Cross-Failure Bug Detection in Persistent Memory Programs

Liu, Sihang; Seemakhupt, Korakit; Wei, Yizhou; Wenisch, Thomas F.; Kolli, Aasheesh; Khan, Samira

doi:10.1145/3373376.3378452

Cited by 45 publications

(29 citation statements)

References 40 publications

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“…It is important but challenging to identify crash-consistency bugs, especially for concurrent PM programs. Existing PM-based debugging tools record the PM accesses [36,37] or track the persistency states [45] to detect the violation of consistency rules. However, these debugging tools fail to efficiently detect the bugs in concurrent PM programs due to the following two reasons: (1) existing PM-specific debugging tools do not consider the thread interleavings in concurrent programs, thus overlooking the inconsistency hidden in specific interleavings and (2) the input space of programs can be so huge that it is almost impossible to efficiently trigger bugs using exhaustive testing.…”

Section: Motivationmentioning

confidence: 99%

“…We refer to concurrency bugs that only occur in PM programs (not triggered in non-PM programs) as PM concurrency bugs. Many PM-specific testing tools have been proposed to detect the violations of crash consistency in PM programs by leveraging various mechanisms, e.g., programmer-annotated assertions [37], crashconsistency bug patterns [15,35,36], symbolic execution [45], model checking [20,29], and likely-correctness conditions with output equivalence checking [19], but none targets PM concurrency bugs. In fact, the concurrency bugs triggered only in specific interleavings are known to be more difficult to detect [33,38].…”

Section: Introductionmentioning

confidence: 99%

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Efficiently detecting concurrency bugs in persistent memory programs

Chen

Hua

Zhang

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Due to the salient DRAM-comparable performance, TB-scale capacity, and non-volatility, persistent memory (PM) provides new opportunities for large-scale in-memory computing with instant crash recovery. However, programming PM systems is error-prone due to the existence of crash-consistency bugs, which are challenging to diagnose especially with concurrent programming widely adopted in PM applications to exploit hardware parallelism. Existing bug detection tools for DRAM-based concurrency issues cannot detect PM crash-consistency bugs because they are oblivious to PM operations and PM consistency. On the other hand, existing PM-specific debugging tools only focus on sequential PM programs and cannot effectively detect crash-consistency issues hidden in concurrent executions.In order to effectively detect crash-consistency bugs that only manifest in concurrent executions, we propose PMRace, the first PM-specific concurrency bug detection tool. We identify and define two new types of concurrent crash-consistency bugs: PM Interthread Inconsistency and PM Synchronization Inconsistency. In particular, PMRace adopts PM-aware and coverage-guided fuzz testing to explore PM program executions. For PM Inter-thread Inconsistency, which denotes the data inconsistency hidden in thread interleavings, PMRace performs PM-aware interleaving exploration and thread scheduling to drive the execution towards executions that reveal such inconsistencies. For PM Synchronization Inconsistency between persisted synchronization variables and program data, PM-Race identifies the inconsistency during interleaving exploration. The post-failure validation reduces the false positives that come from custom crash recovery mechanisms. PMRace has found 14 bugs (10 new bugs) in real-world concurrent PM systems including PM-version memcached.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficiently detecting concurrency bugs in persistent memory programs

Chen

Hua

Zhang

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…Emerging Non-Volatile Random-Access Memories (NVRAM) provide the best of two worlds, namely the efficiency of DRAM, and the data persistency across failures of a non-volatile store [Intel 2019c;Liu et al 2020]. In particular, NVRAMs offer byte addressability, i.e., they can be addressed directly by the CPU through read and write operations, thus giving programs direct and low-latency access to persistent data, without any assistance from the operating system.…”

Section: Introductionmentioning

confidence: 99%

“…This is why NVRAMs are often referred to as Persistent Memories (PMs). The combination of efficiency and persistency is a very attractive feature, breeding interest in PMs, and spurring research both in industry and academia [ARM 2018;Intel 2019b;Liu et al 2020;Raad et al 2020. Leading chip manufacturers such as Intel and ARM have started to integrate the technology in their chips [ARM 2018;Intel 2019b].…”

Section: Introductionmentioning

confidence: 99%

“…So far, works on automated verification of programs running on PMs are rare. The papers [Liu et al 2020[Liu et al , 2019 propose a method for detecting cross-failures, i.e., checking whether persistent data remain consistent across a failure. The papers identify necessary conditions for consistent recoveries, i.e., to decide whether the data that persist before a failure, is used to take the program to a consistent state after the failure.…”

Section: Introductionmentioning

confidence: 99%

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Deciding reachability under persistent x86-TSO

Abdulla

Atig

Bouajjani

et al. 2021

Proc. ACM Program. Lang.

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We address the problem of verifying the reachability problem in programs running under the formal model Px86 defined recently by Raad et al. in POPL'20 for the persistent Intel x86 architecture. We prove that this problem is decidable. To achieve that, we provide a new formal model that is equivalent to Px86 and that has the feature of being a well structured system. Deriving this new model is the result of a deep investigation of the properties of Px86 and the interplay of its components.

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An introduction of transaction session‐induced security scheme using blockchain technology: Understanding the features of Internet of Things–based financial security systems

Wen,

Han

2024

Manage Decis Econ

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Internet of Things (IoT)–based financial systems leverage the capabilities of blockchain and artificial intelligence (AI) to enable seamless transactions and data exchange between devices. IoT‐based financial systems involve interconnected devices and services, such as payment terminals, wearables, and smart appliances, which collect, transmit, and process sensitive financial information. The study explores security methods incorporated in the financial systems designed using IoT and blockchain technologies to improve the background features. The study data were gathered from Complaint Data from the Consumer Financial Protection Bureau 2018–2022, and data‐based analysis is used in this study for detecting illegitimate interrupted transactions. Propensity Score Matching (PSM) is used for the robustness and endogeneity test; descriptive statistics is utilized in this study. Financial security systems are introduced to reduce the forging and breaching of intruders amid transactions. This study offers a novel contribution to the field of blockchain technology by furnishing a comprehensive analysis of the features of IoT‐based financial security systems from the perspective of the transaction, broadening the understanding of the feature focusing on financial security, and providing practical recommendations to address the features of IoT‐based financial security systems in blockchain technology. The study highlights how IoT devices can securely record and verify financial transactions by leveraging the blockchain's distributed ledger, preventing tampering or unauthorized access. The results of the study identify that the TS3 program relies on the transaction gaps between financial sessions, security requests between successive transactions, and sessions saved depending on the time delay. The study finds that sessions were analyzed for violations and fraud using information stored on the blockchain. The study suggests the design and building of devices and sensors in an IoT in financial security systems. Transparency should contribute to setting data privacy and safety problems in financial security systems.

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Cross-Failure Bug Detection in Persistent Memory Programs

Cited by 45 publications

References 40 publications

Efficiently detecting concurrency bugs in persistent memory programs

Efficiently detecting concurrency bugs in persistent memory programs

Deciding reachability under persistent x86-TSO

An introduction of transaction session‐induced security scheme using blockchain technology: Understanding the features of Internet of Things–based financial security systems

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