Abstract: Evaluating Error Resiliency of GPGPU Applications

We present a preliminary evaluation of error-resilience of GPGPU applications. We find that, compared to CPUs, these platforms lead to a higher rate of silent data corruption a major concern since these errors are not flagged at runtime and often remain latent. We also find that out-of-bound memory accesses are the most critical reason of crashes. In the future, we will first focus on techniques to reduce frequency of silent data corruption, as this is critical to most HPC applications.