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A GPU parallelization of branch-and-bound for multiproduct batch plants optimization

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Abstract

Branch-and-bound (B&B) is a popular approach to accelerate the solution of the optimization problems, but its parallelization on graphics processing units (GPUs) is challenging because of B&B’s irregular data structures and poor computation/communication ratio. The contributions of this paper are as follows: (1) we develop two CUDA-based implementations (iterative and recursive) of B&B on systems with GPUs for a practical application scenario—optimal design of multi-product batch plants, with a particular example of a chemical-engineering system (CES); (2) we propose and implement several optimizations of our CUDA code by reducing branch divergence and by exploiting the properties of the GPU memory hierarchy; and(3) we evaluate our implementations and their optimizations on a modern GPU-based system and we report our experimental results.

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Authors and Affiliations

  1. Tambov State Technical University, Tambov, Russia

    Andrey Borisenko

  2. University of Muenster, Münster, Germany

    Michael Haidl & Sergei Gorlatch

Authors
  1. Andrey Borisenko
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  2. Michael Haidl
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  3. Sergei Gorlatch
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Corresponding author

Correspondence to Andrey Borisenko.

Additional information

This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG), Cells-in-Motion Cluster of Excellence (EXC 1003-CiM), University of Muenster, Germany. Andrey Borisenko was supported by the DAAD (German Academic Exchange Service) and by the Ministry of Education and Science of the Russian Federation under the ”Mikhail Lomonosov II”-Programme.

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Borisenko, A., Haidl, M. & Gorlatch, S. A GPU parallelization of branch-and-bound for multiproduct batch plants optimization. J Supercomput 73, 639–651 (2017). https://doi.org/10.1007/s11227-016-1784-x

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  • DOI: https://doi.org/10.1007/s11227-016-1784-x

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