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Static Detection of Place Locality and Elimination of Runtime Checks

  • Shivali Agarwal
  • RajKishore Barik
  • V. Krishna Nandivada
  • Rudrapatna K. Shyamasundar
  • Pradeep Varma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5356)

Abstract

Harnessing parallelism particularly for high performance computing is a demanding topic of research. Limitations and complexities of automatic parallelization have led to programming language notations wherein a user programs parallelism explicitly and partitions a global address space for harnessing parallelism. X10 from IBM uses the notion of places to partition the global address space. The model of computation for such languages involves threads and data distributed over local and remote places. A computation is said to be place local if all the threads and data pertaining to it are at the same place. Analysis and optimizations targeting derivations of place-locality have recently gained ground with the advent of partitioned global address space (PGAS) languages like UPC and X10, wherein efficiency of place local accesses is performance critical.

In this paper, we present a novel framework for statically establishing place locality in X10. The analysis framework is based on a static abstraction of activities (threads) incorporating places and an extension to classical escape analysis to track the abstract-activities to which an object can escape. Using this framework, we describe an algorithm that eliminates runtime checks that are inserted by the X10 compiler to enforce place locality of data access. We also identify place locality checks that are guaranteed to fail. Our framework takes advantage of the high level abstraction of X10 distributions to reason about place locality of array accesses in loops as well. The underlying issues, the framework and its power are illustrated through a series of examples.

Keywords

Place Locality Program Point Index Expression Abstract Place Array Access 
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 2008

Authors and Affiliations

  • Shivali Agarwal
    • 1
  • RajKishore Barik
    • 1
  • V. Krishna Nandivada
    • 1
  • Rudrapatna K. Shyamasundar
    • 1
  • Pradeep Varma
    • 1
  1. 1.IBM India Research LabNew DelhiIndia

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