Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling. Sampling is an embarrassingly parallel problem and may appear to lend itself to GPU acceleration, but the irregularity of graphs makes it hard to use GPU resources effectively. This paper presents NextDoor, a system designed to effectively perform graph sampling on GPUs. NextDoor employs a new approach to graph sampling that we call transit-parallelism, which allows load balancing and caching of edges. NextDoor provides end-users with a high-level abstraction for writing a variety of graph sampling algorithms. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.
翻译：代表式学习算法自动学习数据的特点。 图形数据的一些代表式学习算法, 如 DeepWalk、 node2vec 和 GraphSAGE, 样本图形以制作适合培训 DNN 的微型插头。 然而, 抽样时间可能是培训时间的相当一部分, 现有系统无法有效地平行取样。 取样是一个令人尴尬的平行问题, 似乎有助于 GPU 加速, 但图形的不规则性使得它很难有效地使用 GPU 资源。 本文展示了“ 下Door ”, 这个系统旨在有效地在 GPUs上进行图形取样。 下个 Door 采用了一种新的图表取样方法, 我们称之为“ 中转- 平行 ”, 从而可以平衡负荷和缓冲边缘 。 下一个 Door 向终端用户提供高层次的抽象数据, 用于撰写各种图形取样算法。 我们实施了几个图形抽样应用程序, 并显示“ 下 Door” 运行它们数量级比现有系统要快。
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