SHMEMGraph: Efficient and Balanced Graph Processing Using One-Sided Communication

State-of-the-art synchronous graph processing frameworks face both inefficiency and imbalance issues that cause their performance to be suboptimal. These issues include the inefficiency of communication and the imbalanced graph computation/communication costs in an iteration. We propose to replace their conventional two-sided communication model with the one-sided counterpart. Accordingly, we design SHMEMGraph, an efficient and balanced graph processing framework that is formulated across a global memory space and takes advantage of the flexibility and efficiency of one-sided communication for graph processing. Through an efficient one-sided communication channel, SHMEMGraph utilizes the high-performance operations with RDMA while minimizing the resource contention within a computer node. In addition, SHMEMGraph synthesizes a number of optimizations to address both computation imbalance and communication imbalance. By using a graph of 1 billion edges, our evaluation shows that compared to the state-of-the-art Gemini framework, SHMEMGraph achieves an average improvement of 35.5% in terms of job completion time for five representative graph algorithms.

I. Introduction

Graph processing has emerged as a very attractive practice for data analytics. As the size of graph data keeps exploding, they become increasingly hard to be fit into a single machine [26], [8]. Therefore, distributed graph processing frameworks become the main focus for large-scale graph processing [21], [20], [10], [25], [24], [26], [7], [28]. In particular, Gemini [28] is a recent effort whose computation-centric design has improved previous works by at least an order of magnitude.