Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units
Summary: GPU-oriented inverted-list processing exploits empirically linear docID distributions: Linear Regression and Hash Segmentation tighten parallel intersection searches, while Linear Regression Compression enables parallel decompression. Experiments show higher query throughput than CPU-centric approaches. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Naiyong Ao (Nankai University)
- 2. Fan Zhang (Nankai University)
- 3. Di Wu (Nankai University)
- 4. Douglas S. Stones (Monash University)
- 5. Gang Wang (Nankai University)
- 6. Xiaoguang Liu (Nankai University)
- 7. Jing Liu (Nankai University)
- 8. Sheng Lin (Nankai University)
BibTeX Citation
@article{ao_vldb11,
title = {{Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units}},
author = {Ao, Naiyong and Zhang, Fan and Wu, Di and Stones, Douglas S. and Wang, Gang and Liu, Xiaoguang and Liu, Jing and Lin, Sheng},
journal = {PVLDB},
series = {{VLDB} '11},
volume = {4},
number = {8},
pages = {470--481},
doi = {10.14778/2002974.2002975},
url = {https://doi.org/10.14778/2002974.2002975},
year = {2011}
}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,717 | Improving the Performance of List Intersection | 2009 | VLDB | 9.9327227e-05 |
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