3 citations · 8 across the 5 of their papers we have counts for
4 papers · 1 filter
SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
Xin Huang, Weipeng Zhuo, Minh Phu Vuong +4
Recently, distributed GNN training frameworks, such as DistDGL and PyG, have been developed to enable training GNN models on large graphs by leveraging multiple GPUs in a distribut…
cuSLINK: Single-linkage Agglomerative Clustering on the GPU
Corey J. Nolet, Divye Gala, Alex Fender +6
In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only space and uses a parameter to trade…
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
Xin Huang, Jongryool Kim, Bradley Rees +1
Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast an…
GPU Semiring Primitives for Sparse Neighborhood Methods
Corey J. Nolet, Divye Gala, Edward Raff +4
High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typ…