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HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs.

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Titel: HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs.
Autoren: Weijie Zhou, Yue Zhao, Guoqiang Zhang, Xipeng Shen
Quelle: ICSE: International Conference on Software Engineering; 6/17/2020, p506-517, 12p
Schlagwörter: PYTHON programming language, MACHINE learning, DATA analytics, SOFTWARE engineering, COMPUTER software development
Abstract: Modern machine learning programs are often written in Python, with the main computations specified through calls to some highly optimized libraries (e.g., TensorFlow, PyTorch). How to maximize the computing efficiency of such programs is essential for many application domains, which has drawn lots of recent attention. This work points out a common limitation in existing efforts: they focus their views only on the static computation graphs specified by library APIs, but leave the influence from the hosting Python code largely unconsidered. The limitation often causes them to miss the big picture and hence many important optimization opportunities. This work proposes a new approach named HARP to address the problem. HARP enables holistic analysis that spans across computation graphs and their hosting Python code. HARP achieves it through a set of novel techniques: analytics-conscious speculative analysis to circumvent Python complexities, a unified representation augmented computation graphs to capture all dimensions of knowledge related with the holistic analysis, and conditioned feedback mechanism to allow risk-controlled aggressive analysis. Refactoring based on HARP gives 1.3-3X and 2.07X average speedups on a set of TensorFlow and PyTorch programs. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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: HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs.
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  Data: ICSE: International Conference on Software Engineering; 6/17/2020, p506-517, 12p
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  Data: <searchLink fieldCode="DE" term="%22PYTHON+programming+language%22">PYTHON programming language</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+analytics%22">DATA analytics</searchLink><br /><searchLink fieldCode="DE" term="%22SOFTWARE+engineering%22">SOFTWARE engineering</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+software+development%22">COMPUTER software development</searchLink>
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  Data: Modern machine learning programs are often written in Python, with the main computations specified through calls to some highly optimized libraries (e.g., TensorFlow, PyTorch). How to maximize the computing efficiency of such programs is essential for many application domains, which has drawn lots of recent attention. This work points out a common limitation in existing efforts: they focus their views only on the static computation graphs specified by library APIs, but leave the influence from the hosting Python code largely unconsidered. The limitation often causes them to miss the big picture and hence many important optimization opportunities. This work proposes a new approach named HARP to address the problem. HARP enables holistic analysis that spans across computation graphs and their hosting Python code. HARP achieves it through a set of novel techniques: analytics-conscious speculative analysis to circumvent Python complexities, a unified representation augmented computation graphs to capture all dimensions of knowledge related with the holistic analysis, and conditioned feedback mechanism to allow risk-controlled aggressive analysis. Refactoring based on HARP gives 1.3-3X and 2.07X average speedups on a set of TensorFlow and PyTorch programs. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1145/3377811.3380434
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 506
    Subjects:
      – SubjectFull: PYTHON programming language
        Type: general
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: DATA analytics
        Type: general
      – SubjectFull: SOFTWARE engineering
        Type: general
      – SubjectFull: COMPUTER software development
        Type: general
    Titles:
      – TitleFull: HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs.
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            NameFull: Weijie Zhou
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            NameFull: Yue Zhao
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            NameFull: Guoqiang Zhang
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            NameFull: Xipeng Shen
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          Dates:
            – D: 17
              M: 06
              Text: 6/17/2020
              Type: published
              Y: 2020
          Titles:
            – TitleFull: ICSE: International Conference on Software Engineering
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