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  • We present a GPGPU (General-Purpose computing on Graphics Processing Units) based approach to combinatorial problems, specifically to the permutation flow shop scheduling problem. Presented genetic algorithm uses the homogeneous computing model in order to maximize the algorithm performance. The algorithm is based on work presented by Pospichal et al. In our paper, we suggest several improvements (e.g. another mechanism for individuals migration, more efficient problem representation) allowing to apply this algorithm on a wider group of NP-hard combinatorial problems. Our experimental results show significant speedup with respect to the sequential algorithm version.
  • We present a GPGPU (General-Purpose computing on Graphics Processing Units) based approach to combinatorial problems, specifically to the permutation flow shop scheduling problem. Presented genetic algorithm uses the homogeneous computing model in order to maximize the algorithm performance. The algorithm is based on work presented by Pospichal et al. In our paper, we suggest several improvements (e.g. another mechanism for individuals migration, more efficient problem representation) allowing to apply this algorithm on a wider group of NP-hard combinatorial problems. Our experimental results show significant speedup with respect to the sequential algorithm version. (en)
Title
  • Accelerating a Flow Shop Algorithm on the GPU
  • Accelerating a Flow Shop Algorithm on the GPU (en)
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  • Accelerating a Flow Shop Algorithm on the GPU
  • Accelerating a Flow Shop Algorithm on the GPU (en)
skos:notation
  • RIV/68407700:21230/11:00181903!RIV12-MSM-21230___
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  • P(GPP103/10/P401), Z(MSM6840770038)
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  • RIV/68407700:21230/11:00181903
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  • GPU; Flow shop; CUDA; Scheduling; Combinatorial optimization (en)
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  • [6E4E89E57001]
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  • Zajíček, Tomáš
  • Šůcha, Přemysl
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  • 21230
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