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Found 6 papers, 3 papers with code
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 method for graph-based learning tasks.
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PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Notably, little is known regarding the convergence rate of GCN training with both stale features and stale feature gradients.
Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers
Deep neural networks (DNNs) have grown exponentially in size over the past decade, leaving only those who have massive datacenter-based resources with the ability to develop and train such models.
BDS-GCN: Efficient Full-Graph Training of Graph Convolutional Nets with Partition-Parallelism and Boundary Sampling
While it can be natural to leverage graph partition and distributed training for tackling this challenge, this direction has only been slightly touched on previously due to the unique challenge posed by the GCN structures, especially the excessive amount of boundary nodes in each partitioned subgraph, which can easily explode the required memory and communications for distributed training of GCNs.
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation.
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands.
