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Scaling Deep Learning Training with MPMD Pipeline Parallelism
Anxhelo Xhebraj, Sean Lee, Hanfeng Chen, Vinod Grover · 2024-12-19 · via cs.LG updates on arXiv.org

We present JaxPP, a system for efficiently scaling the training of large deep learning models with flexible pipeline parallelism. We introduce a seamless programming model that allows implementing user-defined pipeline schedules for gradient accumulation. JaxPP automatically distributes tasks, corresponding to pipeline stages, over a cluster of nodes and automatically infers the communication among them. We implement a MPMD runtime for asynchronous execution of SPMD tasks. The pipeline parallelism implementation of JaxPP improves hardware utilization by up to $1.11\times$ with respect to the best performing SPMD configuration.

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