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Fast Evolutionary Algorithms: Comparing High Performance Capabilities of CPUs and GPUs

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dc.contributor.authorHofmann, Johannes
dc.contributor.authorFey, Dietmar
dc.date.accessioned2017-12-06T09:07:45Z
dc.date.available2017-12-06T09:07:45Z
dc.date.issued2013
dc.description.abstractWe use Evolutionary Algorithms (EAs) to evaluate different aspects of high performance computing on CPUs and GPUs. EAs have the distinct property of being made up of parts that behave rather differently from each other, and display different requirements for the underlying hardware as well as software. We can use these motives to answer crucial questions for each platform: How do we make best use of the hardware using manual optimization? Which platform offers the better software libraries to perform standard operations such as sorting? Which platform has the higher net floating-point performance and bandwidth? We draw the conclusion that GPUs are able to outperform CPUs in all categories; thus, considering time-to-solution, EAs should be run on GPUs whenever possible.en
dc.identifier.doi10.1007/BF03354234
dc.identifier.pissn0177-0454
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/8601
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofPARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 30, No. 1
dc.relation.ispartofseriesPARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware
dc.subjectPeak Performance
dc.subjectHardware Thread
dc.subjectStreaming SIMD Extension
dc.subjectLinear Congruential Generator
dc.subjectTheoretical Peak Performance
dc.titleFast Evolutionary Algorithms: Comparing High Performance Capabilities of CPUs and GPUsen
dc.typeText/Journal Article
gi.citation.endPage24
gi.citation.startPage15

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