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Enhancing GPU parallelism in nature-inspired algorithms

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Abstract

We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia’s Fermi architecture.

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Acknowledgements

Partially supported by a travel grant from project EU FP7 NoE HiPEAC IST-217068, and by the Spanish MEC and European Commission FEDER funds under grants “Consolider Ingenio-2010 CSD2006-00046” and “TIN2009-14475-C04-02”, and also by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grant 15290/PI/2010. We also thank NVIDIA for hardware donation under Professor Partnership 2008–2010, CUDA Teaching Center 2011–2013, CUDA Research Center 2012 and CUDA Fellow 2012 Awards.

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Authors and Affiliations

  1. Departamento de Informática, Escuela politécnica, Universidad Católica San Antonio Murcia, Campus de los Jerónimos S/N, Guadalupe, 30107, Murcia, Spain

    José M. Cecilia

  2. School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK

    Andy Nisbet & Martyn Amos

  3. Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30080, Murcia, Spain

    José M. García

  4. Computer Architecture Department, ETSI Informática, University of Málaga, Campus Teatinos, Bulevar Louis Pasteur, s/n, 29071, Málaga, Spain

    Manuel Ujaldón

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  1. José M. Cecilia
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  2. Andy Nisbet
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  3. Martyn Amos
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  4. José M. García
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  5. Manuel Ujaldón
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Correspondence to José M. Cecilia.

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Cecilia, J.M., Nisbet, A., Amos, M. et al. Enhancing GPU parallelism in nature-inspired algorithms. J Supercomput 63, 773–789 (2013). https://doi.org/10.1007/s11227-012-0770-1

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  • DOI: https://doi.org/10.1007/s11227-012-0770-1

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