Rapid Identification of Shared Memory in Multithreaded Embedded Systems with Static Scheduling

Jahic, Jasmin; Kumar, Varun; Jung, Matthias; Wirrer, Gerhard; Wehn, Norbert; Kuhn, Thomas S.

doi:10.1145/3339186.3339195

Cited by 1 publication

(1 citation statement)

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“…Most of the modern data race detection tools use either a Lockset-based approach or HB relation to report data races. Lockset-based race detectors [20,38,74,75] can detect data races that are rare in execution paths. The main disadvantage of using lockset-based approaches is that they are unsound and therefore, may produce a lot of false alarms.…”

Section: Approaches For Building a Dependency Relationmentioning

confidence: 99%

BiRD: Race Detection in Software Binaries under Relaxed Memory Models

Jain

Purandare

Sharma

2022

ACM Trans. Softw. Eng. Methodol.

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Instruction reordering and interleavings in program execution under relaxed memory semantics result in non-intuitive behaviors, making it difficult to provide assurances about program correctness. Studies have shown that up to 90% of the concurrency bugs reported by state-of-the-art static analyzers are false alarms. As a result, filtering false alarms and detecting real concurrency bugs is a challenging problem. Unsurprisingly, this problem has attracted the interest of the research community over the past few decades. Nonetheless, many of the existing techniques rely on analyzing source code, rarely consider the effects introduced by compilers, and assume a sequentially consistent memory model. In a practical setting, however, developers often do not have access to the source code, and even commodity architectures such as x86 and ARM are not sequentially consistent. In this work, we present B i rd , a prototype tool, to dynamically detect harmful data races in x86 binaries under relaxed memory models, TSO and PSO. B i rd employs source-DPOR to explore all distinct feasible interleavings for a multithreaded application. Our evaluation of B i rd on 42 publicly available benchmarks and its comparison with the state-of-the-art tools indicate B i rd ’s potential in effectively detecting data races in software binaries.

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Section: Approaches For Building a Dependency Relationmentioning

confidence: 99%