HNGraph: Parallel Graph Processing in Hybrid Memory Based NUMA Systems

Abstract

Hybrid memories exacerbate the asymmetry of memory access latencies in Non-Uniform Memory Access (NUMA) systems due to the vast performance gap between DRAM and Nonvolatile Memory (NVM). Since most graph processing systems have not considered the memory heterogeneity of NUMA nodes, they have sub-optimal performance due to improper data placement and access strategies. This paper proposes HNGraph, a graph processing framework for hybrid memory based NUMA systems. It mainly focuses on performance improvement by reducing random accesses to both local and remote NVM nodes. First, HNGraph assembles most random memory accesses in DRAM by exploiting a degree-aware partitioning strategy, which distributes high-degree and low-degree vertices to DRAM and NVM nodes, respectively. Second, we propose an adaptive graph processing model, which uses a hybrid inter-node communication mechanism to adapt to the asymmetric access latency between NVM and DRAM nodes. In DRAM nodes, we exploit a message passing communication model for remote random NVM updates. In NVM nodes, we use shared memory primitives to access remote DRAM directly. We evaluate the performance of HNGraph using different graph algorithms on typical datasets. Experimental results show that HNGraph can improve the application performance by 43.8% and 30.6% on average compared with the state-of-the-art graph processing systems GBBS and Polymer, respectively.