1.9k citations · 2.9k across the 29 of their papers we have counts for
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Heta: Distributed Training of Heterogeneous Graph Neural Networks
Yuchen Zhong, Junwei Su, Chuan Wu +1
Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning performance in various…
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs
Yuchen Zhong, Guangming Sheng, Tianzuo Qin +3
Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support trainin…
Supporting Very Large Models using Automatic Dataflow Graph Partitioning
Minjie Wang, Chien-chin Huang, Jinyang Li
This paper presents Tofu, a system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow gr…
Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling
Minjie Wang, Chien-chin Huang, Jinyang Li
Deep learning systems have become vital tools across many fields, but the increasing model sizes mean that training must be accelerated to maintain such systems' utility. Current s…
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen, Mu Li, Yutian Li +7
MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends decl…