Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:59705979, 2018.
Abstract
One of the most widely used optimization methods for largescale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD). However, a key issue that arises here is that of delayed gradients: when a “worker” node asynchronously contributes a gradient update to the “master”, the global model parameter may have changed, rendering this information stale. In massively parallel computing grids, these delays can quickly add up if the computational throughput of a node is saturated, so the convergence of DASGD is uncertain under these conditions. Nevertheless, by using a judiciously chosen quasilinear stepsize sequence, we show that it is possible to amortize these delays and achieve global convergence with probability 1, even when the delays grow at a polynomial rate. In this way, our results help reaffirm the successful application of DASGD to largescale optimization problems.
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