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Accelerating Python UDFs in Vectorized Query Execution

Summary: Accelerates embedded Python UDFs in vectorized analytical engines by combining vectorization, compilation (JIT/AOT), dynamic loading, and parallel execution to enable transparent in-process execution. Compares compilation frameworks and shows that compilation+parallelism yields large speedups and scalability. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
449
Venue
CIDR
Year
2022
Pagerank
5.1647573e-05
Overall Rank
6,189 | 56.95%
DOI
-

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Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,882 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 0.0001021625
2,237 Procedural Extensions of SQL: Understanding their usage in the wild 2021 VLDB 9.2212748e-05
3,080 Compiling PL/SQL Away 2020 CIDR 7.603389e-05
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