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Description
  • Graph is way of structured data representation that is used for solving of many today’s practical problems, e.g. bioinformatics, data mining or social networks. Big graphs, however, require high computing power, which current CPUs can’t provide. But GPU is a high performance, extreme optimized, low cost platform. The article deals with Fruchterman-Reingold graph; how its layout algorithm can be parallelized for GPU using nVidia CUDA computing model. A comparison of execution time on CPU and GPU for processing of large randomly generated graphs is presented.
  • Graph is way of structured data representation that is used for solving of many today’s practical problems, e.g. bioinformatics, data mining or social networks. Big graphs, however, require high computing power, which current CPUs can’t provide. But GPU is a high performance, extreme optimized, low cost platform. The article deals with Fruchterman-Reingold graph; how its layout algorithm can be parallelized for GPU using nVidia CUDA computing model. A comparison of execution time on CPU and GPU for processing of large randomly generated graphs is presented. (en)
Title
  • Graph Visualization Performed by nVidia CUDA Platform
  • Graph Visualization Performed by nVidia CUDA Platform (en)
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  • Graph Visualization Performed by nVidia CUDA Platform
  • Graph Visualization Performed by nVidia CUDA Platform (en)
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  • RIV/62690094:18450/14:50002857!RIV15-MSM-18450___
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  • 18571
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  • RIV/62690094:18450/14:50002857
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  • Parallelization; Graph layout; GPU computing; CUDA (en)
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  • [0576614C8F79]
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  • Limassol
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  • Nicosia
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  • Knowledge, information and creativity support systems (KICSS 2014)
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  • Slabý, Antonín
  • Klapka, Ondřej
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • University of Cyprus
https://schema.org/isbn
  • 978-9963-700-84-4
http://localhost/t...ganizacniJednotka
  • 18450
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