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Optimization of parallel iterated local search algorithms on graphics processing unit

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Abstract

Local search metaheuristics (LSMs) are efficient methods for solving hard optimization problems in science, engineering, economics and technology. By using LSMs, we could obtain satisfactory resolution (approximate optimum) in a reasonable time. However, it is still very CPU time-consuming when solving large problem instances. As graphic process units (GPUs) have been evolved to support general purpose computing, they are taken as a major accelerator in scientific and industrial computing. In this paper, we present an optimized parallel iterated local search algorithm efficiently accelerated on GPUs and test the algorithm with a typical case study of the Travelling Salesman Problem (TSP) in computational science. We introduce novel methods as follows: first, we present an efficient mapping between a neighborhood and a GPU thread. Second, we use the Roofline model to analyze the performance of existing GPU-based 2-opt kernels. Based on our analysis, we point out the limiting factor of these 2-opt kernels and provide our optimization approaches. Furthermore, we test our algorithm with standard TSP problem instances up to 4461 cities, in which our strategy leads to a speedup factor 279\(\times \) over the sequential counterpart. We compare our approach with existing high-performance GPU-based local search algorithms, and the results demonstrate that the proposed algorithm is competitive.

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Notes

  1. http://olab.is.s.u-tokyo.ac.jp/~kamil.rocki/logo-tsp-src_v_0_62.zip.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This paper is supported by the National Science Foundation of China (Grant No. 61472289) and Hubei Province Science Foundation (Grant No. 2015CFB254).

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Yi Zhou & Fazhi He

  2. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Yimin Qiu

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  1. Yi Zhou
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Correspondence to Fazhi He.

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Zhou, Y., He, F. & Qiu, Y. Optimization of parallel iterated local search algorithms on graphics processing unit. J Supercomput 72, 2394–2416 (2016). https://doi.org/10.1007/s11227-016-1738-3

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