{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:33:20Z","timestamp":1777390400876,"version":"3.51.4"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>\n            The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently. As such, there is a void of solutions that enables hardware acceleration at the intersection of these disjoint fields. This paper sets out to be the initial step towards a unifying solution for in-\n            <jats:bold>D<\/jats:bold>\n            atabase\n            <jats:bold>A<\/jats:bold>\n            cceleration of Advanced\n            <jats:bold>A<\/jats:bold>\n            nalytics (DAnA). Deploying specialized hardware, such as FPGAs, for in-database analytics currently requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specification of advanced analytics queries to an FPGA accelerator. The accelerator implementation is generated for a User Defined Function (UDF), expressed as a part of an SQL query using a Python-embedded Domain-Specific Language (DSL). To realize an efficient in-database integration, DAnA accelerators contain a novel hardware structure,\n            <jats:italic>Striders<\/jats:italic>\n            , that directly interface with the buffer pool of the database.\n            <jats:italic>Striders<\/jats:italic>\n            extract, cleanse, and process the training data tuples that are consumed by a multi-threaded FPGA engine that executes the analytics algorithm. We integrate DAnA with PostgreSQL to generate hardware accelerators for a range of real-world and synthetic datasets running diverse ML algorithms. Results show that DAnA-enhanced PostgreSQL provides, on average, 8.3\u00d7 end-to-end speedup for real datasets, with a maximum of 28.2\u00d7. Moreover, DAnA-enhanced PostgreSQL is, on average, 4.0\u00d7 faster than the multi-threaded Apache MADLib running on Greenplum. DAnA provides these benefits while hiding the complexity of hardware design from data scientists and allowing them to express the algorithm in \u224830-60 lines of Python.\n          <\/jats:p>","DOI":"10.14778\/3236187.3236188","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1317-1331","source":"Crossref","is-referenced-by-count":36,"title":["In-RDBMS hardware acceleration of advanced analytics"],"prefix":"10.14778","volume":"11","author":[{"given":"Divya","family":"Mahajan","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon Kyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob","family":"Sacks","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adel","family":"Ardalan","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arun","family":"Kumar","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi","family":"Esmaeilzadeh","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Gartner Report on Analytics. gartner.com\/it\/page.jsp?id=1971516.  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