GraphMP: I/O-Efficient Big Graph Analytics on a Single Commodity Machine

TL;DR

GraphMP introduces a set of techniques to significantly reduce disk I/O overhead in single-machine big graph analytics, achieving performance comparable to distributed systems.

Contribution

GraphMP presents a novel vertex-centric sliding window model, selective scheduling, and compressed edge caching to improve single-machine graph processing efficiency.

Findings

GraphMP outperforms existing out-of-core systems by up to 51%.

GraphMP is as competitive as distributed graph engines.

The proposed techniques effectively reduce disk I/O overhead.

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 existing single-machine out-of-core systems such as GraphChi, X-Stream and GridGraph by up to 51, and can be as highly competitive as distributed graph engines like Pregel+, PowerGraph and Chaos.