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HyperGef: A Framework Enabling Efficient Fusion for Hypergraph Neural Network on GPUs

Zhongming Yu · Guohao Dai · Shang Yang · Genghan Zhang · Hengrui Zhang · Feiwen Zhu · June Yang · Jishen Zhao · Yu Wang

Ballroom B - Position 39


Hypergraph Neural Network (HyperGNN) is an emerging type of Graph Neural Networks (GNNs) that can utilize hyperedges to model high-order relationships among vertices. Current GNN frameworks fail to fuse two message-passing steps from vertices to hyperedges and hyperedges to vertices, leading to high latency and redundant memory consumption. The following challenges need to be solved for efficient fusion in HyperGNNs: (1) Inefficient partition: hardware-efficient and workload-balanced partitions are required for parallel workers to process two consecutive message passing steps after fusion. (2) Workload-Agnostic Format: current data formats like Compressed Sparse Row (CSR) fail to represent a two-step computation workload. (3) Heavy writing conflicts: partitioning leads to heavy writing conflicts when updating the same vertex.To enable efficient fusion for HyperGNNs, we present HyperGef. HyperGef proposes an edge-split workload balance partition scheme to achieve higher efficiency and better workload balancing. To represent the workload after fusion and partition, HyperGef introduces a novel fusion workload-aware format. HyperGef also introduces a shared memory-aware grouping scheme to reduce writing conflicts. Extensive experiments demonstrate that our fused kernel outperforms the NVIDIA cuSPARSE kernel by 3.31x. By enabling efficient fusion for HyperGNNs, HyperGef achieves 2.25x to 3.99x end-to-end speedup on various HyperGNN models compared with state-of-the-art frameworks like DGL and PyG.

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