属性语法的最佳划分算法

Wuu Yang
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引用次数: 4

摘要

语法树的属性依赖图可能被分割成不相交的区域。不同区域中的属性实例彼此独立。属性依赖图划分的优点在于从概念上简化了属性语法,并允许并行求值。提出了一种属性语法的静态划分算法。该算法通过分析语法为每个产品构建所有可行分区的集合。构造了属性语法树之后,为语法树中的每个生产实例选择一个可行分区。将各个生产实例的选定分区粘合在一起,生成语法树的属性依赖图的分区。在评估时不需要进一步合并或分区。除了静态划分外,该算法总是对每个属性依赖图产生最优的划分。划分技术的一个应用是对不包含高阶函数的简单编程语言进行严格性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A finest partitioning algorithm for attribute grammars

The attribute dependence graph of a syntax tree may be partitioned into disjoint regions. Attribute instances in different regions are independent of one other. The advantages of partitioning the attribute dependence graph include simplifying the attribute grammar conceptually and allowing the possibility of parallel evaluation. We present a static partitioning algorithm for attribute grammars. The algorithm builds the set of all feasible partitions for every production by analyzing the grammar. After the attributed syntax tree is constructed, one of the feasible partitions is chosen for each production instance in the syntax tree. Gluing together the selected partitions for individual production instances results in a partition of the attribute dependence graph of the syntax tree. No further merging or partitioning is needed at evaluation time. In addition to static partitioning, the algorithm always produces the finest partition of every attribute dependence graph. An application of the partitioning technique is the strictness analysis for a simple programming language that contains no higher-order functions.

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