Poster
in
Workshop: Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network Training
Breadth-first pipeline parallelism
Joel Lamy-Poirier
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 the 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 increases of up to 53% in training speed.
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