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Improving CPU I/O Performance via SSD Controller FTL Support for Batched Writes

DC Field Value Language
dc.contributor.authorDo, Jae Young-
dc.contributor.authorLomet, David-
dc.contributor.authorPicoli, Ivan Luiz-
dc.date.accessioned2024-05-09T06:42:44Z-
dc.date.available2024-05-09T06:42:44Z-
dc.date.created2024-05-09-
dc.date.issued2019-07-
dc.identifier.citation15th International Workshop on Data Management on New Hardware (Damon 2019), p. 3329925-
dc.identifier.urihttps://hdl.handle.net/10371/201372-
dc.description.abstractExploiting a storage hierarchy is critical to cost-effective data management. One can achieve great performance when working solely on main memory data. But this comes at a high cost. Systems that use secondary storage as the "home" for data have much lower storage costs as they can not only make the data durable but reduce its storage cost as well. Performance then becomes the challenge, reflected in an increased execution cost. Log structured stores, e.g. Deuteronomy, improve I/O cost/performance by batching writes. However, this incurs the cost of host-based garbage collection and recovery, which duplicates SSD flash translation layer (FTL) functionality. This paper describes the design and implementation in a controller for an Open Channel SSD of a new FTL that supports multi-page I/O without host-based log structuring. This both simplifies the host system and improves performance. The new FTL improves I/O cost/performance with only modest change to the current block at a time, update-in-place interface.-
dc.language영어-
dc.publisherAssociation for Computing Machinery-
dc.titleImproving CPU I/O Performance via SSD Controller FTL Support for Batched Writes-
dc.typeArticle-
dc.identifier.doi10.1145/3329785.3329925-
dc.citation.journaltitle15th International Workshop on Data Management on New Hardware (Damon 2019)-
dc.identifier.wosid000525485300002-
dc.identifier.scopusid2-s2.0-85074451087-
dc.citation.startpage3329925-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorDo, Jae Young-
dc.type.docTypeProceedings 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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