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IcebergHT: High Performance Hash Tables Through Stability and Low Associativity

Summary: IcebergHT uses stability (no item movement) and low associativity (few candidate slots) to minimize cache-line traffic and enable crash-safe updates. Iceberg hashing with in-memory metadata is space-efficient and fast, delivering PMEM inserts 50%–3× faster and queries 20%–2× faster, with ~17% space overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6550
Venue
SIGMOD
Year
2023
Pagerank
4.6494835e-05
Overall Rank
7,792 | 45.80%
DOI
10.1145/3588727

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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
899 Faster: A Concurrent Key-Value Store with In-Place Updates 2018 SIGMOD 0.00015509287
1,888 Dash: Scalable Hashing on Persistent Memory 2020 VLDB 0.00010202743
4,903 Persistent Memory Hash Indexes: An Experimental Evaluation 2021 VLDB 5.8399968e-05
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