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Timing errors are a growing concern for system resilience as technology continues to scale. It is problematic to use low-fidelity errors such as single-bit flips to model realistic timing errors. We address the lack of holistic methodology and tool for evaluating resilience of applications against timing errors. The proposed technique is able to rapidly inject high-fidelity and configurable timing errors to applications at the instruction level. Our implementation has no runtime dependencies on proprietary tools, enabling full parallelism of error injection campaign. Furthermore, because an injection point may not generate an actual error for a particular application run, we propose an acceleration technique to maximize the likelihood of generating errors that contribute to the overall campaign with speedup up to 7X. With our tool, we show that realistic timing errors lead to distinct error profiles from those of radiation-induced errors at both the instruction level and the application level.

Strategies to detect, correct, or mitigate the impact of soft errors rely on errors injection experiments. For efficient evaluation, these experiments typically inject errors in software by sampling errors from a candidate distribution. Most often, these strategies randomly select and flip one bit in the output of an instruction. While single-bit flips may constitute a meaningful model for errors affecting hardware, the appropriateness of this model for software-based errors has not been studied. In this paper, we examine the manifestation of errors in the output registers due to errors affecting candidate instructions executed by floating-point arithmetic logic units (ALUs). We inject single-bit flips into the register-transfer level descriptions of floating-point ALUs and analyze the differences between anticipated and observed outputs when executing floating-point addition, subtraction, multiplication, and division. We choose the operands for these instructions randomly and from operands observed in five benchmarks. We observe a rich distribution of errors in the output and analyze their implications for software-based fault injection campaigns.