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The Ethereal
Gradient Coding
December 10, 2016 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis
arXiv ID
1612.03301
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.DC,
cs.IT,
cs.LG,
stat.CO
Citations
78
Venue
arXiv.org
Last Checked
6 months ago
Abstract
We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for Synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and generalization error.
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