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arXiv · 2407.14544

Fast Iterative Graph Computing with Updated Neighbor States

Yijie Zhou·Shufeng Gong·Feng Yao·Hanzhang Chen·Song Yu·Pengxi Liu·Yanfeng Zhang·Ge Yu·Jeffrey Xu Yu
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Abstract

Enhancing the efficiency of iterative computation on graphs has garnered considerable attention in both industry and academia. Nonetheless, the majority of efforts focus on expediting iterative computation by minimizing the running time per iteration step, ignoring the optimization of the number of iteration rounds, which is a crucial aspect of iterative computation. We experimentally verified the correlation between the vertex processing order and the number of iterative rounds, thus making it possible to reduce the number of execution rounds for iterative computation. In this paper, we propose a graph reordering method, GoGraph, which can construct a well-formed vertex processing order effectively reducing the number of iteration rounds and, consequently, accelerating iterative computation. Before delving into GoGraph, a metric function is introduced to quantify the efficiency of vertex processing order in accelerating iterative computation. This metric reflects the quality of the processing order by counting the number of edges whose source precedes the destination. GoGraph employs a divide-and-conquer mindset to establish the vertex processing order by maximizing the value of the metric function. Our experimental results show that GoGraph outperforms current state-of-the-art reordering algorithms by 1.83x on average (up to 3.34x) in runtime.

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Yijie Zhou, Shufeng Gong, Feng Yao, Hanzhang Chen, Song Yu, Pengxi Liu, Yanfeng Zhang, Ge Yu, Jeffrey Xu Yu. 2024-07-16. Fast Iterative Graph Computing with Updated Neighbor States. https://arxiv.org/abs/2407.14544

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