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What We Can Learn from Persistent Memory for CXL

dc.contributor.authorBenson, Lawrence
dc.contributor.authorWeisgut, Marcel
dc.contributor.authorRabl, Tilmann
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:03Z
dc.date.available2023-02-23T14:00:03Z
dc.date.issued2023
dc.description.abstractWith Persistent Memory (PMem) entering the long-established memory hierarchy, various assumptions about the performance and granularity of memory access have been disrupted. To adapt existing applications and design new systems, research focused on how to efficiently move data between different types of memory, how to handle varying access latency, and how to trade off price for performance. Even though Optane is now discontinued, we expect that the insights gained from previous PMem research apply to future work on Compute Express Link (CXL) attached memory. In this paper, we discuss how limited hardware availability impacts the performance generalization of new designs, how existing CPU components are not adapted towards different access characteristics, and how multi-tier memory setups offer different price-performance trade-offs. To support future CXL research in each of these areas, we discuss how our insights apply to CXL and which problems researchers may encounter along the way.en
dc.identifier.doi10.18420/BTW2023-48
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40355
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.titleWhat We Can Learn from Persistent Memory for CXLen
dc.typeText/Conference Paper
gi.citation.endPage761
gi.citation.publisherPlaceBonn
gi.citation.startPage757
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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