GraphRing

Abstract

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.