OpenCAL simulation of the 1992 Tessina landslide

  • Donato D’Ambrosio  ,
  • Alessio De Rango  ,
  • Rocco Rongo  
  • a, b, c Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
Cite as
D’Ambrosio D., De Rango A., Rongo R. (2018). OpenCAL simulation of the 1992 Tessina landslide. Proceedings of the 30th European Modeling & Simulation Symposium (EMSS 2018), pp. 210-217. DOI: https://doi.org/10.46354/i3m.2018.emss.029

Abstract

OpenCAL is a scientific software library developed for the simulation of 2D/3D complex dynamical systems on multi/many-core systems. A MPI preliminary extension also allows for the execution on cluster of many-core devices. The library provides the Extended Cellular Automata paradigm as a Domain-Specific Language for modeling complex systems on structured grids. Here we briefly describe the software library and show a first application regarding the implementation of a simple but effective landslide simulation model, namely the SciddicaT extended cellular automaton. The application to a real case of study, namely the 1992 Tessina landslide (Italy), is also shown. Computational results achieved on an Intel Xeon E5-2650 socket, a Nvidia Tesla K40 compute dedicated many-core device and a Nvidia GeForce GTX 980 GPU are reported.

References

  1. Kumar V., 2002. Introduction to Parallel Computing, 2nd Edition. Addison-Wesley Longman
    Publishing Co., Inc., Boston, MA, USA.
  2. Golub G.H., Ortega J.M., 2014. Scientific computing: an introduction with parallel computing. Academic Press, London, UK.
  3. Giles M., Mudalige G., Spencer B., Bertolli C., Reguly I., 2013. Designing OP2 for GPU architectures. Journal of Parallel and Distributed Computing, 73 (11), 1451–1460.
  4. Reguly I., Mudalige G., Giles M., Curran D., McIntosh- Smith S., 2014. The OPS domain specific
    abstraction for multi-block structured grid computations. Proceedings of WOLFHPC 2014:
    4th International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing - Held in Conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 58–67. November 17, New Orleans (Louisiana, USA).
  5. Malcolm, J., Yalamanchili, P., McClanahan, C., Venugopalakrishnan, V., Patel, K., Melonakos,
    J., 2012. ArrayFire: A GPU acceleration platform. Proceedings of SPIE - The International Society
    for Optical Engineering, 8403, art. no. 84030A.
  6. Su, B.-Y., Keutzer, K., 2012. clSpMV: A crossplatform OpenCL SpMV framework on GPUs
    Proceedings of ICS 2012: 26th ACM international conference on Supercomputing, 2012, pp. 353–364.
  7. Zhang, Y., Li, S., Yan, S., Zhou, H., 2016. A crossplatform SpMV framework on many-core
    architectures. ACM Transactions on Architecture and Code Optimization, 13 (4), 33.
  8. Aliaga, J.I., Reyes, R., Goli, M., 2018. SYCL-BLAS: Combining Expression Trees and Kernel Fusion
    on Heterogeneous Systems. Advances in Parallel Computing, 32, 349–358.
  9. D’Ambrosio D., De Rango A., Oliverio M., Spataro D., Spataro W., Rongo R., Mendicino G., Senatore A., 2018. The Open Computing Abstraction Layer for Parallel Complex Systems Modeling on Many- Core Systems. Journal of Parallel and Distributed Computing, doi: 10.1016/j.jpdc.2018.07.005
  10. De Rango A., Spataro D., Spataro W., D’Ambrosio D., accepted. A First Multi-GPU/Multi-Node
    Implementation of the Open Computing Abstraction Layer. Journal of Computational
    Science.
  11. De Rango A., De Napoli P., D’Ambrosio D., Spataro W., Di Renzo A., Di Maio F., 2018. Structured
    Grid-Based Parallel Simulation of a Simple DEM Model on Heterogeneous Systems. Proceedings of The 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 588-595. March, 21-23, Cambridge (UK).
  12. Di Gregorio S., Serra R., 1999. An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata. Future Generation Computer Systems, 16, 259–271.
  13. Avolio M.V., Crisci G.M., Di Gregorio S., Rongo R., Spataro W., D' Ambrosio, D., 2006. Pyroclastic
    flows modelling using cellular automata. Computers and Geosciences, 7 (32), 897–911.
  14. Avolio M.V., Di Gregorio S., Lupiano V., Mazzanti P., 2013. Sciddica-SS3: A new version of cellular automata model for simulating fast moving landslides. The Journal of Supercomputing, 65 (2), 682–696.
  15. D’Ambrosio D., Filippone F., Marocco D., Rongo R., Spataro W., 2013. Efficient application of GPGPU for lava flow hazard mapping. The Journal o Supercomputing, 65 (2) 630–644.
  16. Spataro D., D'Ambrosio D., Filippone G., Rongo R., Spataro W., Marocco D., 2013. The new
    SCIARA-fv3 numerical model and acceleration by GPGPU strategies. International Journal of High Performance Computing Applications, 31 (2), 163–176.
  17. Machado G., Lupiano V., Avolio M.V., Gullace, F., Di Gregorio, S. 2015. A cellular model for secondary lahars and simulation of cases in the Vascún Valley, Ecuador. Journal of Computational Science, 11, 289–299.
  18. Filippone G., Spataro W., D’Ambrosio D., Spataro D., Marocco D., Trunfio G., 2015. Cuda dynamic active thread list strategy to accelerate debris flow simulations. Proceedings of The 23rd EuromicroInternational Conference on Parallel, Distributed, and Network-Based Processing, pp. 316–320. March 4-6, Turku (Finland).
  19. Avolio MV, Di Gregorio S., Mantovani F, Pasuto A, Rongo R, Silvano S., Spataro W., 2000.
    Simulation of the 1992 Tessina landslide by a cellular automata model and future hazard
    scenarios. International Journal of Applied Earth Observation and Geoinformation, 1(2), 41–50.