Evaluating Persistent Memory Range Indexes
Summary: First comprehensive real-hardware evaluation of PM range indexes (wBTree, NV-Tree, BzTree, FPTree) on Intel Optane DC. PiBench’s unified model exposes which persistence techniques work in practice, plus key trade-offs and caveats. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lucas Lersch (SAP; Technical University of Dresden)
- 2. Xiangpeng Hao (Simon Fraser University)
- 3. Ismail Oukid (Snowflake)
- 4. Tianzhen Wang (Simon Fraser University)
- 5. Thomas Willhalm (Intel)
BibTeX Citation
@article{lersch_vldb20,
title = {{Evaluating Persistent Memory Range Indexes}},
author = {Lersch, Lucas and Hao, Xiangpeng and Oukid, Ismail and Wang, Tianzhen and Willhalm, Thomas},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {4},
pages = {574--587},
doi = {10.14778/3372716.3372728},
url = {https://doi.org/10.14778/3372716.3372728},
year = {2020}
}
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|---|---|---|---|---|
| 1 | 967 | BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory | 2018 | VLDB |
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| 8 | 2,288 | DPTree: Differential Indexing for Persistent Memory | 2020 | VLDB |
| 9 | 8,046 | PiBench Online: Interactive Benchmarking of Persistent Memory Indexes | 2020 | VLDB |
| 10 | 6,901 | Evaluating Persistent Memory Range Indexes: Part Two | 2022 | VLDB |