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International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

IPMU 2018: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications pp 272–283Cite as

Analyzing the Influence of LLVM Code Optimization Passes on Software Performance

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 855))

Abstract

Sensitivity analysis is a mathematical tool that distributes the uncertainty of the output of a model among its different input variables. We use in this work the Extended Fourier Amplitude Sensitivity Test to carefully analyze the impact of 54 LLVM code optimization operators on the execution time of nine benchmark software programs. Experiments presented involve performing over 16 million executions. The results show that the different LLVM transformations have a low direct effect on the execution time, but it becomes meaningful when considering the transformation in combination with the others (almost 60% average impact by all passes on all considered benchmarks). These results provide slight indications on the transformations to apply for optimizing the software, revealing the extreme difficulty of the problem.

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Acknowledgment

The authors would like to acknowledge the Spanish MINECO and ERDF for the support provided under contracts TIN2014-60844-R (the SAVANT project) and RYC-2013-13355.

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Authors and Affiliations

  1. Computer Sciences Engineering Department, University of Cadiz, Cádiz, Spain

    Juan Carlos de la Torre, Bernabé Dorronsoro & Pedro L. Galindo

  2. Mechanical Engineering and Industrial Design Department, University of Cadiz, Cádiz, Spain

    Patricia Ruiz

Authors
  1. Juan Carlos de la Torre
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  2. Patricia Ruiz
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  3. Bernabé Dorronsoro
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  4. Pedro L. Galindo
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Corresponding author

Correspondence to Bernabé Dorronsoro .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cádiz, Cadiz, Spain

    Jesús Medina

  2. Universidad de Málaga, Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  3. Universidad de Granada, Granada, Spain

    José Luis Verdegay

  4. Institute for Research and Applications, University of Ostrava, Ostrava, Czech Republic

    Irina Perfilieva

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Iona College, New Rochelle, New York, USA

    Ronald R. Yager

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de la Torre, J.C., Ruiz, P., Dorronsoro, B., Galindo, P.L. (2018). Analyzing the Influence of LLVM Code Optimization Passes on Software Performance. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_23

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  • DOI: https://doi.org/10.1007/978-3-319-91479-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91478-7

  • Online ISBN: 978-3-319-91479-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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