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基于源代码迁移的编译器优化方法研究.

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Title: 基于源代码迁移的编译器优化方法研究.
Alternate Title: Research on compiler optimization methods based on source code migration.
Authors: 周 放1,2 735672985@qq.com, 刘茂福1,2, 李珊枝3
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Oct2025, Vol. 47 Issue 10, p1890-1900. 11p.
Subjects: Deep learning, Compilers (Computer programs), Mathematical optimization, Program transformation, Flowgraphs
Abstract (English): Compiler optimization aims to enhance the efficiency of code execution on target platforms by applying a series of transformations to the intermediate representation (IR) language. Traditional methods typically rely on machine learning to analyze IR features and predict the optimal combination of LLVM compiler optimization passes. However, these methods are limited by their reliance on existing compiler optimization strategies and insufficient use of global information, which limits their scalability. This study adopts deep learning to automatically translate function-level IR from an unoptimized state to the O2 optimization level, treating this optimization process as a translation task. By integrating a dense data flow graph (DDFG), this method is able to extract the global structural information from the IR code, thereby guiding the model to learn code semantics more comprehensively. Experiments using the Transformer model demonstrate that this method can effectively train IR at the O2 level, and 86.15% of the function-level optimized code can execute correctly on the compiler while ensuring semantic integrity. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 编译器优化旨在通过在中间代码IR语言上进行一系列变换,提高代码在目标平台上的运行 效率。传统方法通常依赖机器学习来分析IR特征,并预测LLVM 编译器优化通道的最佳组合。然而,这 些方法因受限于编译器现有优化策略和对全局信息的有限利用,其扩展性受限。采用深度学习自动将函 数级IR从未优化状态转换至O2级别优化,并将此优化过程视为翻译任务。通过引入密集数据流图 DDFG,能够提取IR代码的全局结构信息,从而引导模型更全面地学习代码语义。使用Transformer模 型进行的实验表明,所提方法的模型能在O2级别有效训练IR,且86.15%的函数级优化代码能在保证语 义完整性的同时,在编译器上正确执行。 [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 基于源代码迁移的编译器优化方法研究.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Compilers+%28Computer+programs%29%22">Compilers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Program+transformation%22">Program transformation</searchLink><br /><searchLink fieldCode="DE" term="%22Flowgraphs%22">Flowgraphs</searchLink>
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  Data: Compiler optimization aims to enhance the efficiency of code execution on target platforms by applying a series of transformations to the intermediate representation (IR) language. Traditional methods typically rely on machine learning to analyze IR features and predict the optimal combination of LLVM compiler optimization passes. However, these methods are limited by their reliance on existing compiler optimization strategies and insufficient use of global information, which limits their scalability. This study adopts deep learning to automatically translate function-level IR from an unoptimized state to the O2 optimization level, treating this optimization process as a translation task. By integrating a dense data flow graph (DDFG), this method is able to extract the global structural information from the IR code, thereby guiding the model to learn code semantics more comprehensively. Experiments using the Transformer model demonstrate that this method can effectively train IR at the O2 level, and 86.15% of the function-level optimized code can execute correctly on the compiler while ensuring semantic integrity. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 编译器优化旨在通过在中间代码IR语言上进行一系列变换,提高代码在目标平台上的运行 效率。传统方法通常依赖机器学习来分析IR特征,并预测LLVM 编译器优化通道的最佳组合。然而,这 些方法因受限于编译器现有优化策略和对全局信息的有限利用,其扩展性受限。采用深度学习自动将函 数级IR从未优化状态转换至O2级别优化,并将此优化过程视为翻译任务。通过引入密集数据流图 DDFG,能够提取IR代码的全局结构信息,从而引导模型更全面地学习代码语义。使用Transformer模 型进行的实验表明,所提方法的模型能在O2级别有效训练IR,且86.15%的函数级优化代码能在保证语 义完整性的同时,在编译器上正确执行。 [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2025.10.017
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1890
    Subjects:
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Compilers (Computer programs)
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Program transformation
        Type: general
      – SubjectFull: Flowgraphs
        Type: general
    Titles:
      – TitleFull: 基于源代码迁移的编译器优化方法研究.
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            NameFull: 周 放
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            NameFull: 刘茂福
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            NameFull: 李珊枝
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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