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Applications of Strong Moding in Concurrent Logic Programming

强模态在并发逻辑编程中的应用

基本信息

批准号：
07680371
负责人：
UEDA Kazunori
金额：
$ 1.54万
依托单位：
Waseda University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1997
项目状态：
已结题

项目摘要

The outstanding features of concurrent logic programming languages such as GHC (Guarded Horn Clauses) are the extreme simplicity of its communication and synchronization mechanisms and its flexibility in describing concurrency. To make the languages really practical, however, static analysis needs to play key roles both in programming and optimization.The head investigator had worked on the theoretical aspects of the static mode system (strong moding) for GHC and designed the algorithms for mode analysis. In this project, we worked on the practical aspects of strong moding and demonstrated its effectiveness from the following respects :1. Implementation of a mode analyzer - We implemented the mode analyzer klint for the concurrent logic language KL1 using KL1 itself as an implementation language. The system was revised several times until it could process large, real-life programs.2. Descriptive power under strong moding - We demonstrated, through the analysis of various real-life Eli programs, that concurrent logic languages with strong moding are as practical and expressive as those without strong moding.3. Program diagnosis based on strong moding - Strong moding turned out to be extremely useful for the static detection of program errors. We designed and implemented a set of algorithms for analyzing the errors of non-well-moded programs efficiently. Furthermore, we proposed a method of automated error correction and showed that it worked quite well for small program errors.4. Optimization based on strong moding - We demonstrated that, by using type and linearity information in addition to mode information, many of runtime operations such as tag checking could be eliminated, leading to performance not too different from that obtained by procedural programs.

并发逻辑编程语言（如GHC（守卫角子句））的突出特性是其通信和同步机制的极端简单性以及描述并发性的灵活性。然而，要使这些语言真正实用，静态分析需要在编程和优化中发挥关键作用。首席研究员研究了GHC静态模态系统（强模态）的理论方面，并设计了模态分析的算法。在本项目中，我们对强建模的实践方面进行了研究，并从以下几个方面论证了强建模的有效性：模式分析器的实现——我们使用KL1本身作为实现语言，为并发逻辑语言KL1实现了模式分析器klint。这个系统被修改了几次，直到它能够处理大型的、现实生活中的程序。强建模下的描述能力——通过对各种现实生活中的Eli程序的分析，我们证明了具有强建模的并发逻辑语言与没有强建模的并发逻辑语言一样实用和富有表现力。基于强建模的程序诊断——强建模对于程序错误的静态检测非常有用。设计并实现了一套有效分析非良态程序误差的算法。此外，我们提出了一种自动纠错的方法，并表明它对小程序错误的处理非常有效。基于强建模的优化——我们证明，除了模式信息外，通过使用类型和线性信息，可以消除许多运行时操作，如标签检查，从而使性能与过程程序获得的性能相差不大。