Multi-walk Parallel Pattern Search Approach on a GPU Computing Platform

  • Weihang Zhu
  • James Curry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5544)

Abstract

This paper studies the efficiency of using Pattern Search (PS) on bound constrained optimization functions on a Graphics Processing Unit (GPU) computing platform. Pattern Search is a direct search optimization technique that does not require derivative information on non-linear programming problems. Pattern Search is ideally suited to a GPU computing environment due to its low memory requirement and no communication between threads in a multi-walk setting. To adapt to a GPU environment, traditional Pattern Search is modified by terminating based on iterations instead of tolerance. This research designed and implemented a multi-walk Pattern Search algorithm on a GPU computing platform. Computational results are promising with a computing speedup of 100+ compared to a corresponding implementation on a single CPU.

Keywords

Nonlinear Optimization Pattern Search GPU CUDA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Weihang Zhu
    • 1
  • James Curry
    • 1
  1. 1.Department of Industrial EngineeirngLamar UniversityBeaumontUSA

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