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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)

Paper ID
12824
Venue
VLDB
Year
2022
Pagerank
8.2564305e-05
Overall Rank
2,688 | 81.56%
DOI
10.14778/3485450.3485462

Incoming Non-self Citations Over Time

Authors

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)

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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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