Characterizing and Exploiting Soft Error Vulnerability Phase Behavior in GPU Applications

System reliability has become a first-class design constraint. As the use of Graphics Processing Units (GPU) continues to increase in compute applications, including High Performance Computing (HPC) and safety-critical applications, so do the number of transient faults in GPUs. However, our understanding of the potential impact of transient fault propagation in GPU applications remains limited. This study shows that the resilience characteristics of GPU programs change significantly during program execution and these characteristics show repetitive, time-varying behavior. Interestingly, these repetitive, time-varying, resilience characteristics of GPU programs do not align or correlate well with the performance phases of GPU programs. Furthermore, this work discovers and validates that temporal changes in the vulnerability behavior during a kernel execution tends to coincide with changes in basic block execution paths. Finally, we demonstrate how these observations can be exploited to accelerate the fault injection campaigns for reliability assessment of GPU programs by an order of magnitude and open opportunities for designing other effective resilience mitigation strategies.