Abstract
The methods used for ecosystem modelling are generally based on differential equations. Nowadays, new computational models based on concurrent processing of multiple agents (multi-agents) or the simulation of biological processes with the Population Dynamic P-System models (PDPs) are gaining importance. These models have significant advantages over traditional models, such as high computational efficiency, modularity and its ability to model the interaction between different biological processes which operate concurrently. By this, they are becoming useful for simulating complex dynamic ecosystems, untreatable with classical techniques.
On the other hand, the main counterpart of P-System models is the need for calibration. The model parameters represent the field measurements taken by experts. However, the exact values of some of these parameters are unknown and experts define a numerical interval of possible values. Therefore, it is necessary to perform a calibration process to fit the best value of each interval. When the number of unknown parameters increases, the calibration process becomes computationally complex and storage requirements increase significantly.
In this paper, we present a parallel tool (PSysCal) for calibrating next generation PDP models. The results shown that the calibration time is reduced exponentially with the amount of computational resources. However, the complexity of the calibration process and a limitation in the number of available computational resources make the calibration process intractable for large models. To solve this, we propose a heuristic technique (PSysCal+H). The results show that this technique significantly reduces the computational cost, it being practical for solving large model instances even with limited computational resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Buyya, R., Venugopal, S.: A gentle introduction to grid computing and technologies. CSI Commun. 29(1), 9–19 (2005)
Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. Comput. Commun. Rev. 39(1), 50–58 (2008)
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)
Leigh, E.R.: The ecological role of Volterra’s equations. In: Some Mathematical Problems in Biology (1968)
Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)
Alber, M.S., Kiskowski, M.A., Glazier, J.A., Jiang, Y.: On cellular automaton approaches to modeling biological cells. In: Rosenthal, J., Gilliam, D.S. (eds.) Mathematical Systems Theory in Biology, Communication, and Finance. IMA, vol. 134, p. 12. Springer, Berlin (2002)
Mock, K.J., Testa, J.W.: Agent-based model of predator prey relationships between transient killer whales and other marine mammals. University of Alaska Anchorage (2007). http://www.math.uaa.alaska.edu/~orca/
Păun, G.: Computing with membranes. J. Comput. Syst. Sci. 61, 108–143 (1998)
Păun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press, London (2010)
Cardona, M., Colomer, M.A., Margalida, A., Palau, A., Perez-Hurtado, I., Perez-Jimenez, M.J., Sanuy, D.: A computational modeling for real ecosystems based on P-systems. Nat. Comput. 10(1), 39–53 (2011)
pLinguaCore: http://www.p-lingua.org/wiki/index.php/PLinguaCore
Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulation of P systems with active membranes on CUDA. Brief. Bioinform. 11(3), 313–322 (2010)
Perez-Hurtado, I., Valencia-Cabrera, L., Perez-Jimenez, M.J., Colomer, M.A., Riscos-Núñez, A.: MeCoSim: A general purpose software tool for simulating biological phenomena by means of P systems. In: Proceedings 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010). IEEE Press, New York (2010)
Cecilia, J.M., García, J.M., Guerrero, G., Martínez-del-Amor, M.A., Pérez-Jiménez, M.J., Ujaldín, M.: P systems simulation on massively parallel architectures. In: 3rd Workshop on Parallel Architectures and Bioinspired Algorithms (2010)
Cecilia, J.M., García, J.M., Guerrero, G., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulating a P system based efficient solution to SAT by using GPUs. J. Log. Algebr. Program. 79 (2010)
Box, G.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, 2nd edn. Wiley, New York (2005)
Colomer, M.A., Montoti, A., García, E., Fondevilla, C.: A computational model to explain annual fluctuations and extinction risk due to climate change related waterflow in a Calotriton asper population. In: Environment & Pyrenees International Conference, Universidad de Navarra (2011)
Miner, D., Shook, A.: MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems. O’Reily, Sebastopol (2010)
Acknowledgements
This work was supported by the Ministry of Education and Science of Spain under contract TIN2011-28689-C02-02.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lerida, J.L., Agraz, A., Solsona, F. et al. PSysCal: a parallel tool for calibration of ecosystem models. Cluster Comput 17, 271–279 (2014). https://doi.org/10.1007/s10586-013-0310-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-013-0310-7