4 papers
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
Nikolai Merkel, Ruben Mayer, Volker Markl +1
Graph Neural Networks (GNNs) are widely used for learning on graph-structured data, but scaling GNN training to massive graphs remains challenging. To enable scalable distributed t…
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Nikolai Merkel, Pierre Toussing, Ruben Mayer +1
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative…
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Nikolai Merkel, Daniel Stoll, Ruben Mayer +1
Recently, graph neural networks (GNNs) have gained much attention as a growing area of deep learning capable of learning on graph-structured data. However, the computational and me…
Partitioner Selection with EASE to Optimize Distributed Graph Processing
Nikolai Merkel, Ruben Mayer, Tawkir Ahmed Fakir +1
For distributed graph processing on massive graphs, a graph is partitioned into multiple equally-sized parts which are distributed among machines in a compute cluster. In the last…