research
∙
03/21/2022
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art...
share
research
∙
03/20/2022
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Graph Convolutional Networks (GCNs) is the state-of-the-art method for l...
research
∙
02/02/2022
Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers
Deep neural networks (DNNs) have grown exponentially in complexity and s...
research
∙
11/08/2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address s...
research
∙
11/08/2018
