SCIENTIA SINICA Informationis, Volume 49 , Issue 10 : 1267-1282(2019) https://doi.org/10.1360/N112019-00050

Selection of compiler-optimization sequences

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  • ReceivedMar 2, 2019
  • AcceptedJun 6, 2019
  • PublishedOct 16, 2019


In recent decades, compiler developers have designed and implemented many compiler-optimization options for a variety of complex optimization requirements; however, it is difficult to effectively adapt existing standard compiler-optimization sequences comprising several optimization options to complex compilation requirements. Because programs have different semantics and compilation goals, it is difficult to achieve desirable optimization results by directly employing standard compiler-optimization sequences. Additionally, the evolution of hardware architectures has increased the complexity of compilation environments, requiring compiler-optimization sequences to be adjusted accordingly. Therefore, selection of good optimization sequences for programs remains challenging. To address this, we reviewed 55 studies related to the selection of compiler-optimization sequences and summarized the research status of this area from several perspectives, including algorithmic approaches, research focuses, target compilers, and target benchmarks. The most popular algorithms used in this research area include heuristic search algorithms (e.g., genetic algorithms) and machine-learning algorithms (e.g., support vector machines). Over 80% of the existing studies focused on novel solutions or validation research. Furthermore, the most popular compiler and benchmark suites used for these experiments are GCC and miBench, respectively. This review offers insight into research trends associated with compiler optimization-sequence selection and provides possible directions for future studies in this field.

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  • Figure 1

    The structure of compiler

  • Figure 2

    Procedure of publication analysis

  • Figure 3

    (Color online) The number of publications per year

  • Figure 4

    (Color online) Venues of publications

  • Figure 5

    (Color online) Heuristic search algorithms and objectives

  • Figure 6

    (Color online) Machine learning algorithms and objectives

  • Figure 7

    (Color online) Statistical results of research types

  • Figure 8

    (Color online) Statistical results of target compilers

  • Figure 9

    (Color online) Statistical results of target benchmarks

  • Table 1   Full names of publication venues
    Abbreviation Full name of journals/conferences
    LCTES ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems
    TACO ACM Transactions on Architecture and Code Optimization
    CGO IEEE/ACM International Symposium on Code Generation and Optimization
    SCCC International Conference of the Chilean Computer Science Society
    EDCC European Dependable Computing Conference
    CASES International Conference on Compilers, Architecture, and Synthesis for Embedded Systems
    TJSC The Journal of Supercomputing
    IJPP International Journal of Parallel Programming
  • Table 2   The comparison between heuristic search and machine learning
    Heuristic algorithms Machine learning
    Input Encode Feature representation
    Output Compiler optimization sequence Compiler optimization sequence
    Training set Unnecessary Necessary
    Time consuming Long Short
  • Table 3   Authors and their publication frequencies
    ID Author Frequency
    1 John Cavazos 9
    2 Prasad A. Kulkarni 7
    3 David Whalley 6
    4 Amir Hossein Ashouri 5
    5 Cristina Silvano 5
    6 Eunjung Park 5
    7 Gianluca Palermo 5
    8 Jack Davidson 5
    9 Joao M. P. Cardoso 5
    10 Keith D. Cooper 5
    11 Ricardo Nobre 5

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