SUGAR: Speeding Up GPGPU Application Resilience Estimation with Input Sizing

As Graphics Processing Units (GPUs) are becoming a de facto solution for accelerating a wide range of applications, their reliable operation is becoming increasingly important. One of the major challenges in the domain of GPU reliability is to accurately measure GPGPU application error resilience. This challenge stems from the fact that a typical GPGPU application spawns a huge number of threads and then utilizes a large amount of potentially unreliable compute and memory resources available on the GPUs. As the number of possible fault locations can be in the billions, evaluating every fault and examining its effect on the application error resilience is impractical. Application resilience is evaluated via extensive fault injection campaigns based on sampling of an extensive fault site space. Typically, the larger the input of the GPGPU application, the longer the experimental campaign.