一句话：将软件测试技术引入硬件可靠性分析，显著加速 SDC 致错指令识别并提升准确率。
随着 CMOS 缩放结束，硬件错误概率上升，静默数据损坏（SDC）尤其危险，因其可在用户不知情时产生不可接受输出。现有可靠性分析能识别导致 SDC 的指令，但速度过慢或为提速牺牲精度。Minotaur 从软件测试借鉴四类技术：输入质量准则（PC 覆盖）、由基准生成最小化输入、错误注入用例优先级与提前终止、跨输入优先级。作者将其应用于 Approxilyzer。前三种技术平均加速 10.3 倍，识别出 96% 的 SDC 致错指令，而单独 Approxilyzer 仅 64%；第四种技术使跨输入识别全部探索到的 SDC 致错指令比逐输入独立分析平均快 2.3 倍。
With the end of conventional CMOS scaling, efficient resiliency solutions are needed to address the increased likelihood of hardware errors. Silent data corruptions (SDCs) are especially harmful because they can create unacceptable output without the user's knowledge. Several resiliency analysis techniques have been proposed to identify SDC-causing instructions, but they remain too slow for practical use and/or sacrifice accuracy to improve analysis speed. We develop Minotaur, a novel toolkit to improve the speed and accuracy of resiliency analysis. The key insight behind Minotaur is that modern resiliency analysis has many conceptual similarities to software testing; therefore, adapting techniques from the rich software testing literature can lead to principled and significant improvements in resiliency analysis. Minotaur identifies and adapts four concepts from software testing: 1) it introduces the concept of input quality criteria for resiliency analysis and identifies PC coverage as a simple but effective criterion; 2) it creates (fast) minimized inputs from (slow) standard benchmark inputs, using the input quality criteria to assess the goodness of the created input; 3) it adapts the concept of test case prioritization to prioritize error injections and invoke early termination for a given instruction to speed up error-injection campaigns; and 4) it further adapts test case or input prioritization to accelerate SDC discovery across multiple inputs. We evaluate Minotaur by applying it to Approxilyzer, a state-of-the-art resiliency analysis tool. Minotaur's first three techniques speed up Approxilyzer's resiliency analysis by 10.3X (on average) for the workloads studied. Moreover, they identify 96% (on average) of all SDC-causing instructions explored, compared to 64% identified by Approxilyzer alone. Minotaur's fourth technique (input prioritization) enables identifying all SDC-causing instructions explored across multiple inputs at a speed 2.3X faster (on average) than analyzing each input independently for our workloads.
@inproceedings{paperbot4853,
title = {Minotaur},
author = {Abdulrahman Mahmoud and Radha Venkatagiri and Khalique Ahmed and Sasa Misailovic and Darko Marinov and Christopher W. Fletcher and Sarita V. Adve},
booktitle = {ASPLOS},
year = {2019},
doi = {10.1145/3297858.3304050}
}