Modeling and characterizing GPGPU reliability in the presence of soft errors

Abstract

Abstract The general-purpose computing on graphic processing units (GPGPUs) becomes increasingly popular due to its high computational throughput for data parallel applications. Modern GPU architectures have limited capability for error detection and fault tolerance since they are originally designed for graphics processing. However, the rigorous execution correctness is required for general-purpose applications, which makes reliability a growing concern in the GPGPU architecture design. With CMOS processing technologies continuously scaling down to the nano-scale, on-chip soft error rate (SER) has been predicted to increase exponentially. GPGPUs with hundreds of cores integrated into a single chip are prone to manifest high SER. This paper explores a first step to model and characterize GPGPU reliability in light of soft errors. We develop GPGPU-SODA (GPGPU SOftware Dependability Analysis), a framework to estimate the soft-error vulnerability of GPGPU microarchitecture. By using GPGPU-SODA, we observe that several microarchitecture structures in GPGPUs exhibit high soft-error susceptibility, and the structure vulnerability is sensitive to the workload characteristics (e.g. branch divergences, memory access pattern). We further investigate the impact of several architectural optimizations on GPU soft-error robustness. For example, we find that increasing the number of threads supported by GPU significantly affects the GPGPU soft-error robustness. However, changing the warp scheduling policy has little impact on the structure vulnerability. The observations made in this study provide designers the useful guidance to build resilient GPGPUs: a comprehensive resiliency solution for GPGPUs should consider the entire GPGPU design instead of solely focusing on a particular structure.