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https://dx.doi.org/10.48550/ar...
Article . 2022
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Practical Detectability for Persistent Lock-Free Data Structures

Authors: Kyeongmin Cho; Seungmin Jeon; Jeehoon Kang;

Practical Detectability for Persistent Lock-Free Data Structures

Abstract

Persistent memory (PM) is an emerging class of storage technology that combines the benefits of DRAM and SSD. This characteristic inspires research on persistent objects in PM with fine-grained concurrency control. Among such objects, persistent lock-free data structures (DSs) are particularly interesting thanks to their efficiency and scalability. One of the most widely used correctness criteria for persistent lock-free DSs is durable linearizability (Izraelevitz et. al., DISC 2016). However, durable linearizability is insufficient to use persistent DSs for fault-tolerant systems requiring exactly-once semantics for storage systems, because we may not be able to detect whether an operation is performed when a crash occurs. We present a practical programming framework for persistent lock-free DSs with detectability. In contrast to the prior work on such DSs, our framework supports (1) primitive detectable operations such as space-efficient compare-and-swap, insertion, and deletion; (2) systematic transformation of lock-free DSs in DRAM into those in PM requiring modest efforts; (3) comparable performance with non-detectable DSs by DRAM scratchpad optimization; and (4) recovery from both full system and thread crashes. The key idea is memento objects serving as a lightweight, precise, and per-thread checkpoints in PM. As a case study, we implement lock-free and combining queues and hash tables with detectability that outperform (and perform comparably) the state-of-the-art DSs with (and without, respectively) detectability.

Keywords

FOS: Computer and information sciences, D.1.3, Computer Science - Programming Languages, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Data Structures and Algorithms, D.3.3, Data Structures and Algorithms (cs.DS), Distributed, Parallel, and Cluster Computing (cs.DC), E.1, D.3.3; E.1; D.1.3, Programming Languages (cs.PL)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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