解锁SSD并行性：一种高性能的ocssd B$^\epsilon$ε-Tree框架

IF 5.9 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2026-03-01 Epub Date: 2026-03-10 DOI:10.1109/TPDS.2026.3672185

Chi-Liang Qiu;Yao-Yu Liao;Tseng-Yi Chen;Yuan-Hao Chang

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引用次数: 0

摘要

键值存储已经成为现代大型数据中心数据管理的关键。与日志结构合并（LSM）树不同，B$^\epsilon$树通过减少读放大和利用其内部节点缓冲区中键的时间局域性来提高读性能。然而，它无法充分利用固态硬盘（ssd）的高并行性，这限制了它的有效性。这种限制源于传统ssd对主机系统隐藏了它们的并行性。开放通道ssd （ocssd）的出现向主机系统公开了物理数据存储布局，为利用ssd在读/写操作中固有的高并行性提供了独特的机会。本文介绍了一种针对ocssd设计的新型高并行性B$^\epsilon$ （HP-B$^\epsilon$）索引方案。该方案特别解决了读取操作期间并行单元（pu）之间的冲突，大大提高了B$^\epsilon$索引方法的读取性能。据我们所知，这是第一个将B$^\epsilon$索引方案应用于OCSSD架构的研究，我们的实验结果是有希望的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Unlocking SSD Parallelism: A High-Performance B$^\epsilon$ε-Tree Framework for OCSSDs

Key-value stores have become pivotal in the management of data for modern large-scale data centers. Unlike the log-structured merge (LSM) tree, the B

$^\epsilon$

tree enhances read performance by mitigating read amplification and by leveraging the temporal locality of keys in the buffer area of its internal nodes. However, its inability to fully harness the high parallelism of solid-state drives (SSDs) limits its effectiveness. This limitation stems from traditional SSDs concealing their parallelism from the host system. The advent of open-channel SSDs (OCSSDs), which expose the physical data storage layout to the host system, provides a unique opportunity to leverage SSDs’ inherent high parallelism in read/write operations. This article introduces a novel high-parallelism B

$^\epsilon$

(HP-B

$^\epsilon$

) indexing scheme designed for OCSSDs. The scheme specifically addresses conflicts between parallel units (PUs) during read operations, substantially improving read performance of the B

$^\epsilon$

indexing approach. To our knowledge, this is the first study to adapt the B

$^\epsilon$

indexing scheme for the OCSSD architecture, and our experimental results are promising.

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.