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Storage"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Data-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,\n            <jats:monospace>Fastmove<\/jats:monospace>\n            , that coordinates the use of the DMA along with the CPU with DDIO-aware strategies, judicious scheduling, and load splitting such that the DMA\u2019s limitations are compensated, and the overall gains are maximized. Finally, with a general kernel-based design, simple APIs, and DAX file system integration,\n            <jats:monospace>Fastmove<\/jats:monospace>\n            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\n            <jats:monospace>NOVA<\/jats:monospace>\n            ,\n            <jats:monospace>ext4-DAX<\/jats:monospace>\n            , and\n            <jats:monospace>XFS-DAX<\/jats:monospace>\n            , 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,\n            <jats:monospace>Fastmove<\/jats:monospace>\n            introduces to TPC-C with MySQL 1.13\u20132.16\u00d7 speedups of peak throughput, reduces the average latency by 17.7\u201360.8%, and saves 37.1\u201368.9% CPU usage spent in data movement. It also shortens the execution time of graph algorithms with GraphWalker by 39.7\u201353.4%, and introduces 1.01\u20131.48\u00d7 throughput speedups for Filebench.\n          <\/jats:p>","DOI":"10.1145\/3656477","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T11:07:08Z","timestamp":1714993628000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fastmove: A Comprehensive Study of On-Chip DMA and its Demonstration for Accelerating Data Movement in NVM-based Storage Systems"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5933-7367","authenticated-orcid":false,"given":"Jiahao","family":"Li","sequence":"first","affiliation":[{"name":"Computer Science, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4378-0168","authenticated-orcid":false,"given":"Jingbo","family":"Su","sequence":"additional","affiliation":[{"name":"Computer Science, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0258-8983","authenticated-orcid":false,"given":"Luofan","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7064-6120","authenticated-orcid":false,"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"CS. 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