Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

This paper has two main contributions. First, we propose a novel coding technique-
Generalized PolyDot-for matrix-vector products that advances on existing techniques for …

The goal of coded distributed computation is to efficiently distribute a computation task, such
as matrix multiplication, N-linear computation, or multivariate polynomial evaluation, across …

We consider distributed gradient descent in the presence of stragglers. Recent work on
gradient coding and approximate gradient coding have shown how to add redundancy in …

Large-scale machine learning and data mining applications require computer systems to
perform massive matrix-vector and matrix-matrix multiplication operations that need to be …

Distributed matrix computations over large clusters can suffer from the problem of slow or
failed worker nodes (called stragglers) which can dominate the overall job execution time …

This article aims to give a comprehensive and rigorous review of the principles and recent
development of coding for large-scale distributed machine learning (DML). With increasing …