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Graph Processing on CPUs: A Survey

机译:CPU上的图形处理:调查

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摘要

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.
机译:在大数据时代,许多现实世界的数据可以自然地表示为图形。因此,可以将许多应用程序域建模为图形处理。图形处理,特别是顶点和边的数量在数十亿甚至数千亿个数量级的大规模图形的处理,已经引起了工业界和学术界的广泛关注。处理这样的大规模图形仍然是一个巨大的挑战。研究人员一直在寻找新的可能的解决方案。由于GPU中具有高度的并行性和较高的内存访问带宽,因此证明了利用GPU加速图形处理是一种很有前途的解决方案。本文调查了GPU上图形处理的关键问题,包括数据布局,内存访问模式,工作负载映射和特定的GPU编程。在本文中,我们总结了基于GPU的图形处理的最新研究,详细分析了现有的挑战,并探索了未来的研究机会。

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