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

Large matrix multiplications are central to large-scale machine learning applications. These
operations are often carried out on a distributed computing platform with a master server and …

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 …

A distributed computing scenario is considered, where the computational power of a set of
worker nodes is used to perform a certain computation task over a dataset that is dispersed …

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 …

Polynomial based methods have recently been used in several works for mitigating the
effect of stragglers (slow or failed nodes) in distributed matrix computations. For a system …

One of the major challenges in using distributed learning to train complicated models with
large data sets is to deal with stragglers effect. As a solution, coded computation has been …

We propose a class of codes called random Khatri-Rao-Product (RKRP) codes for
distributed matrix multiplication in the presence of stragglers. The main advantage of the …

We consider the problem of coded computing, where a computational task is performed in a
distributed fashion in the presence of adversarial workers. We propose techniques to break …

The current big data era routinely requires the processing of large-scale data on massive
distributed computing clusters. In these applications, data sets are often so large that they …