pipeline-parallelism-f2d8cd21·2 events·first seen Aliases: pipeline parallelism
Hugging Face published a guide on N-dimensional parallelism for multi-GPU training using the Accelerate library. The post covers combining data parallelism, tensor parallelism, pipeline parallelism, and other strategies to efficiently scale model training across GPU clusters. This is a practical technical resource aimed at practitioners working with large-scale distributed training setups.
The paper introduces Runtime-Readiness-First Pipeline (RRFP), a new runtime for pipeline-parallel large-model training that treats schedules as non-binding hint orders rather than strict execution sequences. By combining message-driven asynchronous communication, lightweight tensor-parallel coordination, and ready-set arbitration, RRFP dynamically dispatches work based on actual task readiness, reducing idle bubbles and stage misalignment. Implemented on a Megatron-based framework and evaluated at up to 128 GPUs, RRFP achieves up to 1.77× speedup on language-only workloads and 2.77× on multimodal workloads versus fixed-order baselines, and outperforms the fastest comparable external system by up to 1.84×.