Accelerating Graph Sampling for Graph Machine Learning using GPUs

• URL: http://arxiv.org/abs/2009.06693v4
• Date: Tue, 11 May 2021 00:57:02 GMT
• Title: Accelerating Graph Sampling for Graph Machine Learning using GPUs
• Authors: Abhinav Jangda, Sandeep Polisetty, Arjun Guha, Marco Serafini
• Abstract summary: NextDoor is a system designed to perform graph sampling on GPU resources. NextDoor employs a new approach to graph sampling that we call transit-parallelism. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.
• Score: 2.9383911860380127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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