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Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

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Abstract

General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs) designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, and the same is the case with conventional LS algorithms. In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs. We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures. We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we obtain a speedup of 25×. We also discuss the effects of different optimization techniques on the overall execution time.

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Notes

  1. Occupancy is the ratio of active warps to the maximum number of warps supported on a multiprocessor of the GPU, and is helpful in determining how efficient the kernel will be on the GPU.

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Acknowledgments

We would like to thank Dalila Boughachi for her help, regarding the use of genetic algorithms to solve MAX-SAT problem. We would also like to thank the anonymous users who replied to our queries regarding CUDA and GPGPU.

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Authors and Affiliations

  1. Graduate School of Information Science & Technology, Hokkaido University, Sapporo, Japan

    Asim Munawar & Mohamed Wahib

  2. Information Initiative Center, Hokkaido University, Sapporo, Japan

    Masaharu Munetomo & Kiyoshi Akama

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  1. Asim Munawar
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  2. Mohamed Wahib
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  3. Masaharu Munetomo
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  4. Kiyoshi Akama
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Correspondence to Asim Munawar.

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Munawar, A., Wahib, M., Munetomo, M. et al. Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework. Genet Program Evolvable Mach 10, 391–415 (2009). https://doi.org/10.1007/s10710-009-9091-4

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  • DOI: https://doi.org/10.1007/s10710-009-9091-4

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