Dash: Scalable Hashing on Persistent Memory
Summary: Dash: scalable, dynamic hashing on real byte-addressable persistent memory (Optane DCPMM). Adapts extendible and linear hashing; delivers up to 3.9x throughput, about 90% load factor, and 57 ms instant recovery on 24 cores, outperforming emulation-based approaches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Baotong Lu (Chinese University of Hong Kong)
- 2. Xiangpeng Hao (Simon Fraser University)
- 3. Tianzheng Wang (Simon Fraser University)
- 4. Eric Lo (Chinese University of Hong Kong)
BibTeX Citation
@article{lu_vldb20,
title = {{Dash: Scalable Hashing on Persistent Memory}},
author = {Lu, Baotong and Hao, Xiangpeng and Wang, Tianzheng and Lo, Eric},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {8},
pages = {1147--1161},
doi = {10.14778/3389133.3389134},
url = {https://doi.org/10.14778/3389133.3389134},
year = {2020}
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