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dc.contributor.authorArandi, Sameren
dc.contributor.authorMatheou, Georgeen
dc.contributor.authorKyriacou, Costasen
dc.contributor.authorEvripidou, Paraskevasen
dc.creatorArandi, Sameren
dc.creatorMatheou, Georgeen
dc.creatorKyriacou, Costasen
dc.creatorEvripidou, Paraskevasen
dc.date.accessioned2019-11-13T10:38:20Z
dc.date.available2019-11-13T10:38:20Z
dc.date.issued2017
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53574
dc.description.abstractIn this paper we report our experience in implementing and evaluating the Data-Driven Multithreading (DDM) model on a heterogeneous multi-core processor. DDM is a non-blocking multithreading model that decouples the synchronization from the computation portions of a program, allowing them to execute asynchronously in a dataflow manner. Thread dependencies are determined by the compiler/programmer while thread scheduling is done dynamically at runtime based on data availability. The target processor for this implementation is the Cell processor. We call this implementation the Data-Driven Multithreading Virtual Machine for the Cell processor (DDM-(Formula presented.)). Thread scheduling is handled in software by the Power Processing Element core of the Cell while the Synergistic Processing Element cores execute the program threads. DDM-(Formula presented.) virtualizes the parallel resources of the Cell, handles the heterogeneity of the cores, manages the Cell memory hierarchy efficiently and supports distributed execution across a cluster of Cell nodes. DDM-(Formula presented.) has been implemented on a single Cell processor with six computation cores, as well as, on a four Cell processor cluster with 24 computation cores. We present an in-depth performance analysis of DDM-(Formula presented.), using a suite of standard computational benchmarks. The evaluation shows that DDM-(Formula presented.) scales well and tolerates scheduling overheads, memory and communication latencies effectively. Furthermore, DDM-(Formula presented.) compares favorably with other platforms targeting the Cell processor, such as, the CellSs and Sequoia. © 2017 Springer Science+Business Media New Yorken
dc.sourceInternational Journal of Parallel Programmingen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85011880092&doi=10.1007%2fs10766-016-0486-6&partnerID=40&md5=7f06b43eeb05381c70496cea9d0fa07e
dc.subjectCellsen
dc.subjectCytologyen
dc.subjectPerformance analysisen
dc.subjectSchedulingen
dc.subjectMulticore programmingen
dc.subjectProgram processorsen
dc.subjectData-flow schedulingen
dc.subjectMulti-core systemsen
dc.subjectMultitaskingen
dc.subjectCacheFlowen
dc.subjectCommunication latencyen
dc.subjectData flow analysisen
dc.subjectData-Driven Multithreadingen
dc.subjectDataflow schedulingen
dc.subjectHeterogeneous multi core processorsen
dc.subjectHeterogeneous processorsen
dc.subjectVirtual machineen
dc.titleData-Driven Thread Execution on Heterogeneous Processorsen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/s10766-016-0486-6
dc.description.startingpage1
dc.description.endingpage27
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Article in Press</p>en
dc.source.abbreviationInt J Parallel Programen
dc.contributor.orcidEvripidou, Paraskevas [0000-0002-2335-9505]
dc.contributor.orcidMatheou, George [0000-0002-3019-7102]
dc.gnosis.orcid0000-0002-2335-9505
dc.gnosis.orcid0000-0002-3019-7102


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