Publication:
Resource Management Optimization in Multi-Processor Platforms

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorHristov, Atanas
dc.contributor.authorNikolova, Iva
dc.contributor.authorZapryanov, Georgi
dc.contributor.authorKimovski, Dragi
dc.contributor.authorKumbaroska, Vesna
dc.contributor.editorCarretero Pérez, Jesús
dc.contributor.editorGarcía Blas, Javier
dc.contributor.editorMargenov, Svetozar
dc.contributor.otherUniversidad Carlos III de Madrid. Computer Architecture, Communications and Systems Group (ARCOS)
dc.date.accessioned2017-02-20T09:29:59Z
dc.date.available2017-02-20T09:29:59Z
dc.date.issued2016-12
dc.descriptionProceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016). Sofia (Bulgaria), October, 6-7, 2016.es
dc.description.abstractThe modern high-performance computing systems (HPCS) are composed of hundreds of thousand computational nodes. An effective resource allocation in HPCS is a subject for many scientific research investigations. Many programming models for effective resources allocation have been proposed. The main purpose of those models is to increase the parallel performance of the HPCS. This paper investigates the efficiency of parallel algorithm for resource management optimization based on Artificial Bee Colony (ABC) metaheuristic while solving a package of NP-complete problems on multi-processor platform.In order to achieve minimal parallelization overhead in each cluster node, a multi-level hybrid programming model is proposed that combines coarse-grain and fine-grain parallelism. Coarse-grain parallelism is achieved through domain decomposition by message passing among computational nodes using Message Passing Interface (MPI) and fine-grain parallelism is obtained by loop-level parallelism inside each computation node by compiler-based thread parallelization via Intel TBB. Parallel communications profiling is made and parallel performance parameters are evaluated on the basis of experimental results.es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationCarretero Pérez, Jesús; et.al. (eds.). (2016) Proceedings of the Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016): Sofia, Bulgaria. Universidad Carlos III de Madrid, pp. 23-29.es
dc.identifier.isbn978-84-617-7450-0
dc.identifier.publicationtitleProceedings of the Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016): Sofia, Bulgariaes
dc.identifier.urihttps://hdl.handle.net/10016/24230
dc.language.isoenges
dc.relation.eventdateOctober, 6-7, 2016es
dc.relation.eventnumber3
dc.relation.eventplaceSofia, Bulgariaes
dc.relation.eventtitleInternational Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016)es
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.subject.otherHigh-Performance Computinges
dc.subject.otherParallel Programming Modeles
dc.subject.otherParallel Performancees
dc.subject.otherParallel Algorithmes
dc.titleResource Management Optimization in Multi-Processor Platformses
dc.typeconference paper*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
resource_NESUS_2016.pdf
Size:
1.22 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016)