Accelerating Dynamic Graph Analytics on GPUs
Accelerating Dynamic Graph Analytics on GPUs
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DOI：
10.14778/3151113.3151122
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
M. Sha;Yuchen Li;Bingsheng He;K. Tan
中科院分区：
文献类型：
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作者：
M. Sha;Yuchen Li;Bingsheng He;K. Tan
由于图形分析通常涉及计算密集型操作，因此GPU已被广泛用于加速处理。然而，在社交网络、网络安全和欺诈检测等许多应用中，它们的代表图经常演变，必须在GPU上重建图结构才能包含更新。因此，图的重构成为处理高速图流的瓶颈。本文提出了一种基于GPU的动态图形存储方案，以方便地支持现有的图形算法。此外，我们提出了并行更新算法来支持高效的流更新，以便维护的图立即可用于在GPU上进行高速分析处理。我们在大规模真实和合成数据集上的三个流媒体应用程序上的广泛实验证明了我们所提出的方法的优越性能。
As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform a rebuild of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper, we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore, we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.