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Scheduling Dynamic OpenMP Applications over Multicore Architectures

  • François Broquedis
  • François Diakhaté
  • Samuel Thibault
  • Olivier Aumage
  • Raymond Namyst
  • Pierre-André Wacrenier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5004)

Abstract

Approaching the theoretical performance of hierarchical multicore machines requires a very careful distribution of threads and data among the underlying non-uniform architecture in order to minimize cache misses and NUMA penalties. While it is acknowledged that OpenMP can enhance the quality of thread scheduling on such architectures in a portable way, by transmitting precious information about the affinities between threads and data to the underlying runtime system, most OpenMP runtime systems are actually unable to efficiently support highly irregular, massively parallel applications on NUMA machines.

In this paper, we present a thread scheduling policy suited to the execution of OpenMP programs featuring irregular and massive nested parallelism over hierarchical architectures. Our policy enforces a distribution of threads that maximizes the proximity of threads belonging to the same parallel region, and uses a NUMA-aware work stealing strategy when load balancing is needed. It has been developed as a plug-in to the forestGOMP OpenMP platform [TBG+07]. We demonstrate the efficiency of our approach with a highly irregular recursive OpenMP program resulting from the generic parallelization of a surface reconstruction application. We achieve a speedup of 14 on a 16-core machine with no application-level optimization.

Keywords

OpenMP Nested Parallelism Hierarchical Thread Scheduling Bubbles Multi-Core NUMA SMP 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François Broquedis
    • 1
  • François Diakhaté
    • 1
  • Samuel Thibault
    • 1
  • Olivier Aumage
    • 1
  • Raymond Namyst
    • 1
  • Pierre-André Wacrenier
    • 1
  1. 1.INRIA Futurs - LaBRIUniversité Bordeaux 1France

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