Detecting Silent Data Corruptions in Aerospace-Based Computing Using Program Invariants. (15th November 2016)

• Record Type: Journal Article
• Authors: Ma, Junchi; Yu, Dengyun; Wang, Yun; Cai, Zhenbo; Zhang, Qingxiang; Hu, Cheng
• Other Names: Damaren Christopher J. Academic Editor.
• Is Part Of: International journal of aerospace engineering. Volume 2016(2016)
• Publication Date: 2016-11-15
• Subjects: Aerospace engineering -- Periodicals; 629.105
• DOI: 10.1155/2016/8213638
• Languages: English
• ISSNs: 1687-5966

Abstract

Abstract : 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.