  Graph Convolutional Networks (GCNs) are pivotal in extracting latent
information from graph data across various domains, yet their acceleration on
mainstream GPUs is challenged by workload imbalance and memory access
irregularity. To address these challenges, we present Accel-GCN, a GPU
accelerator architecture for GCNs. The design of Accel-GCN encompasses: (i) a
lightweight degree sorting stage to group nodes with similar degree; (ii) a
block-level partition strategy that dynamically adjusts warp workload sizes,
enhancing shared memory locality and workload balance, and reducing metadata
overhead compared to designs like GNNAdvisor; (iii) a combined warp strategy
that improves memory coalescing and computational parallelism in the column
dimension of dense matrices.
  Utilizing these principles, we formulated a kernel for sparse matrix
multiplication (SpMM) in GCNs that employs block-level partitioning and
combined warp strategy. This approach augments performance and multi-level
memory efficiency and optimizes memory bandwidth by exploiting memory
coalescing and alignment. Evaluation of Accel-GCN across 18 benchmark graphs
reveals that it outperforms cuSPARSE, GNNAdvisor, and graph-BLAST by factors of
1.17 times, 1.86 times, and 2.94 times respectively. The results underscore
Accel-GCN as an effective solution for enhancing GCN computational efficiency.
