ST-CRTS: Collaborative Research: Lifting Compiler Optimizations via Generic Programming

ST-CRTS：协作研究：通过通用编程提升编译器优化

• 批准号: 0541335
• 负责人: Andrew Lumsdaine
• 金额: $ 27.92万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0541335&HistoricalAwards=false
• 关键词: ST; CRTS; Collaborative; Research; Lifting

BackgroundA typical compiler is designed to apply its optimizations to built-in types of the language it is compiling. Although built-in general-purpose optimizations might be generally beneficial, they cannot be directly applied to user-defined types because their correctness cannot be determined a priori. Extending a compiler to optimize some particular userdefined type would be useful for that particular type, but this approach becomes burdensome if the compiler must be extended on a type-by-type basis. A more powerful approach would be to enable compiler optimizations that are generic, i.e., that can be applied to any type (built-in or user-defined) that meets fundamental semantic requirements.Intellectual Merit-Formal concept definitions. The PIs propose to integrate concept-based optimization with their research on formalization of concepts for generic programming.-Language support for concepts. Concept-based optimization will leverage and inform our on-going research on direct language support for concepts and generic programming.-Compiler support for concepts. The PI's initial efforts incorporating concepts into the GNU C++ compiler will be extended to support concept-based optimizations.- Concept-based optimizations. The PI's will leverage generic programming mechanisms to define optimizations using concepts so that such optimizations apply to user-defined types, simply by stating that the types model (i.e., meetthe requirements of) particular concepts.-Deployment and library evolution. Selected generic libraries, including the Standard Template Library, the Boost Graph Library, and the Vector, Signal, and Image Processing Library will be upgraded to take advantage of language extensions for generic programming and to take advantage of concept-based optimization.-Evaluation suite. The PIs will collect optimizations for a suite of high-level benchmarks that can be used to evaluate concept-based optimizations. An important benchmark in this regard will be to demonstrate that generic optimizations offer the same performance benefits when applied to built-in types as do traditional type-specific optimizations.Broader ImpactThe proposed research will have:- Direct impact on the future development of mainstream programming languages that support generic programming (e.g., C++, C#, and Java), and on their standard libraries. -Support for generic programming in these languages that will provide valuable high-level information about user-defined types,making high-level and concept-based optimization more widely applicable. This project will directly train graduate students and post-doctoral researchers in the emerging field of generic programming. Results from our research will be integrated into graduate programming coursesand into textbooks.

一个典型的编译器被设计成将其优化应用于它所编译的语言的内置类型。虽然内置的通用优化通常可能是有益的，但它们不能直接应用于用户定义的类型，因为它们的正确性无法事先确定。扩展编译器以优化某些特定的用户定义类型对于该特定类型是有用的，但是如果编译器必须在逐个类型的基础上进行扩展，则这种方法变得繁琐。一种更强大的方法是启用通用的编译器优化，即，可以应用于任何类型（内置或用户定义），满足基本的语义要求。智力优点正式概念定义。PI建议将基于概念的优化与他们的研究相结合， 泛型编程概念的形式化。对概念的语言支持。基于概念的优化将利用并告知我们正在进行的研究 对概念和泛型编程的直接语言支持。更好地支持概念。PI最初的努力将概念合并到GNU C++编译器中， 支持基于概念的优化。基于概念的优化PI将利用通用编程机制来定义优化 使用概念，以便这种优化应用于用户定义的类型，只需声明类型 模型（即，满足）特殊概念的要求。部署和库的演变。选定的通用库，包括标准模板库、Boost图形库以及矢量、信号和图像处理库将进行升级， 利用泛型编程的语言扩展，并利用基于概念的优化。评估套件。PI将为一套高级基准测试收集优化，这些基准测试可用于评估基于概念的优化。这方面的一个重要基准将是表明， 泛型优化在应用于内置类型时提供与传统优化相同的性能优势。 更广泛的影响拟议的研究将有：-对支持泛型的主流编程语言的未来发展的直接影响 编程（例如，C++、C#和Java）以及它们的标准库。- 支持这些语言中的泛型编程，这将提供有关以下内容的有价值的高级信息： 用户定义的类型，使高级和基于概念的优化更广泛地适用。该项目将直接培养新兴领域的研究生和博士后研究人员， 泛型编程我们的研究成果将被纳入研究生编程课程和教科书。