Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/1744
Campo DC Valoridioma
dc.contributor.authorRamírez, Tanausúen_US
dc.contributor.authorPajuelo González, Alejandroen_US
dc.contributor.authorSantana, Oliverio J.en_US
dc.contributor.authorValero Cortés, Mateoen_US
dc.contributor.otherDepartamento de Informática y Sistemas-
dc.date.accessioned2009-10-08T02:31:00Z-
dc.date.accessioned2018-03-07T09:00:48Z-
dc.date.available2018-03-07T09:00:48Z-
dc.date.issued2006en_US
dc.identifier.isbn978-1-59593-302-7-
dc.identifier.issn2687-9247en_US
dc.identifier.other2034-
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/1744-
dc.description.abstractThere is a continuous research effort devoted to overcome the memory wall problem. Prefetching is one of the most frequently used techniques. A prefetch mechanism anticipates the processor requests by moving data into the lower levels of the memory hierarchy. Runahead mechanism is another form of prefetching based on speculative execution. This mechanism executes speculative instructions under an L2 miss, preventing the processor from being stalled when the reorder buffer completely fills, and thus allowing the generation of useful prefetches. Another technique to alleviate the memory wall problem provides processors with large instruction windows, avoiding window stalls due to in-order commit and long latency loads. This approach, known as “Kilo-instruction processors”, relies on exploiting more instruction level parallelism allowing thousands of inflight instructions while long latency loads are outstanding in memory. In this work, we present a comparative study of the three above-mentioned approaches, showing their key issues and performance tradeoffs. We show that Runahead execution achieves better performance speedups (30% on average) than traditional prefetch techniques (21% on average). Nevertheless, the Kilo-instruction processor performs best (68% on average). Kilo-instruction processors are not only faster but also generate a lower number of speculative instructions than Runahead. When combining the prefetching mechanism evaluated with Runahead and Kilo-instruction processor, the performance is improved even more in each case (49,5% and 88,9% respectively), although Kilo-instruction with prefetch achieves better performance and executes less speculative instructions than Runahead.en_US
dc.languageengen_US
dc.relation.ispartofProceedings of the ... Conference on Computing Frontiersen_US
dc.sourceProceedings of the 3rd Conference on Computing Frontiers 2006, CF '06, [ISSN 2687-9247], 2006, p. 269-278, (Diciembre 2006)en_US
dc.subject330406 Arquitectura de ordenadoresen_US
dc.subject.otherKilo-instruction processorsen_US
dc.subject.otherMemory wallen_US
dc.subject.otherPrefetchingen_US
dc.subject.otherRunaheaden_US
dc.subject.otherSpeculative executionen_US
dc.titleKilo-instruction processors, runahead and prefetchingen_US
dc.typeConferenceObjecten_US
dc.relation.conference3rd Conference on Computing Frontiers 2006, CF '06-
dc.identifier.doi10.1145/1128022.1128059en_US
dc.identifier.scopus34247374830-
dc.contributor.authorscopusidInformática y sistemas-
dc.contributor.authorscopusidInformática y sistemas-
dc.contributor.authorscopusidInformática y sistemas-
dc.contributor.authorscopusid35608297100-
dc.contributor.authorscopusid9733817100-
dc.contributor.authorscopusid7003605046-
dc.contributor.authorscopusid24475914200-
dc.contributor.contentdmInformática y sistemas-
dc.identifier.absysnet533719-
dc.description.lastpage278en_US
dc.description.firstpage269en_US
dc.relation.volume2006en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess-
dc.type2Actas de congresosen_US
dc.utils.revisionen_US
dc.contributor.wosstandardInformática y sistemas-
dc.contributor.wosstandardInformática y sistemas-
dc.contributor.wosstandardInformática y sistemas-
dc.contributor.wosstandardInformática y sistemas-
dc.date.coverdateDiciembre 2006en_US
dc.identifier.conferenceidevents121316-
dc.identifier.ulpgces
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-7511-5783-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameSantana Jaria, Oliverio Jesús-
crisitem.event.eventsstartdate03-05-2006-
crisitem.event.eventsenddate05-05-2006-
Colección:Artículos
miniatura
Adobe PDF (219,71 kB)
Vista resumida

Citas SCOPUSTM   

3
actualizado el 01-dic-2024

Visitas

87
actualizado el 13-abr-2024

Descargas

212
actualizado el 13-abr-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.