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Modern advancements in large-scale machine learning would be impossiblewithout the paradigm of data-parallel distributed computing. Since distributedcomputing with large-scale models imparts excessive pressure on communicationchannels, significant recent research has been directed toward co-designingcommunication compression strategies and training algorithms with the goal ofreducing communication costs. While pure data parallelism allows better datascaling, it suffers from poor model scaling properties. Indeed, compute nodesare severely limited by memory constraints, preventing further increases inmodel size. For this reason, the latest achievements in training giant neuralnetwork models also rely on some form of model parallelism. In this work, wetake a closer theoretical look at Independent Subnetwork Training (IST), whichis a recently proposed and highly effective technique for solving theaforementioned problems. We identify fundamental differences between IST andalternative approaches, such as distributed methods with compressedcommunication, and provide a precise analysis of its optimization performanceon a quadratic model.

Towards a Better Theoretical Understanding of Independent Subnetwork Training