Abstract
The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe’s most active volcano.





Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Behncke B, Neri M (2003) Cycles and trends in the recent eruptive behaviour of Mount Etna (Italy). Can J Earth Sci 40:1405–1411
Dibben C (2008) Leaving the city for the suburbs—the dominance of ‘ordinary’ decision making over volcanic risk perception in the production of volcanic risk on Mt Etna. J Volcanol Geotherm Res 172:288–299
Barberi F, Brondi F, Carapezza ML, Cavarra L, Murgia C (2003) Earthen barriers to control lava flows in the 2001 eruption of Mt Etna. J Volcanol Geotherm Res 123:231–243
Ishihara K, Iguchi M, Kamo K (1990) Numerical simulation of lava flows on some volcanoes in Japan. In: IAVCEI proceedings in volcanology, pp 174–207
Del Negro C, Fortuna L, Herault A, Vicari A (2008) Simulations of the 2004 lava flow at Etna volcano using the MAGFLOW Cellular Automata model. Bull Volcanol 70:805–812
Avolio MV, Crisci GM, Di Gregorio S, Rongo R, Spataro W, D’Ambrosio D (2006) Pyroclastic flows modeling using cellular automata. Comput Geosci 32:897–911
Crisci GM, Rongo R, Di Gregorio S, Spataro W (2004) The simulation model SCIARA: the 1991 and 2001 lava flows at Mount Etna. J Volcanol Geotherm Res 132:253–267
NVIDIA CUDA C Programming Guide (2010) v. 3.2
von Neumann J (1966) Theory of self-reproducing automata. University of Illinois Press, Champaign. Edited and completed by A Burks
D’Ambrosio D, Spataro W (2007) Parallel evolutionary modeling of geological processes. Parallel Comput 33(3):186–212
Setoodeh S, Adams DB, Gürdal Z, Watson LT (2006) Pipeline implementation of cellular automata for structural design on message-passing multiprocessors. Math Comput Model 43:966–975
Di Gregorio S, Serra R (1999) An empirical method for modeling and simulating some complex macroscopic phenomena by cellular automata. Future Gener Comput Syst 16:259–271
Spataro W, Avolio MV, Lupiano V, Trunfio GA, Rocco R, D’Ambrosio D (2010) The latest release of the lava flows simulation model SCIARA: first application to Mt Etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface. In: Proceedings of the international conference on computational science 2010. Procedia computer science, vol 1, pp 17–26
Crisci GM, Di Gregorio S, Nicoletta F, Rongo R, Spataro W (1999) Analysing lava risk for the Etnean area: simulation by cellular automata methods. Nat Hazards 20:215–229
NVIDIA CUDA C Best Practices Guide (2012)
Zuo W, Chen Q (2010) Fast and informative flow simulations in a building by using fast fluid dynamics model on graphics processing unit. Build Environ 45(3):747–757
Riegel E, Indinger T, Adams NA (2009) Implementation of a Lattice–Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology. Comput Sci Res Dev 23:241–247
Preis T (2011) GPU computing in econophysics and statistical physics. Eur Phys J Spec Top 194:87–119
Roberts M, Sousa MC, Mitchell JR (2010) A work-efficient GPU algorithm for level set segmentation. In: SIGGRAPH 2010 conference, vol 53
Filippone G, Spataro W, Spingola G, D’Ambrosio D, Rongo R, Perna G, Di Gregorio S (2011) GPGPU programming and cellular automata: implementation of the SCIARA lava flow simulation code. In: Proceedings of the 23rd European Modeling and Simulation Symposium (EMSS), Rome, Italy, 12–14 September 2011, pp 696–702
Bilotta G, Rustico E, Hérault A, Vicari A, Russo G, Del Negro C, Gallo G (2011) Porting and optimizing MAGFLOW on CUDA. Ann Geophys 5:54
D’Ambrosio D, Filippone G, Rongo R, Spataro W, Trunfio GA (2012) Cellular automata and GPGPU: an application to lava flow modeling. Int J Grid and High Perform Comput 4(3):30–47
Crisci GM, Avolio MV, Behncke B, D’Ambrosio D, Di Gregorio S, Lupiano V, Neri M, Rongo R, Spataro W (2010) Predicting the impact of lava flows at Mount Etna. J Geophys Res 115(B4):1–14
Rongo R, Avolio MV, Behncke B, D’Ambrosio D, Di Gregorio S, Lupiano V, Neri M, Spataro W, Crisci GM (2011) Defining high-detail hazard maps by a cellular automata approach: application to Mount Etna (Italy). Ann Geophys 54:568–578
Walter R, Worsch T (2004) Efficient simulation of CA with few activities. In: ACRI 2004. LNCS, vol 3305, pp 101–110
Acknowledgements
This work was partially funded by the European Commission—European Social Fund (ESF) and by the Regione Calabria (Italy).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
D’Ambrosio, D., Filippone, G., Marocco, D. et al. Efficient application of GPGPU for lava flow hazard mapping. J Supercomput 65, 630–644 (2013). https://doi.org/10.1007/s11227-013-0949-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-013-0949-0