Abstract
Low autocorrelation binary sequence (LABS) remains an open hard optimisation problem that has many applications. One of the promising directions for solving the problem is designing advanced solvers based on local search heuristics. The paper proposes two new heuristics developed from the steepest-descent local search algorithm (SDLS), implemented on the GPGPU architectures. The introduced algorithms utilise the parallel nature of the GPU and provide an effective method of solving the LABS problem. As a means for comparison, the efficiency between SDSL and the new algorithms is presented, showing that exploring the wider neighbourhood improves the results.
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Notes
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The GPGPU implementation of discussed approach is not able to check a large space so as a result it provides the worst results. For that reason the detailed description and analysis of this algorithm will be conducted as a future work.
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The research presented in this paper was realized thanks to funds of Polish Ministry of Science and Higher Education assigned to AGH University of Science and Technology.
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Żurek, D., Piętak, K., Pietroń, M., Kisiel-Dorohinicki, M. (2021). New Variants of SDLS Algorithm for LABS Problem Dedicated to GPGPU Architectures. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_18
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