An efficient high throughput system of hypergraph neural network under CPU-GPU heterogeneous environments

In recent years, graph neural networksGNNs) have gained significant attention for their exceptional ability to learn and reason with non-Euclidean data. However, real-world graph structures often involve complex higher-order relationships between vertices, typically represented as hypergraphs. To effectively capture these intricate features and underlying semantic information in hypergraphs, numerous hypergraph neural networkHGNN) models have been proposed. Despite the development of various software systems that leverage the high parallel computing power of GPUs to accelerate HGNN training, these systems still face challenges such as excessive redundant computationand frequent data communication leading to suboptimal GPU utilization. Moreover, these systems are limited to training HGNN models on relatively small-scale graphs. To address these issues, we observed that multiple hyperedge computations often repeatedly access and compute the identical vertex feature vectors due to significant topological overlap between hyperedges in hypergraphs when handling the HyperGNN models. Based on this observation, we propose RHGNN a redundancy-elimination HyperGNN training system tailored for CPU-GPU heterogeneous environments. RHGNN exploits the overlapping characteristics of hyperedges to optimize the computation and updating of hyperedge and vertex features, significantly reducing CPU-GPU data communication overhead. Specifically, RHGNN presents a novel hyperedge-centric redundancy elimination approach, enabling the system to load the feature vectors of vertices to optimize communication, allowing a single load operation to serve multiple hyperedge and vertex feature computations. In addition, RHGNN implements an efficient hyperdegree-aware hierarchy caching mechanism that prioritizes frequently accessed vertex feature vectors and intermediate results on the GPU, further reducing communication costs. To validate the effectiveness of RHGNN, we compared its performance against the state-of-the-art HGNN software systems DGL, PyG, and HyperGef. Experimental results demonstrate that RHGNN achieves a performance improvement of 2.5×∼3.4× over PyG, 2.1×∼3.1× over DGL, and 1.4×∼2.3× over HyperGef in HGNN model training, respectively.