Gradient Coding
classification
📊 stat.ML
cs.DCcs.ITcs.LGmath.ITstat.CO
keywords
codinggradientstragglersacrossamazonapproachesbaselineblocks
pith:ZAJCWFKD Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{ZAJCWFKD}
Prints a linked pith:ZAJCWFKD badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original 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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.