Modular average case analysis: Language implementation and extension

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dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNot applicableen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorSchellekens, Michel P.en
dc.contributor.authorGao, Ang
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2013-04-27T14:28:41Z
dc.date.available2013-04-27T14:28:41Z
dc.date.issued2013
dc.date.submitted2013
dc.description.abstractMotivated by accurate average-case analysis, MOdular Quantitative Analysis (MOQA) is developed at the Centre for Efficiency Oriented Languages (CEOL). In essence, MOQA allows the programmer to determine the average running time of a broad class of programmes directly from the code in a (semi-)automated way. The MOQA approach has the property of randomness preservation which means that applying any operation to a random structure, results in an output isomorphic to one or more random structures, which is key to systematic timing. Based on original MOQA research, we discuss the design and implementation of a new domain specific scripting language based on randomness preserving operations and random structures. It is designed to facilitate compositional timing by systematically tracking the distributions of inputs and outputs. The notion of a labelled partial order (LPO) is the basic data type in the language. The programmer uses built-in MOQA operations together with restricted control flow statements to design MOQA programs. This MOQA language is formally specified both syntactically and semantically in this thesis. A practical language interpreter implementation is provided and discussed. By analysing new algorithms and data restructuring operations, we demonstrate the wide applicability of the MOQA approach. Also we extend MOQA theory to a number of other domains besides average-case analysis. We show the strong connection between MOQA and parallel computing, reversible computing and data entropy analysis.en
dc.description.sponsorshipScience Foundation Ireland (07/IN.1/I977)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGao, A. 2013. Modular average case analysis: Language implementation and extension. PhD Thesis, University College Cork.en
dc.identifier.endpage240
dc.identifier.urihttps://hdl.handle.net/10468/1089
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2013, Ang Gaoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectModule average case analysisen
dc.subjectRandomness preservationen
dc.subjectLanguage designen
dc.subjectData structures and algorithm analysisen
dc.subjectParallel computingen
dc.subject.lcshParallel processing (Electronic computers)en
dc.subject.lcshSoftware engineering.en
dc.thesis.opt-outFALSE*
dc.titleModular average case analysis: Language implementation and extensionen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisorm.schellekens@cs.ucc.ie*
ucc.workflow.supervisorm.schellekens@cs.ucc.ie*
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