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Survey on Large Scale Neural Network Training


Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models don't fit one GPU device or can be trained using only a small per-GPU batch size. This survey provides a systematic overview of the approaches that enable more efficient DNNs training. We analyze techniques that save memory and make good use of computation and communication resources on architectures with a single or several GPUs. We summarize the main categories of strategies and compare strategies within and across categories. Along with approaches proposed in the literature, we discuss available implementations.
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hal-03952171 , version 1 (23-01-2023)



Julia Gusak, Daria Cherniuk, Alena Shilova, Alexandr Katrutsa, Daniel Bershatsky, et al.. Survey on Large Scale Neural Network Training. IJCAI-ECAI 2022 - 31st International Joint Conference on Artificial Intelligence, Jul 2022, Vienna, Austria. pp.5494-5501, ⟨10.24963/ijcai.2022/769⟩. ⟨hal-03952171⟩
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