Abstract
Parallel implementation of the Branch-and-Bound (B&B) technique for optimization problems is a promising approach to accelerating their solution, but it remains challenging on Graphics Processing Units (GPUs) due to B&B’s irregular data structures and poor computation/communication ratio. The contributions of this paper are as follows: (1) we develop two basic implementations (iterative and recursive) of B&B on systems with GPUs for a practical application scenario - optimal design of multi-product batch plants; (2) we propose and implement several optimizations of our CUDA code using both algorithmic techniques of reducing branch divergence and GPU-specific properties of the memory hierarchy; and (3) we evaluate our implementations and optimizations on a modern GPU-based system and we report our experimental results.
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References
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Acknowledgement
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. (2015). Parallelizing Branch-and-Bound on GPUs for Optimization of Multiproduct Batch Plants. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2015. Lecture Notes in Computer Science(), vol 9251. Springer, Cham. https://doi.org/10.1007/978-3-319-21909-7_33
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DOI: https://doi.org/10.1007/978-3-319-21909-7_33
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