  The current BigData era routinely requires the processing of large scale data
on massive distributed computing clusters. Such large scale clusters often
suffer from the problem of "stragglers", which are defined as slow or failed
nodes. The overall speed of a computational job on these clusters is typically
dominated by stragglers in the absence of a sophisticated assignment of tasks
to the worker nodes. In recent years, approaches based on coding theory
(referred to as "coded computation") have been effectively used for straggler
mitigation. Coded computation offers significant benefits for specific classes
of problems such as distributed matrix computations (which play a crucial role
in several parts of the machine learning pipeline). The essential idea is to
create redundant tasks so that the desired result can be recovered as long as a
certain number of worker nodes complete their tasks. In this survey article, we
overview recent developments in the field of coding for straggler-resilient
distributed matrix computations.
