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Found 5 papers, 4 papers with code
Cooperative Minibatching in Graph Neural Networks
Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive.
Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
It is designed to be a direct replacement for Neighbor Sampling (NS) with the same fanout hyperparameter while sampling up to 7 times fewer vertices, without sacrificing quality.
More Recent Advances in (Hyper)Graph Partitioning
In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms.
MG-GCN: Scalable Multi-GPU GCN Training Framework
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more.
On Symmetric Rectilinear Matrix Partitioning
More specifically, in this work, we address the problem of symmetric rectilinear partitioning of a square matrix.
Data Structures and Algorithms
