GCFI:一种基于编译器的高精度暂态硬件故障注入方法

IF 0.5 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Hussien Al-haj Ahmad, Yasser Sedaghat
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引用次数: 0

摘要

近年来,随着系统复杂性的不断提高和技术规模的不断扩大,迫切需要精确的故障注入(FI)技术来对安全关键系统进行可靠性评估,以应对软错误等瞬态硬件故障。由于基于编译器的FI技术以高中间表示(IR)代码运行,因此其准确性不足以评估安全关键系统对软错误的弹性。虽然二进制级FI技术可以提供高精度,但由于缺少程序结构,错误传播分析具有挑战性。本文提出了一种精确的基于GCC编译器的FI技术(GCFI)来评估软件对软错误的弹性。GCFI在GCC编译器的后端操作,并通过编译器扩展对非常低级的IR代码进行检测。GCFI只在优化完成后执行一次检测,确保IR代码与汇编代码一一对应。利用GCFI在x86和ARM架构的不同基准上进行了多次FI实验,评估了GCFI的有效性。我们将结果与高级和二进制级软件FI技术进行比较,以证明GCFI的准确性。结果表明,GCFI可以评估程序对软错误的弹性,准确度与二值FI相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GCFI: A High Accurate Compiler-based Fault Injection for Transient Hardware Faults
Recently, with increasing system complexity and advanced technology scaling, there is a severe need for accurate fault injection (FI) techniques in the reliability evaluation of safety-critical systems against transient hardware faults, like soft errors. Since compiler-based FI techniques operate at a high intermediate representation (IR) code, their accuracy is insufficient to assess the resilience of safety-critical systems against soft errors. Although binary-level FI techniques can provide high accuracy, error propagation analysis is challenging due to missing program structures. This paper proposes an accurate GCC compiler-based FI technique called (GCFI) to assess the resilience of software against soft errors. GCFI operates at the back-end of the GCC compiler and instruments the very low-level IR code through a compiler extension. GCFI only performs instrumentation once right after the completion of optimization passes, assuring one-to-one correspondence of IR code with assembly code. The effectiveness of GCFI is evaluated by employing it to conduct many FI experiments on different benchmarks compiled for x86 and ARM architectures. We compare the results with high-level and binary-level software FI techniques to demonstrate the accuracy of GCFI. The results show that GCFI can assess the resilience of programs against soft errors with high accuracy similar to binary-level FI.
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来源期刊
CiteScore
1.70
自引率
14.30%
发文量
17
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