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TokenTLB+CUP: A Token-Based Page Classification with Cooperative Usage Prediction

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TokenTLB+CUP: A Token-Based Page Classification with Cooperative Usage Prediction

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dc.contributor.author Esteve Garcia, Albert es_ES
dc.contributor.author Ros Bardisa, Alberto es_ES
dc.contributor.author Robles Martínez, Antonio es_ES
dc.contributor.author Gómez Requena, María Engracia es_ES
dc.date.accessioned 2019-07-07T20:02:26Z
dc.date.available 2019-07-07T20:02:26Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1045-9219 es_ES
dc.identifier.uri http://hdl.handle.net/10251/123281
dc.description.abstract [EN] Discerning the private or shared condition of the data accessed by the applications is an increasingly decisive approach to achieving efficiency and scalability in multi- and many-core systems. Since most memory accesses in both sequential and parallel applications are either private (accessed only by one core) or read-only (not written) data, devoting the full cost of coherence to every memory access results in sub-optimal performance and limits the scalability and efficiency of the multiprocessor. This paper introduces TokenTLB, a TLB-based page classification approach based on exchange and count of tokens. Token counting on TLBs is a natural and efficient way for classifying memory pages, and it does not require the use of complex and undesirable persistent requests or arbitration. In addition, classification is extended with Cooperative Usage Predictor (CUP), a token-based system-wide page usage predictor retrieved through TLB cooperation, in order to perform a classification unaffected by TLB size. Through cycle-accurate simulation we observed that TokenTLB spends 43.6% of cycles as private per page on average, and CUP further increases the time spent as private by 22.0%. CUP avoids 4 out of 5 TLB invalidations when compared to state-of-the-art predictors, thus proving far better prediction accuracy and making usage prediction an attractive mechanism for the first time. es_ES
dc.description.sponsorship This work has been jointly supported by the MINECO and European Commission (FEDER funds) under the project TIN2015-66972-C5-1-R and TIN2015-66972-C5-3-R and the Fundacion Seneca-Agencia de Ciencia y Tecnologia de la Region de Murcia under the project Jovenes Lideres en Investigacion 18956/JLI/13. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation MINECO/TIN2015-66972-C5-1-R es_ES
dc.relation Fundación Séneca/18956/JLI/13
dc.relation.ispartof IEEE Transactions on Parallel and Distributed Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Data classification es_ES
dc.subject Token counting es_ES
dc.subject TLB es_ES
dc.subject Private-shared es_ES
dc.subject Read-only data es_ES
dc.subject TLB Usage Predictor es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title TokenTLB+CUP: A Token-Based Page Classification with Cooperative Usage Prediction es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/TPDS.2017.2782808 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Esteve Garcia, A.; Ros Bardisa, A.; Robles Martínez, A.; Gómez Requena, ME. (2018). TokenTLB+CUP: A Token-Based Page Classification with Cooperative Usage Prediction. IEEE Transactions on Parallel and Distributed Systems. 29(5):1188-1201. https://doi.org/10.1109/TPDS.2017.2782808 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1109/TPDS.2017.2782808 es_ES
dc.description.upvformatpinicio 1188 es_ES
dc.description.upvformatpfin 1201 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 29 es_ES
dc.description.issue 5 es_ES
dc.relation.pasarela 351136 es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES
dc.contributor.funder Fundación Séneca


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