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Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer
Center for Reliable and High Performance Computing, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2011

@article{yim2011hauberk,

   title={Hauberk: Lightweight silent data corruption error detectors for gpgpu},

   author={Yim, K.S. and Iyer, R.},

   journal={In Proceedings of the 17th Humantech Thesis Prize (Also in IPDPS 2011)},

   year={2011}

}

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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%).
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