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Grid Task Scheduling Algorithm R3Q for Evolving Artificial Neural Networks

  • Yoshiyuki Matsumura
  • Masashi Oiso
  • Kazuhiro Ohkura
  • Noriyuki Fujimoto
  • Kenichi Hagihara
  • Jeremy Wyatt
  • Xin Yao
Conference paper

Abstract

Task scheduling algorithms for evolving artificial neural networks (EANNs) in grid computing environments is discussed. In this paper, list scheduling with round-robin order replication (RR) is adopted to reduce waiting times due to synchronization. However, RR is suitable for coarse-grained tasks. For EANNs as medium-grained tasks, we propose a new technique to reduce the communication overhead, called the remote work queue (RWQ) method. We then define round-robin replication remote work queue (R3Q) as RWQ with RR. Our results show that R3Q can reduce both the synchronous waiting time and communication time %, and provides efficient forced termination of tasks compared to other methods.

Keywords

Schedule Algorithm Recurrent Neural Network Grid Environment Total Execution Time List Schedule 
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

  • Yoshiyuki Matsumura
    • 1
  • Masashi Oiso
  • Kazuhiro Ohkura
  • Noriyuki Fujimoto
  • Kenichi Hagihara
  • Jeremy Wyatt
  • Xin Yao
  1. 1.Shinshu UniversityUedaJapan

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