Long-Term Reliability Management For Multitasking GPGPUs

This paper proposes long-term reliability management for spatial multitasking GPU architectures. Specifically, we focus on electromigration (EM)-induced long-term failure of the GPU's power delivery network. A distributed power delivery network model at functional unit granularity is developed and used for our EM analysis of GPU architectures. We use a recently proposed physics-based EM reliability model and consider the EM-induced time-to-failure at the GPU system level as a reliability resource. For GPU scheduling, we mainly focus on spatial multitasking, which allows GPU computing resources to be partitioned among multiple applications. We find that the existing reliability-agnostic thread block scheduler for spatial multitasking is effective in achieving high GPU utilization, but poor reliability. We develop and implement a long-term reliability-aware thread block scheduler in GPGPU-Sim, and compare it against existing reliability-agnostic scheduler. We evaluate several use cases of spatial multitasking and find that our proposed scheduler achieves up to 30% improvement in long-term reliability.