Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers

6 Oct 2018 Nuwan Ferdinand Stark Draper

In this paper, we focus on approaches to parallelizing stochastic gradient descent (SGD) wherein data is farmed out to a set of workers, the results of which, after a number of updates, are then combined at a central master node. Although such synchronized SGD approaches parallelize well in idealized computing environments, they often fail to realize their promised computational acceleration in practical settings... (read more)

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