L., Asensio, M.I., Monedero, S.: A numerical method for solving convection-reaction-diffusion multivalued equations in fire spread modelling. Adv. Eng. Softw. 38(6), 366–371 (2007). https://doi.org/10.1016/J.ADVENGSOFT.2006.09.007

    Article  Google Scholar 

  18. Ghisu, T., Arca, B., Pellizzaro, G., Duce, P.: An improved cellular automata for wildfire spread. Procedia Comput. Sci. 51, 2287–2296 (2015). https://doi.org/10.1016/J.PROCS.2015.05.388

    Article  Google Scholar 

  19. Hansen, P.B.: Parallel cellular automata: a model program for computational science. Concurrency Pract. Experience 5(5), 425–448 (1993). https://doi.org/10.1002/cpe.4330050504

    Article  Google Scholar 

  20. Harris, M.: Introducing parallel forall. https://developer.nvidia.com/blog/?p=8. Accessed 3 Oct 2023

  21. Karafyllidis, I., Thanailakis, A.: A model for predicting forest fire spreading using cellular automata. Ecol. Model. 99(1), 87–97 (1997). https://doi.org/10.1016/S0304-3800(96)01942-4

    Article  Google Scholar 

  22. Mandel, J., et al.: A wildland fire model with data assimilation. Math. Comput. Simul. 79(3), 584–606 (2008). https://doi.org/10.1016/j.matcom.2008.03.015

    Article  MathSciNet  Google Scholar 

  23. Mell, W., Jenkins, M.A., Gould, J., Cheney, P.: A physics-based approach to modelling grassland fires. Int. J. Wildland Fire 16(1), 1–22 (2007). https://doi.org/10.1071/WF06002

    Article  Google Scholar 

  24. Montenegro, R., Plaza, A., Ferragut, L., Asensio, M.I.: Application of a nonlinear evolution model to fire propagation. Nonlinear Anal. Theory Methods Appl. 30(5), 2873–2882 (1997). https://doi.org/10.1016/S0362-546X(97)00341-6

    Article  MathSciNet  Google Scholar 

  25. Ntinas, V.G., Moutafis, B.E., Trunfio, G.A., Sirakoulis, G.C.: GPU and FPGA parallelization of fuzzy cellular automata for the simulation of wildfire spreading. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds.) PPAM 2015. LNCS, vol. 9574, pp. 560–569. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32152-3_52

    Chapter  Google Scholar 

  26. NVIDIA: CUDA C++ Programming Guide. https://docs.nvidia.com/cuda/cuda-c-programming-guide/. Accessed 3 Oct 2023

  27. Oliphant, T.E.: Python for scientific computing. Comput. Sci. Eng. 9(3), 10–20 (2007). https://doi.org/10.1109/MCSE.2007.58

    Article  Google Scholar 

  28. Preisler, H.K., Ager, A.A.: Forest-Fire Models. Encycl. Environmetrics (2013). https://doi.org/10.1002/9780470057339.vaf010.pub2

    Article  Google Scholar 

  29. San Martín, D., Torres, C.E.: Exploring a spectral numerical algorithm for solving a wildfire mathematical model. In: 2019 38th International Conference of the Chilean Computer Science Society (SCCC), pp. 1–7 (2019). https://doi.org/10.1109/SCCC49216.2019.8966412

  30. San Martín, D., Torres, C.E.: Ngen-Kütral: Toward an open source framework for chilean wildfire spreading. In: 2018 37th International Conference of the Chilean Computer Science Society (SCCC), pp. 1–8 (2018). https://doi.org/10.1109/SCCC.2018.8705159

  31. San Martin, D., Torres, C.: Open source framework for chilean wildfire spreading (2019). https://github.com/dsanmartin/ngen-kutral. Accessed 1 Mar 2019

  32. San Martin, D., Torres, C.: Open source framework for Chilean wildfire spreading: GPU implementation (2019). https://github.com/dsanmartin/ngen-kutral-gpu. Accessed 1 Mar 2019

  33. San Martin, D., Torres, C.E.: 2D simplified wildfire spreading model in Python: from NumPy to CuPy. CLEI Electron. J. 26, 5:1-5:18 (2023). https://doi.org/10.19153/CLEIEJ.26.1.5

    Article  Google Scholar 

  34. Smith, J., Barfed, L., Dasclu, S.M., Harris, F.C.: Highly parallel implementation of forest fire propagation models on the GPU. In: 2016 International Conference on High Performance Computing and Simulation, HPCS 2016, pp. 917–924 (2016). https://doi.org/10.1109/HPCSIM.2016.7568432

  35. Sousa, F.A., dos Reis, R.J., Pereira, J.C.: Simulation of surface fire fronts using fireLib and GPUs. Environ. Model. Softw. 38, 167–177 (2012). https://doi.org/10.1016/J.ENVSOFT.2012.06.006

    Article  Google Scholar 

  36. Trefethen, L.N.: Spectral Methods in MATLAB. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2000). https://doi.org/10.1137/1.9780898719598

  37. Wu, R., et al.: vFirelib: a GPU-based fire simulation and visualization tool. SoftwareX 23, 101411 (2023). https://doi.org/10.1016/J.SOFTX.2023.101411

    Article  Google Scholar 

  38. Zambrano, M., Pérez, I., Carvajal, F., Esteve, M., Palau, C.: Command and control information systems applied to large forest fires response. IEEE Lat. Am. Trans. 15(9), 1735–1741 (2017). https://doi.org/10.1109/TLA.2017.8015080

    Article  Google Scholar 

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Acknowledgment

This work was partially supported by ANID-Subdirección de Capital Humano/Doctorado Nacional/2019-21191017, ANID PIA/APOYO AFB220004 Centro Científico Tecnológico de Valparaíso - CCTVal, and Programa de Iniciación a la Investigación Científica (PIIC) from Dirección de Postgrado y Programas, Universidad Técnica Federico Santa María, Chile.

Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC (ECM-02).

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Authors and Affiliations

  1. Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Daniel San Martin & Claudio E. Torres

  2. Centro Científico Tecnológico de Valparaíso, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Claudio E. Torres

Authors
  1. Daniel San Martin
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  2. Claudio E. Torres
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Corresponding author

Correspondence to Daniel San Martin .

Editor information

Editors and Affiliations

  1. Industrial University of Santander, Bucaramanga, Colombia

    Carlos J. Barrios H.

  2. Argonne National Laboratory, Lemont, IL, USA

    Silvio Rizzi

  3. Centro Nacional de Alta Tecnología, San José, Costa Rica

    Esteban Meneses

  4. University of Buenos Aires & Center for Computational Simulation Aplicaciones Tecnológicas, Buenos Aires, Argentina

    Esteban Mocskos

  5. Argonne National Laboratory, Lemont, IL, USA

    Jose M. Monsalve Diaz

  6. University of Cartagena, Cartagena, Colombia

    Javier Montoya

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San Martin, D., Torres, C.E. (2024). A GPU Numerical Implementation of a 2D Simplified Wildfire Spreading Model. In: Barrios H., C.J., Rizzi, S., Meneses, E., Mocskos, E., Monsalve Diaz, J.M., Montoya, J. (eds) High Performance Computing. CARLA 2023. Communications in Computer and Information Science, vol 1887. Springer, Cham. https://doi.org/10.1007/978-3-031-52186-7_9

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  • DOI: https://doi.org/10.1007/978-3-031-52186-7_9

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