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More than 70% of cloud computing is paid for but sits idle. A large fractionof these idle compute are cheap CPUs with few cores that are not utilizedduring the less busy hours. This paper aims to enable those CPU cycles to trainheavyweight AI models. Our goal is against mainstream frameworks, which focuson leveraging expensive specialized ultra-high bandwidth interconnect toaddress the communication bottleneck in distributed neural network training.This paper presents a distributed model-parallel training framework thatenables training large neural networks on small CPU clusters with low Internetbandwidth. We build upon the adaptive sparse training framework introduced bythe SLIDE algorithm. By carefully deploying sparsity over distributed nodes, wedemonstrate several orders of magnitude faster model parallel training thanHorovod, the main engine behind most commercial software. We show that withreduced communication, due to sparsity, we can train close to a billionparameter model on simple 4-16 core CPU nodes connected by basic low bandwidthinterconnect. Moreover, the training time is at par with some of the besthardware accelerators.

Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity