xBS-GNN: Accelerating Billion-Scale GNNTraining on FPGA
Yi-Chien Lin, Zhijie Xu, Viktor K. Prasanna · 2024
Graph Neural Networks (GNNs) have been successfully used in a variety of challenging application areas, including Electronic Design Automation and molecular property prediction, among others. However, training GNN models is time-consuming as it incurs a high volume of irregular data accessing due to its graph-structured input data; such a challenge is further exacerbated in real-world applications as they often involve training GNN models on large-scale graphs with over billions of edges. While several GNN accelerators have been proposed, most of them cannot scale to billion-scale graphs due to the limitation of memory capacity. To this end, we propose xBS-GNN, a novel accelerator optimized for billion-scale GNN training. xBS-GNN exploits the multi-level memory hierarchy on state-of-the-art FPGA-based systems to enable billion-scale GNN training. To achieve high training throughput, xBS-GNN jointly exploits several optimizations, including (1) a novel data placement policy optimized for GNN training, along with (2) a vertex-renaming technique and memory-efficient lookup table design for fast data retrieval, and (3) a feature quantization mechanism to reduce memory traffic. We evaluate xBS-GNN with a three-layer GCN model on three large datasets. xBS-GNN achieves up to 8.39× speedup over a widely-used GPU baseline and up to 5.13× speedup over a state-of-the-art GNN training accelerator. xBS-GNN also demonstrates high scalability on multi-FPGA platforms.