{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:37:42Z","timestamp":1783035462362,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. DOE Office of Science, Office of Advanced Scientific Computing Research","award":["66150"],"award-info":[{"award-number":["66150"]}]},{"name":"U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research","award":["78284"],"award-info":[{"award-number":["78284"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,2,28]]},"DOI":"10.1145\/3710848.3710870","type":"proceedings-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T06:20:57Z","timestamp":1740723657000},"page":"252-266","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3378-3458","authenticated-orcid":false,"given":"Yuhang","family":"Liang","sequence":"first","affiliation":[{"name":"University of Oregon, Oregon, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7276-7715","authenticated-orcid":false,"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5541-433X","authenticated-orcid":false,"given":"Jie","family":"Ren","sequence":"additional","affiliation":[{"name":"College of William &amp; Mary, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3734-9137","authenticated-orcid":false,"given":"Ang","family":"Li","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-3982","authenticated-orcid":false,"given":"Bo","family":"Fang","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1905-9171","authenticated-orcid":false,"given":"Jieyang","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Oregon, Oregon, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00052"},{"key":"e_1_3_2_1_2_1","volume-title":"Language models are few-shot learners. arXiv preprint ArXiv:2005.14165","author":"Brown Tom B","year":"2020","unstructured":"Tom B Brown. 2020. Language models are few-shot learners. arXiv preprint ArXiv:2005.14165 (2020)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00071"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/NAS.2016.7549404"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2016.81"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577496"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00095"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3330345.3330355"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2008.58"},{"key":"e_1_3_2_1_10_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_11_1","unstructured":"Harish Dattatraya Dixit Laura Boyle Gautham Vunnam Sneha Pendharkar Matt Beadon and Sriram Sankar. 2022. Detecting silent data corruptions in the wild. arXiv:2203.08989 [cs.AR] https:\/\/arxiv.org\/abs\/2203.08989"},{"key":"e_1_3_2_1_12_1","unstructured":"Abhimanyu Dubey Abhinav Jauhri Abhinav Pandey Abhishek Kadian Ahmad Al-Dahle Aiesha Letman Akhil Mathur Alan Schelten Amy Yang Angela Fan et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2016.24"},{"key":"e_1_3_2_1_14_1","volume-title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027","author":"Gao Leo","year":"2020","unstructured":"Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020. The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027 (2020)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2444855"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00011"},{"key":"e_1_3_2_1_17_1","volume-title":"2012 42nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 1--12","author":"Sastry Hari Siva Kumar","year":"2012","unstructured":"Siva Kumar Sastry Hari, Sarita V Adve, and Helia Naeimi. 2012. Low-cost program-level detectors for reducing silent data corruptions. In 2012 42nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 1--12."},{"key":"e_1_3_2_1_18_1","volume-title":"ACM International Conference on Architectural Support for Programming Languages and Operating Systems.","author":"Sastry Hari Siva Kumar","year":"2012","unstructured":"Siva Kumar Sastry Hari, Sarita V. Adve, Helia Naeimi, and Pradeep Ramachandran. 2012. Relyzer: exploiting application-level fault equivalence to analyze application resiliency to transient faults. In ACM International Conference on Architectural Support for Programming Languages and Operating Systems."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589105"},{"key":"e_1_3_2_1_20_1","volume-title":"Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al.","author":"Hoffmann Jordan","year":"2022","unstructured":"Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556 (2022)."},{"key":"e_1_3_2_1_21_1","volume-title":"Algorithm-based fault tolerance for matrix operations","author":"Huang Kuang-Hua","year":"1984","unstructured":"Kuang-Hua Huang and Jacob A Abraham. 1984. Algorithm-based fault tolerance for matrix operations. IEEE transactions on computers 100, 6 (1984), 518--528."},{"key":"e_1_3_2_1_22_1","volume-title":"21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)","author":"Jiang Ziheng","year":"2024","unstructured":"Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang, Yangrui Chen, Zhi Zhang, Yanghua Peng, Xiang Li, Cong Xie, Shibiao Nong, et al. 2024. {MegaScale}: Scaling large language model training to more than 10,000 {GPUs}. In 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24). 