Accelerating Gossip SGD with Periodic Global Averaging
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Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient than the prevalent parallel SGD. However, its convergence rate is reversely proportional to quantity 1-β which measures the network connectivity. On large and sparse networks where 1-β→ 0, Gossip SGD requires more iterations to converge, which offsets against its communication benefit. This paper introduces Gossip-PGA, which adds Periodic Global Averaging into Gossip SGD. Its transient stage, i.e., the iterations required to reach asymptotic linear speedup stage, improves from Ω(β^4 n^3/(1-β)^4) to Ω(β^4 n^3 H^4) for non-convex problems. The influence of network topology in Gossip-PGA can be controlled by the averaging period H. Its transient-stage complexity is also superior to Local SGD which has order Ω(n^3 H^4). Empirical results of large-scale training on image classification (ResNet50) and language modeling (BERT) validate our theoretical findings.
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