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dc.contributor.authorJordà Peroliu, Marc
dc.contributor.authorRai, Siddharth
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorLabarta Mancho, Jesús José
dc.contributor.authorPeña Monferrer, Antonio José
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2022-11-10T11:07:34Z
dc.date.available2022-11-10T11:07:34Z
dc.date.issued2022
dc.identifier.citationJorda, M. [et al.]. ecoHMEM: Improving object placement methodology for hybrid memory systems in HPC. A: IEEE International Conference on Cluster Computing. "2022 IEEE International Conference on Cluster Computing, Cluster 2022: Heidelberg, Germany, 6-9 September 2022: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 278-288. ISBN 978-1-6654-9856-2. DOI 10.1109/CLUSTER51413.2022.00040.
dc.identifier.isbn978-1-6654-9856-2
dc.identifier.urihttp://hdl.handle.net/2117/375994
dc.description.abstractRecent byte-addressable persistent memory (PMEM) technology offers capacities comparable to storage devices and access times much closer to DRAMs than other non-volatile memory technology. To palliate the large gap with DRAM performance, DRAM and PMEM are usually combined. Users have the choice to either manage the placement to different memory spaces by software or leverage the DRAM as a cache for the virtual address space of the PMEM. We present novel methodology for automatic object-level placement, including efficient runtime object matching and bandwidth-aware placement. Our experiments leveraging Intel® Optane™ Persistent Memory show from matching to greatly improved performance with respect to state-of-the-art software and hardware solutions, attaining over 2x runtime improvement in miniapplications and over 6% in OpenFOAM, a complex production application.
dc.description.sponsorshipThis paper received funding from the Intel-BSC Exascale Laboratory SoW 5.1, the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 749516, the EPEEC project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 801051, the DEEP-SEA project from the European Commission’s EuroHPC program under grant agreement 955606, and the Ministerio de Ciencia e Innovacion—Agencia Estatal de Investigación (PID2019-107255GB-C21/AEI/10.13039/501100011033).
dc.format.extent11 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshMemory management (Computer science)
dc.subject.lcshSupercomputers
dc.subject.otherData placement
dc.subject.otherHybrid memory systems
dc.subject.otherOptane
dc.titleecoHMEM: Improving object placement methodology for hybrid memory systems in HPC
dc.typeConference report
dc.subject.lemacGestió de memòria (Informàtica)
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1109/CLUSTER51413.2022.00040
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9912666
dc.rights.accessOpen Access
local.identifier.drac34857331
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/749516/EU/Advanced Ecosystem for Broad Heterogeneous Memory Usage/ECO-H-MEM
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/801051/EU/European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EPEEC)/EPEEC
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/955606/EU/DEEP – SOFTWARE FOR EXASCALE ARCHITECTURES/DEEP-SEA
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107255GB-C21/ES/BSC - COMPUTACION DE ALTAS PRESTACIONES VIII/
local.citation.authorJorda, M.; Rai, S.; Ayguade, E.; Labarta, J.; Peña, A.
local.citation.contributorIEEE International Conference on Cluster Computing
local.citation.publicationName2022 IEEE International Conference on Cluster Computing, Cluster 2022: Heidelberg, Germany, 6-9 September 2022: proceedings
local.citation.startingPage278
local.citation.endingPage288


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