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dc.contributor.authorFraguela, Basilio B.
dc.contributor.authorAndrade, Diego
dc.date.accessioned2022-01-31T17:46:37Z
dc.date.available2022-01-31T17:46:37Z
dc.date.issued2021
dc.identifier.citationFraguela, BB, Andrade, D. A software cache autotuning strategy for dataflow computing with UPC++ DepSpawn. Comp and Math Methods. 2021; 3:e1148. https://doi.org/10.1002/cmm4.1148es_ES
dc.identifier.urihttp://hdl.handle.net/2183/29512
dc.descriptionThis is the accepted version of the following article: B. B. Fraguela, D. Andrade. A software cache autotuning strategy for dataflow computing with UPC++ DepSpawn. Computational and Mathematical Methods, 3(6), e1148. November 2021, which has been published in final form at http://dx.doi.org/10.1002/cmm4.1148. This article may be used for noncommercial purposes in accordance with the Wiley Self-Archiving Policy [http://www.wileyauthors.com/self-archiving].es_ES
dc.description.abstract[Abstract] Dataflow computing allows to start computations as soon as all their dependencies are satisfied. This is particularly useful in applications with irregular or complex patterns of dependencies which would otherwise involve either coarse grain synchronizations which would degrade performance, or high programming costs. A recent proposal for the easy development of performant dataflow algorithms in hybrid shared/distributed memory systems is UPC++ DepSpawn. Among the many techniques it applies to provide good performance is a software cache that minimizes the communications among the processes involved. In this article we provide the details of the implementation and operation of this cache and we present an autotuning strategy that simplifies its usage by freeing the user from having to estimate an adequate size for this cache. Rather, the runtime is now able to define reasonably sized caches that provide near optimal behavior.es_ES
dc.description.sponsorshipThis research was funded by the Ministry of Science and Innovation of Spain (TIN2016-75845-P and PID2019-104184RB-I00, AEI/FEDER/EU, 10.13039/501100011033), and by the Xunta de Galicia co-funded by the European Regional Development Fund (ERDF) under the Consolidation Programme of Competitive Reference Groups (ED431C 2017/04). The authors acknowledge also the support from the Centro Singular de Investigación de Galicia “CITIC,” funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01. They also acknowledge the Centro de Supercomputación de Galicia (CESGA) for the use of its computerses_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2017/04es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-75845-P/ES/NUEVOS DESAFIOS EN COMPUTACION DE ALTAS PRESTACIONES: DESDE ARQUITECTURAS HASTA APLICACIONES (II)/
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFIOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONES/
dc.relation.urihttps://doi.org/10.1002/cmm4.1148es_ES
dc.subjectAutotuninges_ES
dc.subjectDataflow computinges_ES
dc.subjectDistributed memoryes_ES
dc.subjectLocalityes_ES
dc.subjectPGASes_ES
dc.subjectRuntimeses_ES
dc.titleA Software Cache Autotuning Strategy for Dataflow Computing with UPC++ DepSpawnes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleComputational and Mathematical Methodses_ES
UDC.volume3es_ES
UDC.issue6es_ES
UDC.startPagee1148es_ES
dc.identifier.doi10.1002/cmm4.1148


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