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Fine-Grained Task Scheduling Using Adaptive Data Structures

  • Ralf Hoffmann
  • Thomas Rauber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5168)

Abstract

Task pools have been shown to provide efficient load balancing for irregular applications on heterogeneous platforms. Often, distributed data structures are used to store the tasks and the actual load balancing is achieved by task stealing where an idle processor accesses tasks from another processor. In this paper we extent the concept of task pools to adaptive task pools which are able to adapt the number of tasks moved between the processor to the specific execution scenario, thus reducing the overhead for task stealing significantly. We present runtime experiments for different applications on two execution platforms.

Keywords

Synthetic Application Private Area Linear List Idle Processor Task Pool 
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

  • Ralf Hoffmann
    • 1
  • Thomas Rauber
    • 1
  1. 1.Department for Mathematics, Physics and Computer ScienceUniversity of BayreuthGermany

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