GraphRing: an HMC-ring based graph processing framework with optimized data movement

Due to the irregular memory access and high bandwidth demanding, graph processing is usually inefficient on conventional computer architectures. The recent development of the processing-in-memory (PIM) technique such as hybrid memory cube (HMC) has provided a feasible design direction for graph processing accelerators. Although PIM provides high internal bandwidth, inter-node memory access is inevitable in large-scale graph processing, which greatly affects the performance. In this paper, we propose an HMC-based graph processing framework, GraphRing. GraphRing is a software-hardware codesign framework that optimizes inter-HMC communication. It contains a regularity- and locality-aware graph execution model and a ring-based multi-HMC architecture. The evaluation results based on 5 graph datasets and 4 graph algorithms show that GraphRing achieves on average 2.14× speedup and 3.07× inter-HMC communication energy saving, compared with GraphQ, a state-of-the-art graph processing architecture.