Hauberk: Silent Data Corruption Error Detector for GPGPU

High performance and relatively low cost of GPU-based platforms provide an attractive alternative for general purpose high performance computing (HPC). However, the emerging HPC applications have usually stricter output correctness requirements than typical GPU applications (i.e., 3D graphics). This paper first analyzes the error resiliency of GPGPU platforms using a fault injection tool we have developed for commodity GPU devices. On average, 16-33% of injected faults cause silent data corruption (SDC) errors in the HPC programs executing on GPU. This SDC ratio is significantly higher than that measured in CPU programs (<2.3%). In order to tolerate SDC errors, customized error detectors are strategically placed in the source code of target GPU programs so as to minimize performance impact and error propagation and maximize recoverability. The presented Hauberk technique is deployed in seven HPC benchmark programs and evaluated using a fault injection. The results show a high average error detection coverage (~87%) with a small performance overhead (~15%). This is a practice talk of our IPDPS 2011 paper.