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SC20 Virtual Platform
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
Event Type
Paper
Tags
Machine Learning, Deep Learning and Artificial Intelligence
Memory Optimization
Scalable Computing
Registration Categories
TP
TimeTuesday, 17 November 20201pm - 1:30pm EDT
LocationTrack 4
DescriptionThe dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a viable approach to lessen the memory pressure issue, significant modification of the source code and considerations for algorithms are 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 on the concurrency analysis of out-of-core training behavior, and derive a strategy that combines layer swapping and redundant recomputing. We achieve an average of 1.52x speedup in six different models over the state-of-the-art out-of-core methods. We also introduce the first method to solve the challenging problem of out-of-core multi-node training by carefully pipelining gradient exchanges and performing the parameter updates on the host. Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g., Megatron-LM and Turning-NLG.
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