research-article

Wormhole Filters: Caching Your Hash on Persistent Memory

Authors:

Guihai ChenAuthors Info & Claims

EuroSys '24: Proceedings of the Nineteenth European Conference on Computer Systems

Pages 456 - 471

https://doi.org/10.1145/3627703.3629590

Published: 22 April 2024 Publication History

Abstract

Approximate membership query (AMQ) data structures can approximately determine whether an element is in the set with high efficiency. They are widely used in distributed systems, database systems, bioinformatics, IoT applications, data stream mining, etc. However, the memory consumption of AMQ data structures grows rapidly as the data scale grows, which limits the system's ability to process a massive amount of data. The emerging persistent memory provides a close-to-DRAM access speed and terabyte-level capacity, facilitating AMQ data structures to handle massive data. Nevertheless, existing AMQ data structures perform poorly on persistent memory due to intensive random accesses and/or sequential writes. Therefore, we propose a novel AMQ data structure called wormhole filter, which achieves high performance on persistent memory by reducing random accesses and sequential writes. In addition, we reduce the number of log records for lower recovery overhead. Theoretical analysis and experimental results show that wormhole filters significantly outperform competitive state-of-the-art AMQ data structures. For example, wormhole filters achieve 23.26× insertion throughput, 1.98× positive lookup throughput, and 8.82× deletion throughput of the best competing baseline.

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Cited By

Dai HLi MWang HZhang H(2025)Course Design and Textbook Development for Introduction to Computer Systems Course in the Era of ConcurrencyComputing and Combinatorics10.1007/978-981-96-1195-9_7(42-47)Online publication date: 13-Feb-2025
https://doi.org/10.1007/978-981-96-1195-9_7
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
Wang HDai HChen SLi MGu RChai HZheng JChen ZLi SDeng XChen G(2024)Bamboo Filters: Make Resizing Smooth and AdaptiveIEEE/ACM Transactions on Networking10.1109/TNET.2024.340399732:5(3776-3791)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3403997

Index Terms

Wormhole Filters: Caching Your Hash on Persistent Memory
1. Information systems
  1. Data management systems
    1. Data structures
  2. Information storage systems
    1. Information storage technologies
      1. Storage class memory
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis

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cover image ACM Conferences

EuroSys '24: Proceedings of the Nineteenth European Conference on Computer Systems

April 2024

1245 pages

ISBN:9798400704376

DOI:10.1145/3627703

Copyright © 2024 ACM.

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Published: 22 April 2024

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Huawei Technologies Co., Ltd.
Jiangsu High-level Innovation and Entrepreneurship (Shuangchuang) Program
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University
National Natural Science Foundation of China

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EuroSys '24

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SIGOPS

EuroSys '24: Nineteenth European Conference on Computer Systems

April 22 - 25, 2024

Athens, Greece

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Overall Acceptance Rate 241 of 1,308 submissions, 18%

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EuroSys '25

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Twentieth European Conference on Computer Systems

March 30 - April 3, 2025

Rotterdam , Netherlands

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Cited By

Dai HLi MWang HZhang H(2025)Course Design and Textbook Development for Introduction to Computer Systems Course in the Era of ConcurrencyComputing and Combinatorics10.1007/978-981-96-1195-9_7(42-47)Online publication date: 13-Feb-2025
https://doi.org/10.1007/978-981-96-1195-9_7
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
Wang HDai HChen SLi MGu RChai HZheng JChen ZLi SDeng XChen G(2024)Bamboo Filters: Make Resizing Smooth and AdaptiveIEEE/ACM Transactions on Networking10.1109/TNET.2024.340399732:5(3776-3791)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3403997

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