Understanding the Effects of Permanent Faults in GPU's Parallelism Management and Control Units

Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years, exposing the hardware to aging (i.e., permanent faults arising after the end-of-manufacturing test). Hence, techniques to assess permanent fault impacts in GPUs are strongly required, especially in safety-critical domains. This paper presents a method to evaluate permanent faults in the GPU's scheduler and control units, together with the first figures to quantify these effects. We inject $5.83\times 10^{5}$ permanent faults in the gate-level units of a GPU model. Then, we map the observed error categories as software errors by instrumenting 13 applications and two convolutional neural networks, injecting more than $1.65\times 10^{5}$ permanent errors (1,000 errors per application), reducing evaluation times from several years to hundreds of hours. Our results highlight that faults in GPU parallelism management units impact software execution parameters. Moreover, errors in resource management or instructions codes hang the code, while 45% of errors induce silent data corruption.