FailAmp: Relativization Transformation for Soft Error Detection in Structured Address Generation
FailAmp: Relativization Transformation for Soft Error Detection in Structured Address Generation
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FailAmp：结构化地址生成中软错误检测的相对化变换
DOI：
10.1145/3369381
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发表时间：
2020
影响因子：
1.6
通讯作者：
Gopalakrishnan, Ganesh
中科院分区：
文献类型：
--
作者：
Briggs, Ian;Das, Arnab;Baranowski, Mark;Sharma, Vishal;Krishnamoorthy, Sriram;Rakamarić, Zvonimir;Gopalakrishnan, Ganesh
我们提出了 FailAmp，一种新颖的 LLVM 程序转换算法，它使采用结构化索引计算的程序对软错误更加鲁棒。如果没有 FailAmp，偏移错误可能无法被检测到；使用 FailAmp，所有后续偏移都会相对化，建立在故障偏移的基础上。 FailAmp 可以利用 ARM 等 ISA 来进一步减少开销。我们使用 SMT 求解器验证 FailAMP 的正确性属性，并在故障注入活动下使用许多高性能计算基准进行全面评估。 FailAmp 为地址计算提供完整的软错误检测，同时产生约 5% 的平均开销。
We present FailAmp, a novel LLVM program transformation algorithm that makes programs employing structured index calculations more robust against soft errors. Without FailAmp, an offset error can go undetected; with FailAmp, all subsequent offsets are relativized, building on the faulty one. FailAmp can exploit ISAs such as ARM to further reduce overheads. We verify correctness properties of FailAMP using an SMT solver, and present a thorough evaluation using many high-performance computing benchmarks under a fault injection campaign. FailAmp provides full soft-error detection for address calculation while incurring an average overhead of around 5%.