Distributed Training

ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism
Hybrid parallelism underpins large-scale LLM training across tens of thousands of GPUs. At such scale, hardware failures on individual …
Tenghui Ma, Jihu Guo, Wei Gao, Sitian Lu, Zhisheng YE, Dahua Lin, Hanjing Wang
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AMSP: Super-Scaling LLM Training via Advanced Model States Partitioning
Large Language Models (LLMs) have demonstrated impressive performance across various downstream tasks. When training these models, …
Qiaoling Chen, Qinghao Hu, Zhisheng YE, Guoteng Wang, Peng Sun, Yonggang Wen, Tianwei Zhang
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