Abstract
This chapter presents an in-depth study of a novel parallel optimization algorithm specially designed to run on Graphic Processing Units (GPUs). The underlying operation relates to systolic computing and is inspired by the systolic contraction of the heart that makes possible blood circulation. The algorithm, called Systolic Genetic Search (SGS), is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs to achieve high time performance. SGS has shown not only to numerically outperform a random search and two genetic algorithms for solving the Knapsack Problem over a set of increasingly sized instances, but also its parallel implementation can obtain a runtime reduction that, depending on the GPU technology used, can reach more than 100 times. A study of the performance of the parallel implementation of SGS on four different GPUs has been conducted to show the impact of the Nvidia’s GPU compute capabilities on the runtimes of the algorithm.
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Acknowledgements
Martín Pedemonte acknowledges support from Programa de Desarrollo de las Ciencias Básicas, Universidad de la República, and Agencia Nacional de Investigación e Innovación, Uruguay. Enrique Alba and Francisco Luna acknowledge support from the “Consejería de Innovación, Ciencia y Empresa,” Junta de Andalucía under contract P07-TIC-03044, the DIRICOM project (http://diricom.lcc.uma.es), and the Spanish Ministry of Science and Innovation and FEDER under contracts TIN2008-06491-C04-01 (the M* project: http://mstar.lcc.uma.es) and TIN2011-28194 (the roadME project).
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Pedemonte, M., Luna, F., Alba, E. (2013). New Ideas in Parallel Metaheuristics on GPU: Systolic Genetic Search. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_10
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DOI: https://doi.org/10.1007/978-3-642-37959-8_10
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