  Over the most recent years, quantized graph neural network (QGNN) attracts
lots of research and industry attention due to its high robustness and low
computation and memory overhead. Unfortunately, the performance gains of QGNN
have never been realized on modern GPU platforms. To this end, we propose the
first Tensor Core (TC) based computing framework, QGTC, to support any-bitwidth
computation for QGNNs on GPUs. We introduce a novel quantized low-bit
arithmetic design based on the low-bit data representation and bit-decomposed
computation. We craft a novel TC-tailored CUDA kernel design by incorporating
3D-stacked bit compression, zero-tile jumping, and non-zero tile reuse
technique to improve the performance systematically. We incorporate an
effective bandwidth-optimized subgraph packing strategy to maximize the
transferring efficiency between CPU host and GPU device. We integrate QGTC with
Pytorch for better programmability and extensibility. Extensive experiments
demonstrate that QGTC achieves on average 2.7x speedup compared with the
state-of-the-art Deep Graph Library framework across diverse settings.
