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
Authors
- 1. Orestis Polychroniou (Columbia University)
- 2. Arun Raghavan (Oracle)
- 3. Kenneth A. Ross (Columbia University)
BibTeX Citation
@inproceedings{polychroniou_sigmod15,
title = {{Rethinking SIMD Vectorization for In-Memory Databases}},
author = {Polychroniou, Orestis and Raghavan, Arun and Ross, Kenneth A.},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2747645},
url = {https://dl.acm.org/doi/10.1145/2723372.2747645},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 51 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,051 | Vectorizing an In Situ Query Engine | 2016 | SIGMOD | 5.093636e-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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