FailAmp: Relativization Transformation for Soft Error Detection in Structured Address Generation
Authors: 
Ian Briggs, Arnab Das, Mark Baranowski, Vishal Sharma, Sriram Krishnamoorthy, 
Zvonimir Rakamarić, Ganesh GopalakrishnanAuthors Info & Claims
Article No.: 50, Pages 1 - 21
Abstract
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%.
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Index Terms
- FailAmp: Relativization Transformation for Soft Error Detection in Structured Address Generation
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December 2019
572 pages
Copyright © 2019 ACM.
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Publication History
Published: 18 December 2019
Accepted: 01 October 2019
Revised: 01 September 2019
Received: 01 June 2019
Published in TACO Volume 16, Issue 4
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