Identifying SDC-Causing Instructions Based on Random Forests Algorithm.

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.