Abstract
A parallel solution to the implementation of evolutionary algorithms is proposed, where the most costly part of the whole evolutionary algorithm computations (the population evaluation), is deported to a GPGPU card. Experiments are presented for two benchmark examples on two models of GPGPU cards: first a ”toy” problem is used to illustrate some noticable behaviour characteristics before a real problem is tested out. Results show a speed-up of up to 100 times compared to an execution on a standard micro-processor. To our knowledge, this solution is the first showing such an efficiency with GPGPU cards. Finally, the EASEA language and its compiler are also extended to allow users to easily specify and generate efficient parallel implementations of evolutionay algorithms using GPGPU cards.
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References
Baumes, L.A., Moliner, M., Corma, A.: Design of a full-profile matching solution for high-throughput analysis of multi-phases samples through powder x-ray diffraction. Chemistry - A European Journal (in press)
Baumes, L.A., Moliner, M., Nicoloyannis, N., Corma, A.: A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder x-ray diffraction data. CrystEngComm. 10, 1321–1324 (2008)
Collet, P., Lutton, E., Schoenauer, M., Louchet, J.: Take it easea. In: Informatics: 10 Years Back, 10 Years Ahead. LNCS, pp. 891–901. Springer, Heidelberg (2000)
Corma, A., Moliner, M., Serra, J.M., Serna, P., Diaz-Cabanas, M.J., Baumes, L.A.: A new mapping/exploration approach for ht synthesis of zeolites. Chemistry of Materials, 3287–3296 (2006)
Darwin, C.: On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. John Murray, London (1859)
Fogel, D.B.: Evolving artificial intelligence. Technical report (1992)
Fok, K.-L., Wong, T.-T., Wong, M.-L.: Evolutionary computing on consumer graphics hardware. IEEE Intelligent Systems 22(2), 69–78 (2007)
Li, J.-M., Wang, X.-J., He, R.-S., Chi, Z.-X.: An efficient fine-grained parallel genetic algorithm based on gpu-accelerated. In: NPC Workshops. IFIP International Conference on Network and Parallel Computing Workshops, 2007, pp. 855–862 (2007)
De Jong, K.: Evolutionary Computation: a Unified Approach. MIT Press, Cambridge (2005)
Young, R.A.: The Rietveld Method. OUP and International Union of Crystallography (1993)
Yu, Q., Chen, C., Pan, Z.: Parallel genetic algorithms on programmable graphics hardware. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 1051–1059. Springer, Heidelberg (2005)
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Maitre, O., Lachiche, N., Clauss, P., Baumes, L., Corma, A., Collet, P. (2009). Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards. In: Sips, H., Epema, D., Lin, HX. (eds) Euro-Par 2009 Parallel Processing. Euro-Par 2009. Lecture Notes in Computer Science, vol 5704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03869-3_89
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DOI: https://doi.org/10.1007/978-3-642-03869-3_89
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