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arXiv · 1707.02557

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

Peng Sun·Yonggang Wen·Ta Nguyen Binh Duong·Xiaokui Xiao
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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.

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Peng Sun, Yonggang Wen, Ta Nguyen Binh Duong, Xiaokui Xiao. 2017-07-09. GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine. https://arxiv.org/abs/1707.02557

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