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Automatic Tuning of Compilers Using Machine Learning pp 1–22Cite as

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Abstract

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. The techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to use) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This chapter summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (i) selecting the best optimizations and (ii) the phase-ordering of optimizations. The chapter highlights the approaches taken, the obtained results, the holistic comparisons among different approaches and finally, the visionary path towards the near future.

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Notes

  1. 1.

    https://gcc.gnu.org/onlinedocs/gcc/Option-Summary.html.

  2. 2.

    http://llvm.org/docs/Passes.html.

  3. 3.

    The phase-ordering problem does not have a deterministic upper-bound when we have an unbounded optimization length.

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Authors and Affiliations

  1. Edward S. Rogers Sr. Department of Electrical and Computer Engineering (ECE), University of Toronto, Toronto, ON, Canada

    Amir H. Ashouri

  2. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

    Gianluca Palermo & Cristina Silvano

  3. Computer and Information Sciences (CIS), University of Delaware, Newark, DE, USA

    John Cavazos

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  1. Amir H. Ashouri
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Correspondence to Amir H. Ashouri .

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Ashouri, A.H., Palermo, G., Cavazos, J., Silvano, C. (2018). Background. In: Automatic Tuning of Compilers Using Machine Learning. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-71489-9_1

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