arXiv Machine Learning
Jun 9
Efficient Scaling of LLM Training with Flexible Context Parallelism
arXiv:2602. 21788v2 Announce Type: replace-cross Abstract: Scaling long-context capabilities is crucial for Large Language Models (LLMs).
BASP: Communication-Efficient Batch-Aware Sequence Parallelism for LLM Training
The paper introduces BASP, a batch‑aware sequence parallelism method that partitions GPUs into disjoint groups based on micro‑batch size to reduce all‑to‑all communication. By localizing communication, BASP improves training efficiency for long‑context LLMs. Experiments on NVIDIA A100 clusters show up to 1.17‑1.31× faster end‑to‑end training on Llama and Qwen models while maintaining the same accuracy and memory usage.
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