Recently, iterative graph algorithms are proposed to be handled by GPU-accelerated systems. However, in iterative graph processing, the parallelism of GPU is still underutilized by existing GPU-based solutions. In fact, because of the power-law property of the natural graphs, the paths between a small set of important vertices (e.g., high-degree vertices) play a more important role in iterative graph processing’s convergence speed. Based on this fact, for faster iterative graph processing on GPUs, this article develops a novel system, called AsynGraph , to maximize its data parallelism. It first proposes an efficient structure-aware asynchronous processing way . It enables the state propagations of most vertices to be effectively conducted on the GPUs in a concurrent way to get a higher GPU utilization ratio through efficiently handling the paths between the important vertices. Specifically, a graph sketch (consisting of the paths between the important vertices) is extracted from the original graph to serve as a fast bridge for most state propagations. Through efficiently processing this sketch more times within each round of graph processing, higher parallelism of GPU can be utilized to accelerate most state propagations. In addition, a forward-backward intra-path processing way is also adopted to asynchronously handle the vertices on each path, aiming to further boost propagations along paths and also ensure smaller data access cost. In comparison with existing GPU-based systems, i.e., Gunrock, Groute, Tigr, and DiGraph, AsynGraph can speed up iterative graph processing by 3.06–11.52, 2.47–5.40, 2.23–9.65, and 1.41–4.05 times, respectively.

中文翻译：

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异步图

最近，提出了由 GPU 加速系统处理的迭代图算法。然而，在迭代图处理中，GPU 的并行性仍然未被现有的基于 GPU 的解决方案充分利用。事实上，由于自然图的幂律性质，一小部分重要顶点（例如，高度顶点）之间的路径在迭代图处理的收敛速度中起着更重要的作用。基于这一事实，为了在 GPU 上进行更快的迭代图处理，本文开发了一种新颖的系统，称为异步图，以最大化其数据并行性。它首先提出了一种有效的结构感知异步处理方式. 它使大多数顶点的状态传播能够以并发的方式在GPU上有效地进行，通过有效地处理重要顶点之间的路径来获得更高的GPU利用率。具体来说，从原始图形中提取图形草图（由重要顶点之间的路径组成），作为大多数状态传播的快速桥梁。通过在每一轮图形处理中多次有效地处理该草图，可以利用 GPU 的更高并行性来加速大多数状态传播。此外，一个前向-后向路径内处理方式还采用异步处理每个路径上的顶点，旨在进一步促进沿路径的传播并确保更小的数据访问成本。与现有的基于 GPU 的系统，即 Gunrock、Groute、Tigr 和 DiGraph 相比，AsynGraph 可以将迭代图处理速度分别提高 3.06-11.52、2.47-5.40、2.23-9.65 和 1.41-4.05 倍。