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arXiv · 2211.05953

Breadth-First Pipeline Parallelism

Joel Lamy-Poirier
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Abstract

We introduce Breadth-First Pipeline Parallelism, a novel training schedule which optimizes the combination of pipeline and data parallelism. Breadth-First Pipeline Parallelism lowers training time, cost and memory usage by combining a high GPU utilization with a small batch size per GPU, and by making use of fully sharded data parallelism. Experimentally, we observed an increase of up to 43% in training throughput for a 52 billion-parameter model using a small batch size per GPU compared to Megatron-LM, which would reduce the training time and cost by the same amount on a large GPU cluster.

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Joel Lamy-Poirier. 2022-11-11. Breadth-First Pipeline Parallelism. https://arxiv.org/abs/2211.05953

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