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Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm

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Self-Organizing Migrating Algorithm

Part of the book series: Studies in Computational Intelligence ((SCI,volume 626))

Abstract

Evolutionary algorithms can take advantage of parallel computing, because it decreases the computational time and increases the size of processable instances. In this chapter, various options for a parallelization of Discrete Self-Organising Migrating Algorithm are described, with three implemented parallel variants described in greater detail. They covers the most frequently used hardware and software technologies, namely: parallel computing with threads and shared memory; general purpose programming on GPUs with CUDA; and distributed computing with MPI. The first two implementations speed up the computation, the last one moreover changes the original algorithm. It adds a new layer that simplifies its usage in the distributed environment.

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Notes

  1. 1.

    http://www.intel.com/content/www/us/en/processors/xeon/xeon-phi-detail.html.

  2. 2.

    http://www.top500.org/system/177975.

  3. 3.

    http://www.cplusplus.com/reference/thread/thread/.

  4. 4.

    http://openmp.org/.

  5. 5.

    https://www.cilkplus.org/.

  6. 6.

    http://www.mpi-forum.org/.

  7. 7.

    http://www.khronos.org.

  8. 8.

    http://www.microsoft.com.

  9. 9.

    https://gcc.gnu.org/onlinedocs/libgomp/Enabling-OpenMP.html.

  10. 10.

    The example is a part of Kaira’s distribution.

  11. 11.

    http://www.cplusplus.com/reference/cstdlib/rand/.

  12. 12.

    http://www.it4i.cz/.

  13. 13.

    https://docs.it4i.cz/anselm-cluster-documentation/hardware-overview.

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Acknowledgements

This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme, as well as Czech Ministry of Education, Youth and Sports via the project Large Research, Development and Innovations Infrastructures (LM2011033). The work was also partially supported by Grants of SGS No. SP2015/146 and SP2015/123, VŠB—Technical University of Ostrava, Czech Republic.

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Authors and Affiliations

  1. FEECS, Department of Computer Science, VŠB—Technical University of Ostrava, Ostrava, Czech Republic

    Marek Běhálek, Petr Gajdoš & Donald Davendra

  2. IT4Innovations National Supercomputing Center, VŠB—Technical University of Ostrava, Ostrava, Czech Republic

    Marek Běhálek & Petr Gajdoš

  3. Department of Computer Science, Central Washington University, 400 E. University Way, Ellensburg, WA, 98926-7520, USA

    Donald Davendra

Authors
  1. Marek Běhálek
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  2. Petr Gajdoš
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  3. Donald Davendra
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Correspondence to Marek Běhálek .

Editor information

Editors and Affiliations

  1. Faculty of Ele. Eng. & Computer Sci, Dep. of Comp. Sci. Central Washington Un, Ellensburg, Czech Republic

    Donald Davendra

  2. Faculty of Ele.Eng. & Comp. Sci., VŠB-Technical University of Ostrav, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

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Běhálek, M., Gajdoš, P., Davendra, D. (2016). Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm. In: Davendra, D., Zelinka, I. (eds) Self-Organizing Migrating Algorithm. Studies in Computational Intelligence, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-319-28161-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-28161-2_6

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