F_Radish: Enhancing Silent Data Corruption Detection for Aerospace-Based Computing

Radiation-induced soft errors degrade the reliability of aerospace-based computing. Silent data corruption (SDC) is the most dangerous and insidious type of soft error result. To detect SDC, program invariant assertions are used to harden programs. However, there exist redundant assertions in hardened programs, which impairs the detection efficiency. Benign errors are another type of soft error result. An assertion may detect benign errors, incurring unnecessary recovery overhead. The detection degree of an assertion represents the detection capability, and an assertion with a high detection degree can detect severe errors. To improve the detection efficiency and detection degree while reducing the benign detection ratio, F_Radish is proposed in the present work to screen redundant assertions in a novel way. At a program point, the detection degree and benign detection ratio are considered to evaluate the importance of the assertions in the program point. As a result, only the most important assertion remains in the program point. Moreover, the redundancy degree is considered to screen redundant assertions for neighbouring program points. Experimental results show that in comparison with the Radish approach, the detection efficiency of F_Radish is about two times greater. Moreover, F_Radish reduces the benign detection ratio and improves the detection degree. It can avoid more unnecessary recovery overheads and detect more serious SDC than can Radish.