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Asynchronous Decentralized SGD with Quantized and Local Updates
Giorgi Nadiradze · Amirmojtaba Sabour · Peter Davies · Shigang Li · Dan Alistarh

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, but also introduces new challenges in terms of synchronization costs. To this end, several communication-reduction techniques, such as non-blocking communication, quantization, and local steps, have been explored in the decentralized setting. Due to the complexity of analyzing optimization in such a relaxed setting, this line of work often assumes \emph{global} communication rounds, which require additional synchronization. In this paper, we consider decentralized optimization in the simpler, but harder to analyze, \emph{asynchronous gossip} model, in which communication occurs in discrete, randomly chosen pairings among nodes. Perhaps surprisingly, we show that a variant of SGD called \emph{SwarmSGD} still converges in this setting, even if \emph{non-blocking communication}, \emph{quantization}, and \emph{local steps} are all applied \emph{in conjunction}, and even if the node data distributions and underlying graph topology are both \emph{heterogenous}. Our analysis is based on a new connection with multi-dimensional load-balancing processes. We implement this algorithm and deploy it in a super-computing environment, showing that it can outperform previous decentralized methods in terms of end-to-end training time, and that it can even rival carefully-tuned large-batch SGD for certain tasks.

Author Information

Giorgi Nadiradze (Institute of Science and Technology Austria)
Amirmojtaba Sabour (Sharif University of Technology, Sharif University of Technology)
Peter Davies (University of Surrey)
Shigang Li (Swiss Federal Institute of Technology)
Dan Alistarh (IST Austria & NeuralMagic)

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