As GPUs become more pervasive in both scalable high-performance computing systems and safety-critical embedded systems, evaluating and analyzing their resilience to soft errors caused by high-energy particle strikes will grow increasingly important. GPU designers must develop tools and techniques to understand the effect of these soft errors on applications. In this project we developed an error injection-based methodology and tool called SASSIFI to study the soft error resilience of massively parallel applications running on NVIDIA GPUs. Our approach uses a low-level assembly-language instrumentation tool called SASSI to profile and inject errors. SASSI provides efficiency by allowing instrumentation code to execute entirely on the GPU and provides the ability to inject into different architecture-visible state. For example, SASSIFI can inject errors in general-purpose registers, GPU memory, condition code registers, and predicate registers. SASSIFI can also inject errors into addresses and register indices. SASSIFI is publicly available on GitHub at https://github.com/NVlabs/sassifi.
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