GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine

TL;DR

GraphMP is a single-machine big graph processing system that reduces disk I/O overhead through innovative computation models, selective scheduling, and edge caching, outperforming existing systems significantly.

Contribution

The paper introduces GraphMP, a novel semi-external-memory graph processing system with three key techniques to improve efficiency on large-scale graphs.

Findings

GraphMP outperforms GraphChi, X-Stream, and GridGraph by up to 54.5x.

The system effectively handles billion-vertex graphs.

Low disk I/O overhead achieved through new computation and caching strategies.

Abstract

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 state-of-the-art systems such as GraphChi, X-Stream and GridGraph by 31.6x, 54.5x and 23.1x respectively, when running popular graph applications on a billion-vertex graph.