Accelerating graph sampling for graph machine learning using GPUs
Abhinav Jangda, Sandeep Polisetty, Arjun Guha, Marco Serafini · 2021
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph-SAGE, 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.