Characterizing and Understanding Defense Methods for GNNs on GPUs

Graph neural networks (GNNs) are widely deployed in many vital fields, but suffer from adversarial attacks, which seriously compromise the security in these fields. Plenty of defense methods have been proposed to mitigate the impact of these attacks, however, they have introduced extra time-consuming stages into the execution of GNNs. These extra stages need to be accelerated because the end-to-end acceleration is essential for GNNs to achieve fast development and deployment. To disclose the performance bottlenecks, execution patterns, execution semantics, and overheads of the defense methods for GNNs, we characterize and explore these extra stages on GPUs. Given the characterization and exploration, we provide several useful guidelines for both software and hardware optimizations to accelerate the defense methods for GNNs.