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dc.contributor.authorAliaga Estellés, José Ignacio
dc.contributor.authorCastillo, Maribel
dc.contributor.authorIserte, Sergio
dc.contributor.authorMartín Álvarez, Iker
dc.contributor.authorMayo, Rafael
dc.date.accessioned2022-09-29T07:28:34Z
dc.date.available2022-09-29T07:28:34Z
dc.date.issued2022-05-22
dc.identifier.citationAliaga, J.I.; Castillo, M.; Iserte, S.; Martín-Álvarez, I.; Mayo, R. A Survey on Malleability Solutions for High-Performance Distributed Computing. Appl. Sci. 2022, 12, 5231. https://doi.org/10.3390/app12105231ca_CA
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10234/199982
dc.description.abstractMaintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-Performance Computing (HPC) facilities is a cornerstone in the next generation of exascale supercomputers. Process malleability is presented as a straightforward mechanism to address that issue. Nowadays, the vast majority of HPC facilities are intended for distributed-memory applications based on the Message Passing (MP) paradigm. For this reason, many efforts are based on the Message Passing Interface (MPI), the de facto standard programming model. Malleability aims to rescale executions on-the-fly, in other words, reconfigure the number and layout of processes in running applications. Process malleability involves resources reallocation within the HPC system, handling processes of the application, and redistributing data among those processes to resume the execution. This manuscript compiles how different frameworks address process malleability, their main features, their integration in resource management systems, and how they may be used in user codes. This paper is a detailed state-of-the-art devised as an entry point for researchers who are interested in process malleabilityca_CA
dc.format.extent32 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relationComputación y Comunicaciones de Altas Prestaciones Consciente del Consumo Energético. Aplicaciones al Aprendizaje Profundo Computacional-UJIca_CA
dc.relation.isPartOfApplied Sciences, 2022, vol. 12, no 10ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectexascaleca_CA
dc.subjectjob reconfigurationca_CA
dc.subjectMPIca_CA
dc.subjectdata redistributionca_CA
dc.subjectresource managementca_CA
dc.subjectadaptive workloadsca_CA
dc.titleA Survey on Malleability Solutions for High-Performance Distributed Computingca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.3390/app12105231
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.mdpi.com/2076-3417/12/10/5231/htmca_CA
dc.description.sponsorshipThis research has been funded by the following projects: Project PID2020-113656RB-C21 supported by MCIN/AEI/10.13039/501100011033 and Project UJI-B2019-36 supported by Universitat Jaume I. Researcher S.Iserte was supported by the postdoctoral fellowship APOSTD/2020/026 and researcher I.Martin-Alvarez was supported by the predoctoral fellowship ACIF/2021/260 both from Valencian Region Government and European Social Funds.
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.identifierhttp://dx.doi.org/10.13039/501100011033ca_CA
project.funder.nameMinisterio de Ciencia, Innovación y Universidadesca_CA
project.funder.nameUniversitat Jaume Ica_CA
project.funder.nameGeneralitat Valencianaca_CA
oaire.awardNumberMICIU/ICTI2017-2020/PID2020-113656RB-C21ca_CA
oaire.awardNumberUJI-B2019-36ca_CA
oaire.awardNumberACIF/2021/260ca_CA
oaire.awardNumberAPOSTD/2020/026ca_CA


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