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Implementing the Global Cellular Automata on CUDA

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dc.contributor.authorMilde, Benjamin
dc.contributor.authorBuescher, Niklas
dc.contributor.authorGoesele, Michael
dc.date.accessioned2017-12-06T09:06:19Z
dc.date.available2017-12-06T09:06:19Z
dc.date.issued2011
dc.description.abstractThe Global Cellular Automata (GCA) is a generalization of the Cellular Automata. As a massively parallel model, it is used to describe complex systems and algorithms in a coherent way. In this work, we evaluated how the generic GCA model transfers to NVIDIA's CUDA architecture on GPUs using two exemplary GCA algorithms. We compared our CUDA implementations with parallel CPU implementations and a fast and optimized 32-pipeline FPGA implementation. We obtained more than one order of magnitude in performance gain compared to standard CPUs, while our system also compared favourably to the FPGA implementations, showing that the GCA model fits well to current graphics cards using CUDA.en
dc.identifier.doi10.1007/BF03341982
dc.identifier.pissn0177-0454
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/8591
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofPARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 28, No. 1
dc.relation.ispartofseriesPARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware
dc.subjectCellular Automaton
dc.subjectCellular Automaton
dc.subjectGlobal Memory
dc.subjectGraphic Card
dc.subjectCellular Automaton Model
dc.titleImplementing the Global Cellular Automata on CUDAen
dc.typeText/Journal Article
gi.citation.endPage37
gi.citation.startPage28

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