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In this paper, we focus on approaches to parallelizing stochastic gradientdescent (SGD) wherein data is farmed out to a set of workers, the results ofwhich, after a number of updates, are then combined at a central master node.Although such synchronized SGD approaches parallelize well in idealizedcomputing environments, they often fail to realize their promised computationalacceleration in practical settings. One cause is slow workers, termedstragglers, who can cause the fusion step at the master node to stall, whichgreatly slowing convergence. In many straggler mitigation approaches workcompleted by these nodes, while only partial, is discarded completely. In thispaper, we propose an approach to parallelizing synchronous SGD that exploitsthe work completed by all workers. The central idea is to fix the computationtime of each worker and then to combine distinct contributions of all workers.We provide a convergence analysis and optimize the combination function. Ournumerical results demonstrate an improvement of several factors of magnitude incomparison to existing methods.

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