ITR/SY: A New Framework For Program Optimization

ITR/SY：程序优化的新框架

• 批准号: 0121401
• 负责人: David Padua
• 金额: $ 180万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2005-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121401&HistoricalAwards=false
• 关键词: ITR; SY; New; Framework; Program

The objective of this project is to develop a methodology to design the optimization component of a compiler that learns from experience. The strategy to be studied involves the use of an explanation-based learning (EBL) sub-system that, based on analytical and empirical information, will generate policies to control the optimization component of the compiler. The analytical information will relate program characteristics to performance. The empirical information will be obtained by profiling the program and will be stored in a database containing information from earlier versions of the program and from other programs in the same problem domain.An experimental compiler will be implemented to evaluate the methodolog,. The core of the compiler will be a translator controlled by parameters that could be selected from a standard collection, in which case the compiler will behave like a conventional compiler, or be generated by the machine learning sub-system.Specific topics to be studied as part of this project include: compiler organization, program transformations and their interaction, performance prediction based on both static and dynamic information, machine learning techniques and the integration of both prior knowledge (performance abstractions in our case) and empirical data, and context-adaptive computing systems.

该项目的目标是开发一种方法来设计从经验中学习的编译器的优化组件。 要研究的策略涉及使用基于解释的学习（EBL）子系统，该子系统基于分析和经验信息，将生成策略来控制编译器的优化组件。分析信息将程序特征与性能联系起来。经验信息将通过分析程序获得，并将存储在包含来自程序的早期版本和同一问题域中的其他程序的信息的数据库中。将实施实验编译器来评估方法。编译器的核心将是一个由可以从标准集合中选择的参数控制的翻译器，在这种情况下，编译器将像传统编译器一样运行，或者由机器学习子系统生成。要研究的具体主题为该项目的一部分包括：编译器组织、程序转换及其交互、基于静态和动态信息的性能预测、机器学习技术以及先验知识（在我们的例子中是性能抽象）和经验数据的集成，以及上下文自适应计算系统。