GPUs have emerged as general-purpose accelerators in high-performance computing (HPC) and scientific applications. However, the reliability characteristics of GPU applications have not been investigated in depth. While error propagation has been extensively investigated for non-GPU applications, GPU applications have a very different programming model which can have a significant effect on error propagation in them. We perform an empirical study to understand and characterize error propagation in GPU applications. We build a compilerbased fault-injection tool for GPU applications to track error propagation, and define metrics to characterize propagation in GPU applications. We find GPU applications exhibit significant error propagation for some kinds of errors, but not others, and the behaviour is highly application specific. We observe the GPUCPU interaction boundary naturally limits error propagation in these applications compared to traditional non-GPU applications. We also formulate various guidelines for the design of faulttolerance mechanisms in GPU applications based on our results.