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SpecGen: Accelerating Agentic Kernel Optimization with Speculative Generation
We present SpecGen, an agentic kernel optimization system that uses speculative generation to fork candidate kernels during LLM reasoning, validating and profiling them in parallel to reduce end-to-end optimization time and improve resource utilization.
Jihu Guo, Sitian Lu, Tenghui Ma, Wei Gao, Zhisheng YE, Xingcheng Zhang, Dahua Lin
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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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