GraphMP: I/O-Efficient big graph analytics on a single commodity machine

• Main Authors: SUN, Peng, WEN, Yonggang, TA, Nguyen Binh Duong, XIAO, Xiaokui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Graph Processing; Big Data; Parallel Computing; Vertex-Centric Programming Model; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4847; https://ink.library.smu.edu.sg/context/sis_research/article/5850/viewcontent/GraphMP__PV.pdf
• Institution: Singapore Management University

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform existing single-machine out-of-core systems such as GraphChi, X-Stream and GridGraph by up to 30, and can be as highly competitive as distributed graph engines like Pregel+, PowerGraph and Chaos.