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DURINN: Adversarial Memory and Thread Interleaving for Detecting Durable Linearizability Bugs

Xinwei Fu, Dongyoon Lee, Changwoo Min

2022Year
10Citations
10Top-tier citations

Abstract

Non-volatile memory (NVM) has promoted the development of concurrent crash-consistent data structures, which serve as the backbone of various in-memory persistent applications. Durable linearizability defines the correct semantics of NVM-backed concurrent crash-consistent data structures, in which linearizability is preserved even in the presence of a crash event. However, designing and implementing a correct durable linearizable data structure remain challenging as developers are to manually control durability (persistence) using low-level cache flush and store fence instructions.

We present DURINN, to the best of our knowledge, the first durable linearizability checker for concurrent NVM data structures. DURINN is based on the novel observation on the gap between linearizability point -when the changes to a concurrent data structure become publicly visible -and durability point -when the changes become persistent. From the detailed gap analysis, we derive three durable linearizability bug patterns that render a linearizable data structure not durable linearizable. To tame the huge NVM states and thread interleaving test space, DURINN statically identifies likely-linearization points and actively constructs adversarial NVM state and thread interleaving settings that increase the likelihood of revealing durable linearizability bugs. DURINN effectively detected 27 (15 new) durable linearizability bugs from 12 concurrent NVM data structures without a test space explosion problem.

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