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Breadth-First Pipeline Parallelism
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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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Cited by 1 Pith paper
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Scaling Deep Learning Training with MPMD Pipeline Parallelism
JaxPP introduces a user-defined MPMD pipeline schedule API on top of JAX/GSPMD and reports throughput gains up to 1.11x over SPMD training on H100 clusters.
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