Nezha: An Efficient Distributed Graph Processing System on Heterogeneous Hardware

Pengjie Cui, Haotian Liu, Dong Jiang, Bo Tang, Ye Yuan

Abstract: The growing scale of graph data across various applications demands efficient distributed graph processing systems. Despite the widespread use of the Scatter-Gather model for large-scale graph processing across distributed machines, the performance still can be significantly improved as the computation ability of each machine is not fully utilized and the communication costs during graph processing are expensive in the distributed environment. In this work, we propose a novel and efficient distributed graph processing system Nezha on heterogeneous hardware, where each machine is equipped with both CPU and GPU processors and all these machines in the distributed cluster are interconnected via Remote Direct Memory Access (RDMA). To reduce the communication costs, we devise an effective communication mode with a graphfriendly communication protocol in the graph-based RDMA communication adapter of Nezha. To improve the computation efficiency, we propose a multi-device cooperative execution mechanism in Nezha, which fully utilizes the CPU and GPU processors of each machine in the distributed cluster. We also alleviate the workload imbalance issue at inter-machine and intra-machine levels via the proposed workload balancer in Nezha. We conduct extensive experiments by running 4 widely-used graph algorithms on 5 graph datasets to demonstrate the superiority of Nezha over existing systems.