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Codes are widely used in many engineering applications to offer <italic>robustness</italic> against <italic>noise</italic>. In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms&#x2014;straggler nodes, system failures, or communication bottlenecks&#x2014;but there has been little interaction cutting across codes, machine learning, and distributed systems. In this paper, we provide theoretical insights on how <italic>coded</italic> solutions can achieve significant gains compared with uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms: <italic>matrix multiplication</italic> and <italic>data shuffling</italic>. For matrix multiplication, we use codes to alleviate the effect of stragglers and show that if the number of homogeneous workers is <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of <inline-formula> <tex-math notation="LaTeX">$\log n$ </tex-math></inline-formula>. For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> of the data matrix can be cached at each worker, and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of workers, <italic>coded shuffling</italic> reduces the communication cost by a factor of <inline-formula> <tex-math notation="LaTeX">$\left({\alpha + \frac {1}{n}}\right)\gamma (n)$ </tex-math></inline-formula> compared with uncoded shuffling, where <inline-formula> <tex-math notation="LaTeX">$\gamma (n)$ </tex-math></inline-formula> is the ratio of the cost of unicasting <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> messages to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> users to multicasting a common message (of the same size) to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> users. For instance, <inline-formula> <tex-math notation="LaTeX">$\gamma (n) \simeq n$ </tex-math></inline-formula> if multicasting a message to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> users is as cheap as unicasting a message to one user. We also provide experimental results, corroborating our theoretical gains of the coded algorithms.

Speeding Up Distributed Machine Learning Using Codes