Classification and utilization of abstractions for optimization

  • Authors:

  • Dan Quinlan;Markus Schordan;Qing Yi;Andreas Saebjornsen

  • Affiliations:

  • Lawrence Livermore National Laboratory;Vienna University of Technology;Lawrence Livermore National Laboratory;University of Oslo, Norway

  • Venue:
  • ISoLA'04 Proceedings of the First international conference on Leveraging Applications of Formal Methods
  • Year:
  • 2004

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Abstract

We define a novel approach for optimizing the use of libraries within applications. We propose that library-defined abstractions be annotated with additional semantics to support their automated optimization. By leveraging these additional semantics we enable specialized optimizations of application codes which use library abstractions. We believe that such an approach entails the use of formal methods. It is a common perception that performance is inversely proportional to the level of abstraction. Our work shows that this is not the case if the additional semantics of library-defined abstractions can be leveraged. We describe ROSE, a framework for building source-to-source translators that perform high-level optimizations on scientific applications. ROSE allows the recognition of library abstractions and the optimization of their use in applications. We show how ROSE can utilize the semantics of userdefined abstractions in libraries within the compile-time optimization of applications.