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Data Flow Analysis Driven Dynamic Data Partitioning

  • Jodi Tims
  • Rajiv Gupta
  • Mary Lou Soffa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1511)

Abstract

The use of distributed memory architectures as an effective approach to parallel computing brings with it a more complex program development process. Finding a partitioning of program code and data that supports sufficient parallelism without incurring prohibitive communication costs is a challenging and critical step in the development of programs for distributed memory systems. Automatic data distribution techniques have the goal of placing the responsibility of determining a suitable data partitioning into the domain of the compiler. Static program analysis techniques that expose data interrelationships and derive performance estimates are central to the development of automatic data distribution heuristics. In this paper we present a data partitioning heuristic that makes use of array data flow analysis information in the modeling of data interrelationships and the estimation of costs associated with resolving interrelationships via communication. The global view provided by data flow analysis permits consideration of potential communication optimizations before data partitioning decisions are made. Our heuristic uses tiling techniques to determine data partitionings. The resulting data distributions, while still regular, are not limited to the standard BLOCK, CYCLIC and BLOCK-CYCLIC varieties. Preliminary results indicate an overall reduction in communication cost with our technique.

Keywords

Communication Cost Program Point Control Flow Graph Communication Dependence Extreme Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jodi Tims
    • 1
  • Rajiv Gupta
    • 1
  • Mary Lou Soffa
    • 1
  1. 1.Department of Computer ScienceUniversity of PittsburghPittsburgh

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