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Found 7 papers, 3 papers with code
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest.
DGI: Easy and Efficient Inference for GNNs
In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution.
Seastar: vertex-centric programming for graph neural networks
Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering.
DGCL: an efficient communication library for distributed GNN training
Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics.
Elastic Deep Learning in Multi-Tenant GPU Clusters
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs).
