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Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU

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2 Author(s)
Imran S. Haque ; Dept. of Comput. Sci., Stanford Univ., Stanford, CA, USA ; Vijay S. Pande

Graphics processing units (GPUs) are gaining widespread use in high-performance computing because of their performance advantages relative to CPUs. However, the reliability of GPUs is largely unproven. In particular, current GPUs lack error checking and correcting (ECC) in their memory subsystems. The impact of this design has not been previously measured at a large enough scale to quantify soft error events. We present MemtestG80, our software for assessing memory error rates on NVIDIA graphics cards. Furthermore, we present a large-scale assessment of GPU error rate, conducted by running MemtestG80 on over 50,000 hosts on the Folding@home distributed computing network. Our control experiments on consumer-grade and dedicated-GPGPU hardware in a controlled environment found no errors. However, our survey on Folding@home finds that, in their installed environments, two-thirds of tested GPUs exhibit a detectable, pattern-sensitive rate of memory soft errors. We show that these errors persist after controlling for over clocking and environmental proxies for temperature, but depend strongly on board architecture.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on

Date of Conference:

17-20 May 2010