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WolfGraph: The edge-centric graph processing on GPU

机译：WolfGraph：GPU上以边缘为中心的图形处理

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摘要

There is the significant interest nowadays in developing the frameworks for parallelizing the processing of large graphs such as social networks, web graphs, etc. The work has been proposed to parallelize the graph processing on clusters (distributed memory), multicore machines (shared memory) and GPU devices. Most existing research on GPU-based graph processing employs the vertex-centric processing model and the Compressed Sparse Row (CSR) form to store and process a graph. However, they suffer from irregular memory access and load imbalance in GPU, which hampers the full exploitation of GPU performance. In this paper, we present WolfGraph, a GPU-based graph processing framework that addresses the above problems. WolfGraph adopts the edge-centric processing, which iterates over the edges rather than vertices. The data structure and graph partition in WolfGraph are carefully crafted so as to minimize the graph pre-processing and allow the coalesced memory access. WolfGraph fully utilizes the GPU power by processing all edges in parallel. We also develop a new method, called Concatenated Edge List (CEL), to process a graph that is bigger than the global memory of GPU. WolfGraph allows the users to define their own graph-processing methods and plug them into the WolfGraph framework. Our experiments show that WolfGraph achieves 7-8x speedup over GraphChi and X-Stream when processing large graphs, and it also offers 65% performance improvement over the existing GPU-based, vertex-centric graph processing frameworks, such as Gunrock.
机译：现在在开发用于并行化大图形的框架时，有重大兴趣，如社交网络，Web图等的大图形处理。已经提出了工作，并将图形处理并行化集群（分布式存储器），多核机器（共享存储器）和GPU设备。基于GPU的图形处理的大多数现有研究采用了顶点的处理模型和压缩稀疏行（CSR）表单来存储和处理图形。然而，它们在GPU中遭受了不规则的内存访问和负载不平衡，妨碍了GPU性能的完全开发。在本文中，我们展示了沃尔夫图，一种基于GPU的图形处理框架，用于解决上述问题。 Wolfpraph采用以边缘为中心的处理，它迭代边缘而不是顶点。 WolfGraph中的数据结构和图形分区被仔细制作，以最小化图形预处理并允许聚结的内存访问。沃尔夫图通过处理所有边缘并行处理GPU功率。我们还开发了一种新的方法，称为连接边缘列表（CEL），以处理比GPU的全局内存更大的图表。 WolfPraph允许用户定义自己的图形处理方法并将其插入WolfGraph框架。我们的实验表明，在处理大图时，沃尔夫图在Graphichi和X流中实现了7-8倍的加速，并且它还通过现有的基于GPU的顶点的顶点图形处理框架提供65％的性能改进，例如Gunrock。

著录项

  • 来源
    《Future generation computer systems》 |2020年第10期|552-569|共18页
  • 作者

    Huanzhou Zhu; Ligang He; Matthew Leeke; Rui Mao;

  • 作者单位

    Department of Computing Imperial College London United Kingdom of Great Britain and Northern Ireland;

    Department of Computer Science University of Warwick United Kingdom of Great Britain and Northern Ireland;

    Department of Computer Science University of Warwick United Kingdom of Great Britain and Northern Ireland;

    College of Computer Science and Software Engineering Shenzhen University China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    GPGPU; Graph processing; CUDA; Parallel processing;

    机译：GPGPU;图形处理;CUDA;并行处理;

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