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Predicting Worst-Case Execution Times During Multi-criterial Function Inlining

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13163))

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Abstract

In the domain of hard real-time systems, the Worst-Case Execution Time (WCET) is one of the most important design criteria. Safely and accurately estimating the WCET during a static WCET analysis is computationally demanding because of the involved data flow, control flow, and microarchitecture analyses. This becomes critical in the field of multi-criterial compiler optimizations that trade the WCET with other design objectives. Evolutionary algorithms are typically exploited to solve a multi-objective optimization problem, but they require an extensive evaluation of the objectives to explore the search space of the problem. This paper proposes a method that utilizes machine learning to build a surrogate model in order to quickly predict the WCET instead of costly estimating it using static WCET analysis. We build a prediction model that is independent of the source code and assembly code features, so a compiler can utilize it to perform any compiler-based optimization. We demonstrate the effectiveness of our model on multi-criterial function inlining, where we aim to explore trade-offs between the WCET, code size, and energy consumption at compile time.

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Acknowledgment

This work received funding from Deutsche Forschungsgemeinschaft (DFG) under grant FA 1017/3-2.

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Authors and Affiliations

  1. Hamburg University of Technology, Hamburg, Germany

    Kateryna Muts & Heiko Falk

Authors
  1. Kateryna Muts
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  2. Heiko Falk
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Corresponding author

Correspondence to Kateryna Muts .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Muts, K., Falk, H. (2022). Predicting Worst-Case Execution Times During Multi-criterial Function Inlining. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-95467-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95466-6

  • Online ISBN: 978-3-030-95467-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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