Rethinking SIMD Vectorization for In-Memory Databases
Summary: Novel SIMD vectorized operators for in-memory DBs using gathers/scatters (selections, hashes, partitioning, sorting, joins). On MIC Xeon Phi and mainstream CPUs, up to 10x faster than prior work; highlights algorithmic and power-efficiency implications across simple vs. complex cores. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 51 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,850 | Vectorizing an In Situ Query Engine | 2016 | SIGMOD | 4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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