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Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards

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Search Based Software Engineering (SSBSE 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6956))

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Abstract

Despite claims of “embarrassing parallelism” for many optimisation algorithms, there has been very little work on exploiting parallelism as a route for SBSE scalability. This is an important oversight because scalability is so often a critical success factor for Software Engineering work. This paper shows how relatively inexpensive General Purpose computing on Graphical Processing Units (GPGPU) can be used to run suitably adapted optimisation algorithms, opening up the possibility of cheap scalability. The paper develops a search based optimisation approach for multi objective regression test optimisation, evaluating it on benchmark problems as well as larger real world problems. The results indicate that speed–ups of over 25x are possible using widely available standard GPUs. It is also encouraging that the results reveal a statistically strong correlation between larger problem instances and the degree of speed up achieved. This is the first time that GPGPU has been used for SBSE scalability.

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Author information

Authors and Affiliations

  1. University College, London, UK

    Shin Yoo & Mark Harman

  2. University of Bristol, UK

    Shmuel Ur

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  1. Shin Yoo
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  2. Mark Harman
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  3. Shmuel Ur
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Eingineering, University of Nebraska-Lincoln, 68588-0115, Lincoln, NE, USA

    Myra B. Cohen

  2. School of Computer Science and Informatics, University College Dublin, Belfield, Dubline 4, Ireland

    Mel Ó Cinnéide

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© 2011 Springer-Verlag Berlin Heidelberg

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Yoo, S., Harman, M., Ur, S. (2011). Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards. In: Cohen, M.B., Ó Cinnéide, M. (eds) Search Based Software Engineering. SSBSE 2011. Lecture Notes in Computer Science, vol 6956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23716-4_20

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  • DOI: https://doi.org/10.1007/978-3-642-23716-4_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23715-7

  • Online ISBN: 978-3-642-23716-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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