GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware. (arXiv:2108.11571v2 [cs.LG] UPDATED)

Sampling is a critical operation in Graph Neural Network (GNN) training that
helps reduce the cost. Previous literature has explored improving sampling
algorithms via mathematical and statistical methods. However, there is a gap
between sampling algorithms and hardware. Without consideration of hardware,
algorithm designers merely optimize sampling at the algorithm level, missing
the great potential of promoting the efficiency of existing sampling algorithms
by leveraging hardware features. In this paper, we pioneer to propose a unified
programming model for mainstream …

algorithms arxiv gap hardware lg sampling