Exploring the Hidden Dimension in Graph Processing


Mingxing Zhang, Yongwei Wu, and Kang Chen, Tsinghua University; Xuehai Qian, University of Southern California; Xue Li and Weimin Zheng, Tsinghua University


Task partitioning of a graph-parallel system is traditionally considered equivalent to the graph partition problem. Such equivalence exists because the properties associated with each vertex/edge are normally considered indivisible. However, this assumption is not true for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. This feature opens a new dimension for task partitioning because a vertex could be divided and assigned to different nodes.

To explore this new opportunity, this paper presents 3D partitioning, a novel category of task partition algorithms that significantly reduces network traffic for certain MLDM applications. Based on 3D partitioning, we build a distributed graph engine CUBE. Our evaluation results show that CUBE outperforms state-of-the-art graph-parallel system PowerLyra by up to 4:7x (up to 7:3x speedup against PowerGraph).

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@inproceedings {199311,
author = {Mingxing Zhang and Yongwei Wu and Kang Chen and Xuehai Qian and Xue Li and Weimin Zheng},
title = {Exploring the Hidden Dimension in Graph Processing},
booktitle = {12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)},
year = {2016},
isbn = {978-1-931971-33-1},
address = {Savannah, GA},
pages = {285--300},
url = {https://www.usenix.org/conference/osdi16/technical-sessions/presentation/zhang-mingxing},
publisher = {USENIX Association},
month = nov,

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