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Automated selective caching for reference attribute grammars

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Author

Editor

  • Brian Malloy
  • Steffen Staab
  • Mark van den Brand

Summary, in English

Reference attribute grammars (RAGs) can be used to express semantics as super-imposed graphs on top of abstract syntax trees (ASTs). A RAG-based AST can be used as the in-memory model providing semantic information for software language tools such as compilers, refactoring tools, and meta-

modeling tools. RAG performance is based on dynamic attribute evaluation with caching. Caching all attributes gives optimal performance in the sense that each attribute is evaluated at most once. However, performance can be further improved by a selective caching strategy, avoiding caching overhead where it does not pay off. In this paper we present a profiling-based technique for automatically finding a good caching configuration. The technique has been evaluated on a generated Java compiler, compiling programs from the Jacks test suite and the DaCapo benchmark suite.

Publishing year

2011

Language

English

Pages

2-21

Publication/Series

Lecture Notes in Computer Science

Volume

6563

Document type

Conference paper

Publisher

Springer

Topic

  • Computer Science

Conference name

SLE'10: 3rd International Conference on Software Language Engineering

Conference date

2010-10-12

Status

Published

Project

  • Embedded Applications Software Engineering

ISBN/ISSN/Other

  • ISBN: 978-3-642-19439-9