  Graphics processing units (GPUs) are gaining widespread use in computational
chemistry and other scientific simulation contexts because of their huge
performance advantages relative to conventional CPUs. However, the reliability
of GPUs in error-intolerant applications is largely unproven. In particular, a
lack of error checking and correcting (ECC) capability in the memory subsystems
of graphics cards has been cited as a hindrance to the acceptance of GPUs as
high-performance coprocessors, but the impact of this design has not been
previously quantified.
  In this article we present MemtestG80, our software for assessing memory
error rates on NVIDIA G80 and GT200-architecture-based graphics cards.
Furthermore, we present the results of a large-scale assessment of GPU error
rate, conducted by running MemtestG80 on over 20,000 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 over cards 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 demonstrate that these errors persist after
controlling for overclocking and environmental proxies for temperature, but
depend strongly on board architecture.
