Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers

7 Aug 2018Emre OzfaturaDeniz GunduzSennur Ulukus

Distributed gradient descent (DGD) is an efficient way of implementing gradient descent (GD), especially for large data sets, by dividing the computation tasks into smaller subtasks and assigning to different computing servers (CSs) to be executed in parallel. In standard parallel execution, per-iteration waiting time is limited by the execution time of the straggling servers... (read more)

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