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一种基于Java虚拟机的动静结合自适应优化方法.

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Title: 一种基于Java虚拟机的动静结合自适应优化方法.
Alternate Title: A static and dynamic adaptive optimization method based on Java virtual machine.
Authors: 张海军1 zhanghjjn@163.com, 郑艳2, 叶俊1, 白书敬1
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Jun2019, Vol. 41 Issue 6, p981-986. 6p.
Subjects: Virtual machine systems, Dylan (Computer program language), Mathematical optimization
Abstract (English): Dynamic language can take advantage of the profiling information at runtime to guide various optimizations o£ the program. However，the existing JAVA virtual machine does not effectively utilize the information collected at runtime，and directly discards it at the end. It re-monitors and collects the information needed for optimization when the program is executed again. We therefore propose a static and dynamic adaptive optimization method based on HotSpot virtual machine，which saves the optimal parameters or information obtained by the optimized object iterative search at runtime into the resource library. It can learn from the resource library to obtain the best parameters or options suitable for the current program，and effectively use the data accumulated at runtime. Resource analysis is static and offline，and does not take up the overhead for running the application. In the process of iterative learning ? the accuracy and efficiency of the resource library learning process are ensured by avoiding redundancy instances to enter the library and removing noise instances from the library. Experiments show that the proposal is practical in guiding the adaptive optimization for Java virtual machine on different platforms. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 态语言可以利用程序运行时获取的动态信息，指导程序进行各种优化。但是，现有的Java 虚拟机没有将运行过程中收集的信息有效利用，而是在运行结束后直接去弃，下一次执行程序的时候重新 监测、收集、优化需要的信息。基于HotSpot虚拟机提出一种动静结合的自适应优化方法，将运行过程中 优化对象迭代搜索到的最佳参数或者信息保存到资源库中, 能够从资源库中学习获得适合当前程序的最 佳参数或选项，可有效地利用运行过程中积累的数据广资源分析是静态且离线的，不占用应用程序运行的 开销, 迭代学习的过程中，通过避免冗余实例入库以及从库中剔除噪声实例，保证资源库学习过程的精度 与效率。实验表明，该框架对指导Java虚拟机在不同的平台上自适应优化具有一定的实用性. [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: 一种基于Java虚拟机的动静结合自适应优化方法.
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  Data: Dynamic language can take advantage of the profiling information at runtime to guide various optimizations o£ the program. However，the existing JAVA virtual machine does not effectively utilize the information collected at runtime，and directly discards it at the end. It re-monitors and collects the information needed for optimization when the program is executed again. We therefore propose a static and dynamic adaptive optimization method based on HotSpot virtual machine，which saves the optimal parameters or information obtained by the optimized object iterative search at runtime into the resource library. It can learn from the resource library to obtain the best parameters or options suitable for the current program，and effectively use the data accumulated at runtime. Resource analysis is static and offline，and does not take up the overhead for running the application. In the process of iterative learning ? the accuracy and efficiency of the resource library learning process are ensured by avoiding redundancy instances to enter the library and removing noise instances from the library. Experiments show that the proposal is practical in guiding the adaptive optimization for Java virtual machine on different platforms. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 态语言可以利用程序运行时获取的动态信息，指导程序进行各种优化。但是，现有的Java 虚拟机没有将运行过程中收集的信息有效利用，而是在运行结束后直接去弃，下一次执行程序的时候重新 监测、收集、优化需要的信息。基于HotSpot虚拟机提出一种动静结合的自适应优化方法，将运行过程中 优化对象迭代搜索到的最佳参数或者信息保存到资源库中, 能够从资源库中学习获得适合当前程序的最 佳参数或选项，可有效地利用运行过程中积累的数据广资源分析是静态且离线的，不占用应用程序运行的 开销, 迭代学习的过程中，通过避免冗余实例入库以及从库中剔除噪声实例，保证资源库学习过程的精度 与效率。实验表明，该框架对指导Java虚拟机在不同的平台上自适应优化具有一定的实用性. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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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        Value: 10.3969/j.issn.1007-130X.2019.06.004
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      – Code: eng
        Text: English
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        PageCount: 6
        StartPage: 981
    Subjects:
      – SubjectFull: Virtual machine systems
        Type: general
      – SubjectFull: Dylan (Computer program language)
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      – SubjectFull: Mathematical optimization
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      – TitleFull: 一种基于Java虚拟机的动静结合自适应优化方法.
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            NameFull: 张海军
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            NameFull: 郑艳
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              Text: Jun2019
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