Abstract
Many today’s practical problems, e.g. bioinformatics, data mining or social networks can be visualized and better examined and understood in the form of a graph. Elaborating big graphs, however, requires high computing power. The performance of CPUs is not sufficient for this purpose but graphics processing unit (GPU) may serve as a suitable high performance, well optimized and low cost platform for calculations of this kind. The article deals with the Fruchterman-Reingold graph and brings solution to this problem; how its layout algorithm can be parallelized for the GPU using nVidia CUDA computing model. This article is continuation and extension of (Klapka and Slaby, The 9th international conference on knowledge, information and creativity support systems, 2014) [8] and gives some other facts and details.
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Klapka, O., Slaby, A. (2016). nVidia CUDA Platform in Graph Visualization. In: Kunifuji, S., Papadopoulos, G., Skulimowski, A., Kacprzyk , J. (eds) Knowledge, Information and Creativity Support Systems. Advances in Intelligent Systems and Computing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-319-27478-2_38
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DOI: https://doi.org/10.1007/978-3-319-27478-2_38
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