Accelerating Recommendation System Training by Leveraging Popular Choices
Summary: FAE exploits extreme popularity skew in embedding accesses, placing hot entries in scarce GPU memory and accelerating their updates. A hot-embedding-aware layout cuts CPU–GPU transfers, delivering 2.3× speedup over CPU-only and 1.52× over hybrid training without accuracy loss. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Muhammad Adnan (University of British Columbia)
- 2. Yassaman Ebrahimzadeh Maboud (University of British Columbia)
- 3. Divya Mahajan (Microsoft)
- 4. Prashant J. Nair (University of British Columbia)
BibTeX Citation
@article{adnan_vldb22,
title = {{Accelerating Recommendation System Training by Leveraging Popular Choices}},
author = {Adnan, Muhammad and Maboud, Yassaman Ebrahimzadeh and Mahajan, Divya and Nair, Prashant J.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {1},
pages = {127--140},
doi = {10.14778/3485450.3485462},
url = {https://doi.org/10.14778/3485450.3485462},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 803 | MRShare: Sharing Across Multiple Queries in MapReduce | 2010 | VLDB | 0.00013899943 |
| 1,446 | Analyzing and Mitigating Data Stalls in DNN Training | 2021 | VLDB | 0.0001076818 |
| 1,666 | HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics | 2016 | VLDB | 0.00010068964 |
| 3,682 | In-RDBMS Hardware Acceleration of Advanced Analytics | 2018 | VLDB | 7.2035518e-05 |
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