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The dedicated memory of hardware accelerators can be insufficient to storeall weights and/or intermediate states of large deep learning models. Althoughmodel parallelism is a viable approach to reduce the memory pressure issue,significant modification of the source code and considerations for algorithmsare required. An alternative solution is to use out-of-core methods instead of,or in addition to, data parallelism. We propose a performance model based onthe concurrency analysis of out-of-core training behavior, and derive astrategy that combines layer swapping and redundant recomputing. We achieve anaverage of 1.52x speedup in six different models over the state-of-the-artout-of-core methods. We also introduce the first method to solve thechallenging problem of out-of-core multi-node training by carefully pipelininggradient exchanges and performing the parameter updates on the host. Our dataparallel out-of-core solution can outperform complex hybrid model parallelismin training large models, e.g. Megatron-LM and Turning-NLG.

Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA