Abstract
This paper tackles the problem of modeling a shared-memory metaheuristic scheme. The use of a model of the execution time allows us to decide at running time the number of threads to use to obtain a reduced execution time. A parameterized metaheuristic scheme is used, so different metaheuristics and hybridations can be applied to a particular problem, and it is easier to obtain a satisfactory metaheuristic for the problem. The model of the execution time and consequently the optimum number of threads depend on a number of factors: the problem to be solved, the metaheuristic scheme and the implementation of the basic functions in it, the computational system where the problem is being solved, etc. So, obtaining a satisfactory model and an autotuning methodology is not an easy task. This paper presents an autotuning methodology for shared-memory parameterized metaheuristic schemes, and its application to a problem of minimization of electricity consumption in exploitation of wells. The model and the methodology work satisfactorily, which allows us to reduce the execution time and to obtain lower electricity consumptions than previously obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley Interscience (2005)
Almeida, F., Cuenca, J., Giménez, D., Llanes-Castro, A., Martínez-Gallar, J.P.: A Framework for the Application of Metaheuristics to Tasks-to-Processors Assignation Problems. The Journal of Supercomputing (2009) (published online)
Almeida, F., Giménez, D., López-Espín, J.J.: A parameterised shared-memory scheme for parameterised metaheuristics. The Journal of Supercomputing (2011) (published online)
Cuenca, J., Giménez, D., González, J.: Architecture of an Automatic Tuned Linear Algebra Library. Parallel Computing 30, 187–220 (2004)
Cuenca, J., Giménez, D., Martínez-Gallar, J.P.: Heuristics for work distribution of a homogeneous parallel dynamic programming scheme on heterogeneous systems. Parallel Computing 31, 717–735 (2005)
Clinton Whaley, R., Petitet, A., Dongarra, J.: Automated empirical optimizations of software and the ATLAS project. Parallel Computing 27, 3–35 (2001)
Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization. Springer (2005)
Frigo, M.: FFTW: An Adaptive Software Architecture for the FFT. In: Proceedings of the ICASSP Conference, vol. 3, p. 1381 (1998)
Glover, F., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer (2003)
Raidl, G.R.: A Unified View on Hybrid Metaheuristics. In: Almeida, F., Blesa Aguilera, M.J., Blum, C., Moreno Vega, J.M., Pérez Pérez, M., Roli, A., Sampels, M. (eds.) HM 2006. LNCS, vol. 4030, pp. 1–12. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cutillas-Lozano, LG., Cutillas-Lozano, JM., Giménez, D. (2012). Modeling Shared-Memory Metaheuristic Schemes for Electricity Consumption. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-28765-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28764-0
Online ISBN: 978-3-642-28765-7
eBook Packages: EngineeringEngineering (R0)