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GA-Based Compiler Parameter Set Tuning

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Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 324))

Abstract

Determining nearly optimal optimization options for modern-day compilers is a combinatorial problem. Added to this, specific to a given application, platform and optimization objective, fine-tuning the parameter set being used by various optimization passes, enhance the complexity further. In this paper, we apply genetic algorithm (GA) to tune compiler parameter set and investigate the impact of fine-tuning the parameter set on the code size. The effectiveness of GA-based parameter tuning mechanism is demonstrated with the benchmark programs from SPEC2006 benchmark suite that there is a significant impact of tuning the parameter values on the code size. Results obtained by the proposed GA-based parameter tuning technique are compared with existing methods and that shows significant performance gains.

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Author information

Authors and Affiliations

  1. ANURAG, Hyderabad, India

    N. A. B Sankar Chebolu

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    N. A. B Sankar Chebolu, Rajeev Wankar & Raghavendra Rao Chillarige

Authors
  1. N. A. B Sankar Chebolu
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  2. Rajeev Wankar
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  3. Raghavendra Rao Chillarige
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Corresponding author

Correspondence to N. A. B Sankar Chebolu .

Editor information

Editors and Affiliations

  1. Electrical & Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L. Padma Suresh

  2. Electrical and Electronics Engineering, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  3. Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Chebolu, N.A.B.S., Wankar, R., Chillarige, R.R. (2015). GA-Based Compiler Parameter Set Tuning. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_22

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  • DOI: https://doi.org/10.1007/978-81-322-2126-5_22

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2125-8

  • Online ISBN: 978-81-322-2126-5

  • eBook Packages: EngineeringEngineering (R0)

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