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It is well known that many optimization methods, including SGD, SAGA, and Accelerated SGD for over-parameterized models, do not scale linearly in the parallel setting. In this paper, we present a new version of block coordinate descent that solves this issue for a number of methods. The core idea is to make the sampling of coordinate blocks on each parallel unit independent of the others. Surprisingly, we prove that the optimal number of blocks to be updated by each of n units in every iteration is equal to m/n, where m is the total number of blocks. As an illustration, this means that when n=100 parallel units are used, 99% of work is a waste of time. We demonstrate that with m/n blocks used by each unit the iteration complexity often remains the same. Among other applications which we mention, this fact can be exploited in the setting of distributed optimization to break the communication bottleneck. Our claims are justified by numerical experiments which demonstrate almost a perfect match with our theory on a number of datasets.
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