Detecting Silent Data Corruptions in Aerospace-Based Computing Using Program Invariants Article Swipe
Junchi Ma
,
Deng-Yun Yu
,
Yun Wang
,
Cai Zhenbo
,
Qingxiang Zhang
,
Cheng Hu
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1155/2016/8213638
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1155/2016/8213638
Soft error caused by single event upset has been a severe challenge to aerospace-based computing. Silent data corruption (SDC) is one of the results incurred by soft error. SDC occurs when a program generates erroneous output with no indications. SDC is the most insidious type of results and very difficult to detect. To address this problem, we design and implement an invariant-based system called Radish. Invariants describe certain properties of a program; for example, the value of a variable equals a constant. Radish first extracts invariants at key program points and converts invariants into assertions. It then hardens the program by inserting the assertions into the source code. When a soft error occurs, assertions will be found to be false at run time and warn the users of soft error. To increase the coverage of SDC, we further propose an extension of Radish, named Radish_D, which applies software-based instruction duplication mechanism to protect the uncovered code sections. Experiments using architectural fault injections show that Radish achieves high SDC coverage with very low overhead. Furthermore, Radish_D provides higher SDC coverage than that of either Radish or pure instruction duplication.
Related Topics
Concepts
Overhead (engineering)
Computer science
Soft error
Invariant (physics)
Fault tolerance
Algorithm
Code (set theory)
Software
Key (lock)
Extension (predicate logic)
Computer engineering
Theoretical computer science
Distributed computing
Programming language
Mathematics
Engineering
Operating system
Electronic engineering
Set (abstract data type)
Mathematical physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2016/8213638
- http://downloads.hindawi.com/journals/ijae/2016/8213638.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2556508997
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2556508997Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2016/8213638Digital Object Identifier
- Title
-
Detecting Silent Data Corruptions in Aerospace-Based Computing Using Program InvariantsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Junchi Ma, Deng-Yun Yu, Yun Wang, Cai Zhenbo, Qingxiang Zhang, Cheng HuList of authors in order
- Landing page
-
https://doi.org/10.1155/2016/8213638Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/ijae/2016/8213638.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/ijae/2016/8213638.pdfDirect OA link when available
- Concepts
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Overhead (engineering), Computer science, Soft error, Invariant (physics), Fault tolerance, Algorithm, Code (set theory), Software, Key (lock), Extension (predicate logic), Computer engineering, Theoretical computer science, Distributed computing, Programming language, Mathematics, Engineering, Operating system, Electronic engineering, Set (abstract data type), Mathematical physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2, 2022: 1, 2021: 1, 2020: 1, 2019: 2Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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