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Main memory controller with multiple media technologies for big data workloads

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Main memory controller with multiple media technologies for big data workloads

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dc.contributor.author Avargues, Miguel A. es_ES
dc.contributor.author Lurbe-Sempere, Manel es_ES
dc.contributor.author Petit Martí, Salvador Vicente es_ES
dc.contributor.author Gómez Requena, María Engracia es_ES
dc.contributor.author Yang, Rui es_ES
dc.contributor.author Zhu, Xiaoping es_ES
dc.contributor.author Wang, Guanhao es_ES
dc.contributor.author Sahuquillo Borrás, Julio es_ES
dc.date.accessioned 2024-06-26T18:11:41Z
dc.date.available 2024-06-26T18:11:41Z
dc.date.issued 2023-05-22 es_ES
dc.identifier.uri http://hdl.handle.net/10251/205507
dc.description.abstract [EN] SRAM and DRAM memory technologies have been dominant in the implementations of memory subsystems. In recent years, and mainly driven by the huge memory demands of big data applications, NVRAM technology has emerged as a denser memory technology, enabling the design of new hybrid DRAM/NVRAM memory hierarchies that combine multiple memory media technologies to balance memory capacity, latency, cost, and endurance. Two main approaches are being applied to the design of hybrid memory hierarchies: the hybrid address space approach, which relies on the programmer or the operating system to choose the memory technology where each memory page should be stored; and the (only) NVM address space approach, where a faster technology (e.g. commodity DRAM) is needed to acts as a cache of NVRAM to boost the performance. This approach presents architectural challenges such the organization of metadata (e.g. cache tags) and the selection of the proper technology for each memory component. In contrast to existing approaches, this work proposes a memory controller that leverages novel memory technologies such as eDRAM and MRAM to mitigate NVRAM bus contention and improve the performance of the NVM address space. The devised solution proposes a two-level cache hierarchy in the memory controller: a SRAM sector cache and a (x)RAM cache. The (x)RAM cache, much denser, helps significantly reduce the number of accesses to NVRAM. Experimental results show that implementing the (x)RAM cache with eDRAM or MRAM is the best performing approach. Moreover, the eRAM is able to improve the SRAM cache miss penalty by up to 50% and 80%, and overall system performance by 15% and 23%. es_ES
dc.description.sponsorship This work has been partially supported by Huawei under Agreement No: TC20210705020, by the Spanish Ministerio de Ciencia e Innovacion and European ERDF under grants PID2021-123627OB-C51 and TED2021-130233B-C32. es_ES
dc.language Inglés es_ES
dc.publisher SpringerOpen es_ES
dc.relation.ispartof Journal of Big Data es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Gem5 simulator es_ES
dc.subject NVMain simulator es_ES
dc.subject Main memory es_ES
dc.subject NVRAM media es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Main memory controller with multiple media technologies for big data workloads es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1186/s40537-023-00761-0 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123627OB-C51/ES/MEJORA DEL PROCESADOR, SUBSISTEMA DE MEMORIA, ACELERADORES Y REDES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/HUAWEI TECHNOLOGIES CO., LTD//TC20210705020//SIMULATED HYBRID MEMORY CONTROLLER (SHMC) AND PREFETCHING TC20210705020/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//TED2021-130233B-C32//SERVIDORES Y REDES CON ALTA EFICIENCIA ENERGETICA PARA CENTROS DE PROCESOS DE DATOS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Avargues, MA.; Lurbe-Sempere, M.; Petit Martí, SV.; Gómez Requena, ME.; Yang, R.; Zhu, X.; Wang, G.... (2023). Main memory controller with multiple media technologies for big data workloads. Journal of Big Data. 10(1). https://doi.org/10.1186/s40537-023-00761-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1186/s40537-023-00761-0 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 2196-1115 es_ES
dc.relation.pasarela S\505763 es_ES
dc.contributor.funder HUAWEI TECHNOLOGIES CO., LTD es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
upv.costeAPC 1690 es_ES


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