arXiv AI By Shiju Wang, Yujie Wang, Fangcheng Fu, Ao Sun, Yinxiao Feng, Zijian Zhu, Bin Cui, Xu Han, Kaisheng Ma

Concertina: Data-Centric Adaptive Pipeline Parallelism for Efficient Heterogeneous Long-Context LLM Training

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arXiv AI
Jun 29

DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers

arXiv:2601. 16956v1 Announce Type: cross Abstract: The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitated training across thousands of GPUs using complex hybrid parallelism strategies (e.

By Avinash Maurya, M. Mustafa Rafique, Franck Cappello, Bogdan Nicolae
arXiv AI
Jul 7

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

arXiv:2607. 05147v1 Announce Type: new Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification.

By Xin Cheng, Xingkai Yu, Chenze Shao, Jiashi Li, Yunfan Xiong, Yi Qian, Jiaqi Zhu, Shirong Ma, Xiaokang Zhang, Jiasheng Ye, Qinyu Chen, Chengqi Deng, Jiping Yu, Damai Dai, Zhengyan Zhang, Yixuan Wei, Yixuan Tan, Wenkai Yang, Runxin Xu, Yu Wu, Zhean Xu, Xuanyu Wang, Muyang Chen, Rui Tian, Xiao Bi, Zhewen Hao, Shaoyuan Chen, Huanqi Cao, Wentao Zhang, Anyi Xu, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
arXiv Machine Learning
Sep 4

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.

By Bigyan Ghimire, Jon C. Calhoun