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05/28/2022
ByteComp: Revisiting Gradient Compression in Distributed Training
Gradient compression (GC) is a promising approach to addressing the comm...
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07/15/2021
MXDAG: A Hybrid Abstraction for Cluster Applications
Distributed applications, such as database queries and distributed train...
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06/11/2021
Efficient and Less Centralized Federated Learning
With the rapid growth in mobile computing, massive amounts of data and c...
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04/19/2021
Shufflecast: An Optical, Data-rate Agnostic and Low-Power Multicast Architecture for Next-Generation Compute Clusters
An optical circuit-switched network core has the potential to overcome t...
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03/28/2021
MergeComp: A Compression Scheduler for Scalable Communication-Efficient Distributed Training
Large-scale distributed training is increasingly becoming communication ...
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04/27/2018
