Graph neural networks (GNNs) have been proven to have increasingly widespread applications in the real world. In the mainstream mini-batch training mode, multiple cache-based GNN training acceleration systems have been proposed because of the possibility of selecting the same vertex multiple times during the sampling process. However, on ultra-large scale graphs, especially those exhibiting power-law characteristics, these systems are difficult to fully utilize the distribution characteristics of cached data, which limits training performance. To this end, we propose TAC, a GNN training acceleration system that fully exploits the distribution characteristics of cached data to optimize both data transmission and computational efficiency. Specifically, we have designed a data affinity optimization algorithm that significantly enhances the locality of cache access. Secondly, an adaptive sparse matrix operator for sparsity perception is proposed, which dynamically selects the optimal computing mode based on the location of data. Finally, we have constructed a fine-grained training pipeline that maximizes system parallelism by hiding the sampling and computation. The experimental results show that TAC significantly outperforms existing state-of-the-art cache acceleration systems on multiple benchmark datasets, demonstrating higher training efficiency.

Full-batch Graph Neural Network (GNN) training is indispensable for interdisciplinary applications. Although fullbatch training has advantages in convergence accuracy and speed, it still faces challenges such as severe load imbalance and high communication traffic overhead. In order to address these challenges, we propose ParGNN, an efficient full-batch training system for GNNs, which adopts a profiler-guided adaptive load balancing method along with graph over-partition to alleviate load imbalance. Based on the over-partition results, we present a subgraph pipeline algorithm to overlap communication and computation while maintaining the accuracy of GNN training. Extensive experiments demonstrate that ParGNN can not only obtain the highest accuracy but also reach the preset accuracy in the shortest time. In the end-to-end experiments performed on the four datasets, ParGNN outperforms the two state-of-theart full-batch GNN systems, PipeGCN and DGL, achieving the highest speedup of $2.7 \times$ and $21.8 \times$ times respectively.

Full-batch graph neural network (GNN) training is essential for interdisciplinary applications. Large-scale graph data is usually divided into subgraphs and distributed across multiple compute units to train GNN. The state-of-the-art load balancing method based on direct graph partition is too rough to effectively achieve true load balancing on GPU clusters. We propose ParGNN, which employs a profiler-guided load balance workflow in conjunction with graph repartition to alleviate load imbalance and minimize communication traffic. Experiments have verified that ParGNN has the capability to scale to larger clusters.