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dc.contributor.authorGibson, M
dc.contributor.authorKeedwell, Edward
dc.contributor.authorSavić, Dragan
dc.date.accessioned2016-03-31T09:43:50Z
dc.date.issued2013-07-10
dc.description.abstractCellular automata, represented by a discrete set of elements are ideal candidates for parallelisation, particularly on graphics cards using GPGPU technology. This paper shows that the speedups of 50 times over CPU are possible but that the hardware is only partially responsible and the memory model is vital to exploiting the additional computational power of the GPU.en_GB
dc.identifier.citationGECCO '13 Companion Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, pp. 171-172en_GB
dc.identifier.doi10.1145/2464576.2464660
dc.identifier.urihttp://hdl.handle.net/10871/20895
dc.language.isoenen_GB
dc.publisherAssociation for Computing Machinery (ACM)en_GB
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=2464576.2464660en_GB
dc.rightsCopyright is held by the author/owner(s). This is the final version of the article. Available via the DOI in this record.en_GB
dc.subjectCellular Automataen_GB
dc.subjectCAen_GB
dc.subjectGeneral Purpose Graphical Processing Uniten_GB
dc.subjectGPGPUen_GB
dc.subjectOpenCLen_GB
dc.subjectMultiple Data Stream Architectures (Multiprocessors)en_GB
dc.subjectSingle-instruction-stream, multiple-data-stream processors (SIMD)en_GB
dc.titleUnderstanding the efficient parallelisation of Cellular Automata on CPU and GPGPU hardwareen_GB
dc.typeConference paperen_GB
dc.date.available2016-03-31T09:43:50Z
dc.identifier.isbn978-1-4503-1964-5


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