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Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            This article introduces\n            <jats:sans-serif>DANSEN<\/jats:sans-serif>\n            , the hardware accelerator component for neoDBMS, a full-stack computational storage system designed to manage on-device execution of database queries\/transactions as a Near-Data Processing (NDP)-operation. The proposed system enables Database Management Systems (DBMS) to offload NDP-operations to the storage while maintaining control over data through a\n            <jats:italic>native storage interface<\/jats:italic>\n            .\n            <jats:sans-serif>DANSEN<\/jats:sans-serif>\n            provides an NDP-engine that enables DBMS to perform both low-level database tasks, such as performing database administration, as well as high-level tasks like executing SQL,\n            <jats:italic>on<\/jats:italic>\n            the smart storage device while observing the DBMS concurrency control. Furthermore,\n            <jats:sans-serif>DANSEN<\/jats:sans-serif>\n            enables the incorporation of custom accelerators as an NDP-operation (e.g., to perform hardware-accelerated ML inference directly on the stored data). We built the\n            <jats:sans-serif>DANSEN<\/jats:sans-serif>\n            storage prototype and interface on an UltraScale+HBM FPGA and fully integrated it with PostgreSQL 12. Experimental results demonstrate that the proposed NDP approach outperforms software-only PostgreSQL using a fast off-the-shelf NVMe drive and significantly improves the end-to-end execution time of an aggregation operation (similar to Q6 from CH-benCHmark, 150 million records) by \u2248 10.6\u00d7. The versatility of the proposed approach is also validated by integrating a compute-intensive data analytics application with multi-row results, outperforming PostgreSQL by \u2248 1.5\u00d7.\n          <\/jats:p>","DOI":"10.1145\/3655625","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T12:19:27Z","timestamp":1712233167000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["DANSEN: Database Acceleration on Native Computational Storage by Exploiting NDP"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8092-2969","authenticated-orcid":false,"given":"Sajjad","family":"Tamimi","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4056-4883","authenticated-orcid":false,"given":"Arthur","family":"Bernhardt","sequence":"additional","affiliation":[{"name":"Reutlingen University, Reutlingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9411-0267","authenticated-orcid":false,"given":"Florian","family":"Stock","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6042-9878","authenticated-orcid":false,"given":"Ilia","family":"Petrov","sequence":"additional","affiliation":[{"name":"Reutlingen University, Reutlingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1164-3082","authenticated-orcid":false,"given":"Andreas","family":"Koch","sequence":"additional","affiliation":[{"name":"Technical Universit\u00e4t Darmstadt, Darmstadt, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/291069.291026"},{"key":"e_1_3_2_3_2","unstructured":"Anastassia Ailamaki David J. 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