Towards Building Error Resilient GPGPU Applications
GPUs (Graphics Processing Units) have gained wide adoption as accelerators for general purpose computing. They are widely used in error-sensitive applications, i.e. General Purpose GPU (GPGPU) applications However, the reliability implications of using GPUs are unclear. This paper presents a fault injection study to investigate the end-to-end reliability characteristics of GPGPU applications. The investigation showed that 8% to 40% of the faults result in Silent Data Corruption (SDC). To reduce the percentage of SDCs, the authors propose heuristics to selectively protect specific elements of the application and design error detectors based on heuristics. They evaluate the efficacy of the detectors in reducing SDCs and measure performance overheads of the detectors.