Warped-DMR: Light-weight Error Detection for GPGPU

General purpose graphics processing units (GPGPUs) are feature rich GPUs that provide general purpose computing ability with massive number of parallel threads. The massive parallelism combined with programmability made GPGPUs the most attractive choice in supercomputing centers. Unsurprisingly, most of the GPGPU-based studies have been focusing on performance improvement leveraging GPGPU´s high degree of parallelism. However, for many scientific applications that commonly run on supercomputers, program correctness is as important as performance. Few soft or hard errors could lead to corrupt results and can potentially waste days or even months of computing effort. In this research we exploit unique architectural characteristics of GPGPUs to propose a light weight error detection method, called Warped Dual Modular Redundancy (Warped-DMR). Warped-DMR detects errors in computation by relying on opportunistic spatial and temporal dual-modular execution of code. Warped-DMR is light weight because it exploits the underutilized parallelism in GPGPU computing for error detection. Error detection spans both within a warp as well as between warps, called intra-warp and inter-warp DMR, respectively. Warped-DMR achieves 96% error coverage while incurring a worst-case 16% performance overhead without extra execution units or programmer´s effort.