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$n$

, and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of

$\\log n$

. For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction

$\\alpha $

of the data matrix can be cached at each worker, and

$n$

is the number of workers, coded shuffling reduces the communication cost by a factor of

$\\left({\\alpha + \\frac {1}{n}}\\right)\\gamma (n)$

compared with uncoded shuffling, where

$\\gamma (n)$

is the ratio of the cost of unicasting

$n$

messages to

$n$

users to multicasting a common message (of the same size) to

$n$

users. For instance,

$\\gamma (n) \\simeq n$

if multicasting a message to

$n$

users is as cheap as unicasting a message to one user. 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Speeding Up Distributed Machine Learning Using Codes 论文

Speeding Up Distributed Machine Learning Using Codes · 相关事件