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

Weihang Zhu and James Curry
Lamar University, Beaumont, TX, USA
Computational Science – ICCS 2009, Lecture Notes in Computer Science, 2009, Volume 5544/2009, 984-993

@article{zhu2009multi,

   title={Multi-walk parallel pattern search approach on a GPU computing platform},

   author={Zhu, W. and Curry, J.},

   journal={Computational Science–ICCS 2009},

   pages={984–993},

   year={2009},

   publisher={Springer}

}

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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.
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