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Lessons learned from the early performance evaluation of Intel optane DC persistent memory in DBMS

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dc.contributor.authorWu, Yinjun-
dc.contributor.authorPark, Kwang Hyun-
dc.contributor.authorSen, Rathijit-
dc.contributor.authorKroth, Brian-
dc.contributor.authorDo, Jae Young-
dc.date.accessioned2024-05-09T06:42:38Z-
dc.date.available2024-05-09T06:42:38Z-
dc.date.created2024-05-09-
dc.date.issued2020-06-
dc.identifier.citationProceedings of the 16th International Workshop on Data Management on New Hardware, DaMoN 2020, p. 160604-
dc.identifier.urihttps://hdl.handle.net/10371/201370-
dc.description.abstractNon-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices, while providing the low access latency and byte-addressablity of traditional DRAM memory. In this paper, we provide extensive performance evaluations on a recently released NVM device, Intel Optane DC Persistent Memory (PMem), under different configurations with several micro-benchmark tools. Further, we evaluate OLTP and OLAP database workloads with Microsoft SQL Server 2019 when using PMem as buffer pool or persistent storage. From the lessons learned we share some recommendations for future DBMS design with PMem, e.g. simple hardware or software changes are not enough for the best use of PMem in DBMSs.-
dc.language영어-
dc.publisherAssociation for Computing Machinery-
dc.titleLessons learned from the early performance evaluation of Intel optane DC persistent memory in DBMS-
dc.typeArticle-
dc.identifier.doi10.1145/3399666.3399898-
dc.citation.journaltitleProceedings of the 16th International Workshop on Data Management on New Hardware, DaMoN 2020-
dc.identifier.scopusid2-s2.0-85087654131-
dc.citation.startpage160604-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorDo, Jae Young-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area AI 애플리케이션을 위한 알고리즘-시스템 공동 설계, AI-powered Big Data Management, Generative AI, Large Language Model, ML, 고성능 대규모 AI 데이터 분석 및 처리, 모달 AI

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