745--760."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3126908.3126964"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.31193\/ssap.isbn.9787520118873"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020972"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3126908.3126915"},{"key":"e_1_3_2_1_27_1","volume-title":"Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms. arXiv preprint arXiv:2404.12674","author":"Lin Zhongyi","year":"2024","unstructured":"Zhongyi Lin, Ning Sun, Pallab Bhattacharya, Xizhou Feng, Louis Feng, and John D Owens. 2024. Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms. arXiv preprint arXiv:2404.12674 (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN-W50199.2020.00014"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2015.50"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2016.7446091"},{"key":"e_1_3_2_1_32_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_33_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1 8 (2019) 9."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00024"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465834"},{"key":"e_1_3_2_1_36_1","volume-title":"SWIFT: Software Implemented Fault Tolerance. In International Symposium on Code Generation and Optimization. IEEE, 243--254","author":"Reis G A","year":"2005","unstructured":"G A Reis, J Chang, N Vachharajani, R Rangan, and D I August. 2005. SWIFT: Software Implemented Fault Tolerance. In International Symposium on Code Generation and Optimization. IEEE, 243--254."},{"key":"e_1_3_2_1_37_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_38_1","volume-title":"ByteCheckpoint: A Unified Checkpointing System for LLM Development. arXiv preprint arXiv:2407.20143","author":"Wan Borui","year":"2024","unstructured":"Borui Wan, Mingji Han, Yiyao Sheng, Zhichao Lai, Mofan Zhang, Junda Zhang, Yanghua Peng, Haibin Lin, Xin Liu, and Chuan Wu. 2024. ByteCheckpoint: A Unified Checkpointing System for LLM Development. arXiv preprint arXiv:2407.20143 (2024)."},{"key":"e_1_3_2_1_39_1","volume-title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461","author":"Wang Alex","year":"2018","unstructured":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018)."},{"key":"e_1_3_2_1_40_1","volume-title":"Bingsheng He, and Xiaowen Chu.","author":"Wang Yuxin","year":"2023","unstructured":"Yuxin Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Xinglin Pan, Yang Zheng, Xiaoyu Wu, Amelie Chi Zhou, Bingsheng He, and Xiaowen Chu. 2023. Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining. arXiv preprint arXiv:2310.12670 (2023)."},{"key":"e_1_3_2_1_41_1","volume-title":"Transom: An efficient fault-tolerant system for training llms. arXiv preprint arXiv:2310.10046","author":"Wu Baodong","year":"2023","unstructured":"Baodong Wu, Lei Xia, Qingping Li, Kangyu Li, Xu Chen, Yongqiang Guo, Tieyao Xiang, Yuheng Chen, and Shigang Li. 2023. Transom: An efficient fault-tolerant system for training llms. arXiv preprint arXiv:2310.10046 (2023)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600212.2600232"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018743.3018750"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2907294.2907315"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118402"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTER.2019.8890989"},{"key":"e_1_3_2_1_47_1","first-page":"1677","article-title":"FT-CNN: Algorithm-based fault tolerance for convolutional neural networks","volume":"32","author":"Zhao Kai","year":"2020","unstructured":"Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, Yujia Zhai, Jieyang Chen, Kaiming Ouyang, Franck Cappello, and Zizhong Chen. 2020. FT-CNN: Algorithm-based fault tolerance for convolutional neural networks. IEEE Transactions on Parallel and Distributed Systems 32, 7 (2020), 1677--1689.","journal-title":"IEEE Transactions on Parallel and Distributed Systems"}],"event":{"name":"PPoPP '25: The 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming","location":"Las Vegas NV USA","acronym":"PPoPP '25","sponsor":["SIGPLAN ACM Special Interest Group on Programming Languages","SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing"]},"container-title":["Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3710848.3710870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3710848.3710870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:15:55Z","timestamp":1755875755000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3710848.3710870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":47,"alternative-id":["10.1145\/3710848.3710870","10.1145\/3710848"],"URL":"https:\/\/doi.org\/10.1145\/3710848.3710870","relation":{},"subject":[],"published":{"date-parts":[[2025,2,28]]},"assertion":[{"value":"2025-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}