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Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation.
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Speeding up distributed machine learning using codes
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Arock: an algorithmic framework for asynchronous parallel coordinate updates
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Scaling distributed machine learning with the parameter server
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Improving distributed gradient descent using reed-solomon codes
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Coded computation over heterogeneous clusters
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Asynchronous coordinate descent under more realistic assumptions
Tao Sun, Robert Hannah, and Wotao Yin · 2017
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Gradient coding: Avoiding stragglers in distributed learning
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Coded distributed computing for inverse problems
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