VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Revitalizing the Forgotten On-Chip DMA to Expedite Data Movement in NVM-based Storage Systems

dc.contributor.authorSu, Jingboen
dc.contributor.authorLi, Jiahaoen
dc.contributor.authorChen, Luofanen
dc.contributor.authorLi, Chengen
dc.contributor.authorZhang, Kaien
dc.contributor.authorYang, Liangen
dc.contributor.authorNoh, Sam H.en
dc.contributor.authorXu, Yinlongen
dc.date.accessioned2024-02-19T20:08:36Zen
dc.date.available2024-02-19T20:08:36Zen
dc.date.issued2023en
dc.description.abstractData-intensive applications executing on NVM-based storage systems experience serious bottlenecks when moving data between DRAM and NVM. We advocate for the use of the long-existing but recently neglected on-chip DMA to expedite data movement with three contributions. First, we explore new latency-oriented optimization directions, driven by a comprehensive DMA study, to design a high-performance DMA module, which significantly lowers the I/O size threshold to observe benefits. Second, we propose a new data movement engine, Fastmove, that coordinates the use of the DMA along with the CPU with judicious scheduling and load splitting such that the DMA’s limitations are compensated, and the overall gains are maximized. Finally, with a general kernel-based design, simple APIs, and DAX file system integration, Fastmove allows applications to transparently exploit the DMA and its new features without code change. We run three data-intensive applications MySQL, GraphWalker, and Filebench atop NOVA, ext4-DAX, and XFS-DAX, with standard benchmarks like TPC-C, and popular graph algorithms like PageRank. Across single- and multi-socket settings, compared to the conventional CPU-only NVM accesses, Fastmove introduces to TPC-C with MySQL 1.13-2.16× speedups of peak throughput, reduces the average latency by 17.7-60.8%, and saves 37.1-68.9% CPU usage spent in data movement. It also shortens the execution time of graph algorithms with GraphWalker by 39.7-53.4%, and introduces 1.12-1.27× throughput speedups for Filebench.en
dc.description.versionPublished versionen
dc.format.extentPages 363-378en
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn9781939133328en
dc.identifier.orcidNoh, Sam Hyuk [0000-0002-9152-0321]en
dc.identifier.urihttps://hdl.handle.net/10919/118053en
dc.language.isoenen
dc.publisherUsenix Associationen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleRevitalizing the Forgotten On-Chip DMA to Expedite Data Movement in NVM-based Storage Systemsen
dc.title.serialPROCEEDINGS of THE 21ST USENIX CONFERENCE ON FILE AND STORAGE TECHNOLOGIES, FAST 2023en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherBooken
pubs.finish-date2023-02-23en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2023-02-21en

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
fast23-su.pdf
Size:
955 KB
Format:
Adobe Portable Document Format
Description:
Published version
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description: