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05/24/2021
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
A graph neural network (GNN) enables deep learning on structured graph d...
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05/10/2019
Priority-based Parameter Propagation for Distributed DNN Training
Data parallel training is widely used for scaling distributed deep neura...
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06/11/2017
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
Deep learning models can take weeks to train on a single GPU-equipped ma...
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12/19/2015
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines
Deep learning (DL) has achieved notable successes in many machine learni...
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12/04/2014
LightLDA: Big Topic Models on Modest Compute Clusters
When building large-scale machine learning (ML) programs, such as big to...
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10/29/2014
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
As Machine Learning (ML) applications increase in data size and model co...
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12/30/2013
Consistent Bounded-Asynchronous Parameter Servers for Distributed ML
In distributed ML applications, shared parameters are usually replicated...
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12/30/2013