3 citations · 3 across the 5 of their papers we have counts for
5 papers
Accelerating Sparse Matrix-Matrix Multiplication on GPUs with Processing Near HBMs
Shiju Li, Younghoon Min, Hane Yie +5
Sparse General Matrix-Matrix Multiplication (SpGEMM) is a fundamental operation in numerous scientific computing and data analytics applications, often bottlenecked by irregular me…
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
Xin Huang, Chul-Ho Lee
Graph neural networks (GNNs) are powerful tools for solving graph-related problems. Distributed GNN frameworks and systems enhance the scalability of GNNs and accelerate model trai…
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…
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…
GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals
Weipeng Zhuo, Ziqi Zhao, Ka Ho Chiu +4
We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most sa…