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A finest partitioning algorithm for attribute grammars

dc.citation.epage164en_US
dc.citation.issue3en_US
dc.citation.spage145en_US
dc.citation.volume25en_US
dc.citation.woscount0
dc.contributor.authorYang, Wen_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.date.accessioned2014-12-08T15:46:12Z
dc.date.available2014-12-08T15:46:12Z
dc.date.issued1999-10-01en_US
dc.description.abstractThe 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. (C) 2000 Elsevier Science Ltd. All rights reserved.en_US
dc.identifier.issn0096-0551en_US
dc.identifier.journalCOMPUTER LANGUAGESen_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/31074
dc.identifier.wosnumberWOS:000087521000002
dc.language.isoen_USen_US
dc.subjectattribute grammarsen_US
dc.subjectpartitioningen_US
dc.subjectstrictness analysisen_US
dc.subjectparallel evaluationen_US
dc.titleA finest partitioning algorithm for attribute grammarsen_US
dc.typeArticleen_US

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