Please use this identifier to cite or link to this item: https://doi.org/10.15480/336.4890
DC FieldValueLanguage
dc.contributor.authorMuts, Kateryna-
dc.date.accessioned2023-01-30T08:22:43Z-
dc.date.available2023-01-30T08:22:43Z-
dc.date.issued2023-01-26-
dc.identifier.urihttp://hdl.handle.net/11420/14664-
dc.description.abstractCompiler-based optimizations are efficient techniques to improve a program to be compiled. Function inlining is a well-known compiler-based optimization that substitutes function calls by the body of the function. The data represent the results of multiobjective function inlining for hard real-time systems with code size, energy consumption and worst-case execution time (WCET) as objectives. Since the analyses of energy consumption and WCET are very time-consuming at compile time, search space reduction and predictions based on machine learning techniques were applied to speed up the multiobjective function inlining at compile time.en
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG)de_DE
dc.language.isoende_DE
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/de_DE
dc.subjectCompiler-based optimizationde_DE
dc.subjectFunction Inliningde_DE
dc.subjectMachine learningde_DE
dc.subjectSearch space reductionde_DE
dc.subjectHard real-time systemde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleMultiobjective code compression and function inlining for hard real-time systemsde_DE
dc.typeDatasetde_DE
dc.identifier.doi10.15480/336.4890-
dc.type.diniResearchData-
dcterms.DCMITypeDataset-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0209776-
tuhh.abstract.englishCompiler-based optimizations are efficient techniques to improve a program to be compiled. Function inlining is a well-known compiler-based optimization that substitutes function calls by the body of the function. The data represent the results of multiobjective function inlining for hard real-time systems with code size, energy consumption and worst-case execution time (WCET) as objectives. Since the analyses of energy consumption and WCET are very time-consuming at compile time, search space reduction and predictions based on machine learning techniques were applied to speed up the multiobjective function inlining at compile time.de_DE
tuhh.publication.instituteEingebettete Systeme E-13de_DE
tuhh.identifier.doi10.15480/336.4890-
tuhh.type.opusDataset-
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
dc.type.driverother-
dc.type.casraiOther-
dc.relation.projectMultikriterielle Code-Optimierung für Eingebettete Harte Echtzeitsystemede_DE
datacite.relation.IsCompiledBydoi:10.1007/s11241-010-9101-x-
datacite.relation.IsSupplementTodoi:10.15480/882.4799-
tuhh.type.rdmtrue-
local.researchdata.deleteaftertenyearsfalsede_DE
datacite.resourceTypeResearchData-
datacite.resourceTypeGeneralDataset-
item.openairecristypehttp://purl.org/coar/resource_type/c_ddb1-
item.languageiso639-1en-
item.creatorGNDMuts, Kateryna-
item.mappedtypeDataset-
item.cerifentitytypeProducts-
item.openairetypeDataset-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.creatorOrcidMuts, Kateryna-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
crisitem.project.grantnoFA 1017/3-1-
crisitem.author.deptEingebettete Systeme E-13-
crisitem.author.orcid0000-0002-2255-3410-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik (E)-
crisitem.funder.funderid501100001659-
crisitem.funder.funderrorid018mejw64-
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