3 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
Accelerating Fast Fourier Transforms Using Hadoop and CUDA
Rostislav Tsiomenko, Bradley S. Rees
There has been considerable research into improving Fast Fourier Transform (FFT) performance through parallelization and optimization for specialized hardware. However, even with t…