research-article

A quantitative evaluation of persistent memory hash indexes

Authors:

Hao ChenAuthors Info & Claims

The VLDB Journal, Volume 33, Issue 2

Pages 375 - 397

https://doi.org/10.1007/s00778-023-00812-1

Published: 09 September 2023 Publication History

Abstract

Persistent memory (PMem) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery. Especially with the release of Intel Optane DC Persistent Memory Modules, we have witnessed a flourish in (re)designing persistent hash indexes. However, most of them are focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how the proposed hash indexes perform under a unified testing framework and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of several state-of-the-art hash tables including CCEH, Dash, PCLHT, Clevel, Viper, Halo, SOFT, and Plush, with the second-generation PMem hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PMem bandwidth, CPU instructions, and NUMA) affect the performance of PMem-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts and suggest desirable optimizations and directions for the future development of PMem-based hash tables.

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Cited By

Wang HDai HChen SChen G(2024)Rethinking Hash Tables: Challenges and Opportunities with Compute Express Link (CXL)Proceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674418(23-27)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674418

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A quantitative evaluation of persistent memory hash indexes

Index terms have been assigned to the content through auto-classification.

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cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 33, Issue 2

Mar 2024

305 pages

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Publication History

Published: 09 September 2023

Accepted: 18 August 2023

Revision received: 15 June 2023

Received: 12 January 2023

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Wang HDai HChen SChen G(2024)Rethinking Hash Tables: Challenges and Opportunities with Compute Express Link (CXL)Proceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674418(23-27)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674418

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