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  • Differential evolution is an efficient meta-heuristic optimization method with solid record of real world applications. In this paper, we present a simple and efficient implementation of the differential evolution using the massively parallel CUDA architecture. We demonstrate the speedup and improvements obtained by the parallelization of this intelligent algorithm on the problem of scheduling of independent tasks in heterogeneous environments.
  • Differential evolution is an efficient meta-heuristic optimization method with solid record of real world applications. In this paper, we present a simple and efficient implementation of the differential evolution using the massively parallel CUDA architecture. We demonstrate the speedup and improvements obtained by the parallelization of this intelligent algorithm on the problem of scheduling of independent tasks in heterogeneous environments. (en)
Title
  • An implementation of differential evolution for independent tasks scheduling on GPU
  • An implementation of differential evolution for independent tasks scheduling on GPU (en)
skos:prefLabel
  • An implementation of differential evolution for independent tasks scheduling on GPU
  • An implementation of differential evolution for independent tasks scheduling on GPU (en)
skos:notation
  • RIV/61989100:27240/11:86080819!RIV12-MPO-27240___
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  • P(FR-TI1/420)
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  • PART 1
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  • 185482
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  • RIV/61989100:27240/11:86080819
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  • CUDA; differential evolution (en)
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  • DE - Spolková republika Německo
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  • [FB326E3FB1B7]
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  • Lecture Notes in Computer Science-Computational Science
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  • 6678
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  • Abraham Padath, Ajith
  • Krömer, Pavel
  • Platoš, Jan
  • Snášel, Václav
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-21219-2_47
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  • 27240
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