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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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CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
Batch inference for agentic workloads stresses the GPU key-value (KV) cache in a sustained and cumulative manner, often causing severe …
Qiaoling Chen, Zhisheng YE, Tian Tang, Peng Sun, Boyu Tian, Guoteng Wang, Shenggui Li, Yonggang Wen, Zhenhua Han, Tianwei Zhang
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LEMUR: Large Scale End-to-End Multimodal Recommendation
Traditional ID-based recommender systems often struggle with cold-start and generalization challenges. Multimodal recommendation …
Xintian Han, Honggang Chen, Quan Lin, Jingyue Gao, Xiangyuan Ren, Lifei Zhu, Zhisheng YE, Shikang Wu, XiongHang Xie, Xiaochu Gan, Bingzheng Wei, Peng Xu, Zhe Wang, Yuchao Zheng, Jingjian Lin, Di Wu, Junfeng Ge
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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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