Facilitating the search for compositions of program transformations

  • Authors:

  • Albert Cohen;Marc Sigler;Sylvain Girbal;Olivier Temam;David Parello;Nicolas Vasilache

  • Affiliations:

  • Paris-Sud University;Paris-Sud University;Paris-Sud University;Paris-Sud University;Paris-Sud University;Paris-Sud University

  • Venue:
  • Proceedings of the 19th annual international conference on Supercomputing
  • Year:
  • 2005

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Abstract

Static compiler optimizations can hardly cope with the complex run-time behavior and hardware components interplay of modern processor architectures. Multiple architectural phenomena occur and interact simultaneously, which requires the optimizer to combine multiple program transformations. Whether these transformations are selected through static analysis and models, runtime feedback, or both, the underlying infrastructure must have the ability to perform long and complex compositions of program transformations in a flexible manner. Existing compilers are ill-equipped to perform that task because of rigid phase ordering, fragile selection rules using pattern matching, and cumbersome expression of loop transformations on syntax trees. Moreover, iterative optimization emerges as a pragmatic and general means to select an optimization strategy via machine learning and operations research. Searching for the composition of dozens of complex, dependent, parameterized transformations is a challenge for iterative approaches.The purpose of this article is threefold: (1) to facilitate the automatic search for compositions of program transformations, introducing a richer framework which improves on classical polyhedral representations, suitable for iterative optimization on a simpler, structured search space, (2) to illustrate, using several examples, that syntactic code representations close to the operational semantics hamper the composition of transformations, and (3) that complex compositions of transformations can be necessary to achieve significant performance benefits. The proposed framework relies on a unified polyhedral representation of loops and statements. The key is to clearly separate four types of actions associated with program transformations: iteration domain, schedule, data layout and memory access functions modifications. The framework is implemented within the Open64/ORC compiler, aiming for native IA64, AMD64 and IA32 code generation, along with source-to-source optimization of Fortran90, C and C++.