Fundamental to any fault tolerance research is the definition of correct program execution. Traditionally, correct program's execution requires architectural state to be numerically perfect. However, in many cases, even if program execution is not 100% numerically correct, it may be completely acceptable if the answers can satisfy user's requirement. Hence, faults which have caused such numerically faulty execution are no longer intolerable.

The extent to which programs are more fault resilient at higher levels of abstraction is application dependent. Programs that produce inexact and/or approximate outputs can be very resilient at the application level. We call such programs soft computations, and we find they are common in multimedia workloads, as well as artificial intelligence (AI) workloads. Programs that compute exact numerical outputs offer less error resilience at the application level. However, we find all programs studied in this paper exhibit some enhanced fault resilience at the application level, including those that are traditionally considered exact computations-e.g., SPECInt CPU2000.

This report investigates definitions of program correctness that view correctness from the application's standpoint rather than the architecture's standpoint. Under application-level correctness, a program's execution is deemed correct as long as the result it produces is acceptable to the user. To quantify user satisfaction, we rely on application-level fidelity metrics that capture user-perceived program solution quality. We conduct a detailed fault susceptibility study that measures how much more fault resilient programs are when defining correctness at the application level compared to the architecture level. Our results show for 6 multimedia and AI benchmarks that 45.8% of architecturally incorrect faults are correct at the application level. For 3 SPECInt CPU2000 benchmarks, 17.6% of architecturally incorrect faults are correct at the application level. Based on our study on algorithmic properties for fault tolerance, we also investigate a lightweight fault recovery mechanism that exploits the relaxed requirements on numerical integrity provided by application-level correctness to reduce checkpoint cost. Our lightweight fault recovery mechanism successfully recovers 66.3% of program crashes in our multimedia and AI workloads, while incurring minimum runtime overhead.