In-RDBMS Hardware Acceleration of Advanced Analytics
Summary: In-database analytics via FPGA; DAnA auto-maps high-level analytics queries (Python-DSL UDFs) to hardware. Striders attach to the DB buffer pool; end-to-end FPGA analytics on PostgreSQL yields ~8x avg speedup (up to 28x), beating MADLib, with 30–60 lines of Python. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Divya Mahajan (Georgia Institute of Technology)
- 2. Joon Kyung Kim (Georgia Institute of Technology)
- 3. Jacob Sacks (Georgia Institute of Technology)
- 4. Adel Ardalan (University of Wisconsin)
- 5. Arun Kumar (University of California San Diego)
- 6. Hadi Esmaeilzadeh (University of California San Diego)
BibTeX Citation
@article{mahajan_vldb18,
title = {{In-RDBMS Hardware Acceleration of Advanced Analytics}},
author = {Mahajan, Divya and Kim, Joon Kyung and Sacks, Jacob and Ardalan, Adel and Kumar, Arun and Esmaeilzadeh, Hadi},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {11},
pages = {1317--1331},
doi = {10.14778/3236187.3236188},
url = {https://doi.org/10.14778/3236187.3236188},
year = {2018}
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