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Gemini 2.5: Our most intelligent ai model. https:\/\/blog.google\/technology\/google-deepmind\/gemini-model-thinking-updates-march-2025\/, 2025."},{"key":"e_1_3_2_1_20_1","volume-title":"LoongTrain: Efficient training of long-sequence llms with head-context parallelism. arXiv 2406.18485 [cs.DC]","author":"Gu Diandian","year":"2024","unstructured":"Diandian Gu, Peng Sun, Qinghao Hu, Ting Huang, Xun Chen, Yingtong Xiong, Guoteng Wang, Qiaoling Chen, Shangchun Zhao, Jiarui Fang, Yonggang Wen, Tianwei Zhang, Xin Jin, and Xuanzhe Liu. LoongTrain: Efficient training of long-sequence llms with head-context parallelism. arXiv 2406.18485 [cs.DC], 2024."},{"key":"e_1_3_2_1_21_1","volume-title":"NAACL'24","author":"Han Chi","year":"2024","unstructured":"Chi Han, Qifan Wang, Hao Peng, Wenhan Xiong, Yu Chen, Heng Ji, and Sinong Wang. LM-infinite: Zero-shot extreme length generalization for large language models. 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In ICLR'24, 2024."},{"key":"e_1_3_2_1_29_1","volume-title":"NCCL: Optimized primitives for inter-gpu communication. https:\/\/github.com\/NVIDIA\/nccl","author":"NVIDIA.","year":"2024","unstructured":"NVIDIA. NCCL: Optimized primitives for inter-gpu communication. https:\/\/github.com\/NVIDIA\/nccl, 2024."},{"key":"e_1_3_2_1_30_1","volume-title":"https:\/\/github.com\/NVIDIA\/TransformerEngine","author":"Transformer Engine NVIDIA.","year":"2024","unstructured":"NVIDIA. Transformer Engine. https:\/\/github.com\/NVIDIA\/TransformerEngine, 2024."},{"key":"e_1_3_2_1_31_1","volume-title":"NVIDIA Nsight Systems. https:\/\/developer.nvidia.com\/nsight-systems","author":"NVIDIA.","year":"2025","unstructured":"NVIDIA. NVIDIA Nsight Systems. https:\/\/developer.nvidia.com\/nsight-systems, 2025."},{"key":"e_1_3_2_1_32_1","volume-title":"Gpt-4o system card. arXiv 2410.21276 [cs.CL]","author":"AI.","year":"2024","unstructured":"OpenAI. Gpt-4o system card. arXiv 2410.21276 [cs.CL], 2024."},{"key":"e_1_3_2_1_33_1","volume-title":"NeurIPS'22","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. NeurIPS'22, 2022."},{"key":"e_1_3_2_1_34_1","volume-title":"Rabe and Charles Staats. Self-attention does not need o(n2) memory. arXiv 2112.05682 [cs.LG]","author":"Markus","year":"2022","unstructured":"Markus N. Rabe and Charles Staats. Self-attention does not need o(n2) memory. arXiv 2112.05682 [cs.LG], 2022."},{"key":"e_1_3_2_1_35_1","volume-title":"Language models are unsupervised multitask learners","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019."},{"key":"e_1_3_2_1_36_1","volume-title":"NeurIPS'23","author":"Rafailov Rafael","year":"2023","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. NeurIPS'23, 2023."},{"key":"e_1_3_2_1_37_1","unstructured":"Redis. Redis - the real-time data platform. https:\/\/redis.io\/."},{"key":"e_1_3_2_1_38_1","unstructured":"Sebastian Schlag. High-Quality Hypergraph Partitioning. PhD thesis Karlsruhe Institute of Technology Germany 2020."},{"key":"e_1_3_2_1_39_1","volume-title":"Megatron-lm: Training multibillion parameter language models using model parallelism. arXiv","author":"Shoeybi Mohammad","year":"1909","unstructured":"Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-lm: Training multibillion parameter language models using model parallelism. arXiv 1909.08053 [cs.CL], 2020."},{"key":"e_1_3_2_1_40_1","first-page":"19","volume-title":"MAPL'19","author":"Tillet Philippe","year":"2019","unstructured":"Philippe Tillet, H. T. Kung, and David Cox. Triton: an intermediate language and compiler for tiled neural network computations. In MAPL'19, page 10\u201319, 2019."},{"key":"e_1_3_2_1_41_1","volume-title":"togethercomputer\/Long-Data-Collections. https:\/\/huggingface.co\/datasets\/togethercomputer\/Long-Data-Collections","year":"2024","unstructured":"together.ai. togethercomputer\/Long-Data-Collections. https:\/\/huggingface.co\/datasets\/togethercomputer\/Long-Data-Collections, 2024."},{"key":"e_1_3_2_1_42_1","volume-title":"NeurIPS'17","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS'17, 2017."},{"key":"e_1_3_2_1_43_1","volume-title":"ICLR'25","author":"Wang Guoxia","year":"2025","unstructured":"Guoxia Wang, Jinle Zeng, Xiyuan Xiao, Siming Wu, Jiabin Yang, Lujing Zheng, Zeyu Chen, Jiang Bian, Dianhai Yu, and Haifeng Wang. Flashmask: Efficient and rich mask extension of flashattention. In ICLR'25, 2025."},{"key":"e_1_3_2_1_44_1","volume-title":"ASPLOS'25","author":"Wang Yujie","year":"2025","unstructured":"Yujie Wang, Shiju Wang, Shenhan Zhu, Fangcheng Fu, Xinyi Liu, Xuefeng Xiao, Huixia Li, Jiashi Li, Faming Wu, and Bin Cui. Flexsp: Accelerating large language model training via flexible sequence parallelism. In ASPLOS'25, 2025."},{"key":"e_1_3_2_1_45_1","volume-title":"Wlb-llm: Workload-balanced 4d parallelism for large language model training. arXiv 2503.17924 [cs.DC]","author":"Wang Zheng","year":"2025","unstructured":"Zheng Wang, Anna Cai, Xinfeng Xie, Zaifeng Pan, Yue Guan, Weiwei Chu, Jie Wang, Shikai Li, Jianyu Huang, Chris Cai, Yuchen Hao, and Yufei Ding. Wlb-llm: Workload-balanced 4d parallelism for large language model training. arXiv 2503.17924 [cs.DC], 2025."},{"key":"e_1_3_2_1_46_1","volume-title":"ICLR'24","author":"Xiao Guangxuan","year":"2024","unstructured":"Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. 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