{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:33:42Z","timestamp":1769733222123,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072235"],"award-info":[{"award-number":["62072235"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709424","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:42:22Z","timestamp":1743792142000},"page":"2803-2814","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Instruction Semantics Enhanced Dual-Flow Graph Model for GPU Error Resilience Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9926-9442","authenticated-orcid":false,"given":"Pengfei","family":"Yu","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3989-1520","authenticated-orcid":false,"given":"Jingjing","family":"Gu","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3947-4153","authenticated-orcid":false,"given":"Dazhong","family":"Shen","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5650-7046","authenticated-orcid":false,"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2596-8419","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00092"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-8157-6_13"},{"key":"e_1_3_2_2_3_1","first-page":"1877","article-title":"Language Models are Few-Shot Learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 33. 1877--1901.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2009.5306797"},{"key":"e_1_3_2_2_5_1","volume-title":"ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. In International Conference on Machine Learning (ICML). 2244--2253","author":"Cummins Chris","year":"2021","unstructured":"Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F P O'Boyle, and Hugh Leather. 2021. ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. In International Conference on Machine Learning (ICML). 2244--2253."},{"key":"e_1_3_2_2_6_1","volume-title":"DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives. arXiv preprint arXiv:2403.04287","author":"Ding Leilei","year":"2024","unstructured":"Leilei Ding, Dazhong Shen, Chao Wang, Tianfu Wang, Le Zhang, and Yanyong Zhang. 2024. DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives. arXiv preprint arXiv:2403.04287 (2024)."},{"key":"e_1_3_2_2_7_1","volume-title":"Auto-tuning a high-level language targeted to GPU codes","author":"Grauer-Gray Scott","unstructured":"Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalasomayajula, and John Cavazos. 2012. Auto-tuning a high-level language targeted to GPU codes. In IEEE Innovative Parallel Computing (InPar). 1--10."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2021.02.015"},{"key":"e_1_3_2_2_9_1","volume-title":"Vulberta: Simplified source code pre-training for vulnerability detection. In International joint conference on neural networks (IJCNN). 1--8.","author":"Hanif Hazim","year":"2022","unstructured":"Hazim Hanif and Sergio Maffeis. 2022. Vulberta: Simplified source code pre-training for vulnerability detection. In International joint conference on neural networks (IJCNN). 1--8."},{"key":"e_1_3_2_2_10_1","volume-title":"IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 249--258","author":"Sastry Hari Siva Kumar","year":"2017","unstructured":"Siva Kumar Sastry Hari, Timothy Tsai, Mark Stephenson, Stephen W Keckler, and Joel Emer. 2017. Sassifi: An architecture-level fault injection tool for gpu application resilience evaluation. In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 249--258."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3607078"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3571885.3571907"},{"key":"e_1_3_2_2_13_1","volume-title":"Contrastive Code Representation Learning. In Conference on Empirical Methods in Natural Language Processing (EMNLP). 5954--5971","author":"Jain Paras","year":"2021","unstructured":"Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph Gonzalez, and Ion Stoica. 2021. Contrastive Code Representation Learning. In Conference on Empirical Methods in Natural Language Processing (EMNLP). 5954--5971."},{"key":"e_1_3_2_2_14_1","volume-title":"MeRLiN: Exploiting Dynamic Instruction Behavior for Fast and Accurate Microarchitecture Level Reliability Assessment. In Annual International Symposium on Computer Architecture (ISCA). 241--254","author":"Kaliorakis Manolis","year":"2017","unstructured":"Manolis Kaliorakis, Dimitris Gizopoulos, Ramon Canal, and Antonio Gonzalez. 2017. MeRLiN: Exploiting Dynamic Instruction Behavior for Fast and Accurate Microarchitecture Level Reliability Assessment. In Annual International Symposium on Computer Architecture (ISCA). 241--254."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00072"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3382132"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-short.80"},{"key":"e_1_3_2_2_18_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Annual Conference of the North American","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 4171--4186."},{"key":"e_1_3_2_2_19_1","volume-title":"5th International Conference on Learning Representations (ICLR).","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3014904.3014932"},{"key":"e_1_3_2_2_21_1","volume-title":"Modeling Soft-Error Propagation in Programs. In Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). 27--38","author":"Li Guanpeng","year":"2018","unstructured":"Guanpeng Li, Karthik Pattabiraman, Siva Kumar Sastry Hari, Michael Sullivan, and Timothy Tsai. 2018. Modeling Soft-Error Propagation in Programs. In Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). 27--38."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468597"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.2994954"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441589"},{"key":"e_1_3_2_2_25_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. 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:1907.11692 (2019)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539176"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC.2018.00070"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2016.7446091"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2018.00022"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2018.00066"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3285094"},{"key":"e_1_3_2_2_32_1","volume-title":"Annual IEEE\/IFIP International Conference on Dependable Systems and Networks-Supplemental","volume":"88","author":"Portet Sergi Alcaide","year":"2020","unstructured":"Sergi Alcaide Portet, Leonidas Kosmidis, Carles Hernandez, and Jaume Abella. 2020. Software-only triple diverse redundancy on GPUs for autonomous driving platforms. In Annual IEEE\/IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). 82--88."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.2991136"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00349"},{"key":"e_1_3_2_2_35_1","first-page":"14681","article-title":"Topic modeling revisited: A document graph-based neural network perspective","volume":"34","author":"Shen Dazhong","year":"2021","unstructured":"Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, Hengshu Zhu, and Hui Xiong. 2021. Topic modeling revisited: A document graph-based neural network perspective. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 34. 14681--14693.","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342014522573"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3623345"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750375"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2807591.2807666"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN48987.2021.00041"},{"key":"e_1_3_2_2_41_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","volume":"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. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 30."},{"key":"e_1_3_2_2_42_1","volume-title":"6th International Conference on Learning Representations (ICLR).","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. In 6th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358307"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-82153-1_47"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3330821"},{"key":"e_1_3_2_2_46_1","volume-title":"Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning. In IEEE\/ACM International Conference on Software Engineering (ICSE). 2275--2286","author":"Wen Xin-Cheng","year":"2023","unstructured":"Xin-Cheng Wen, Yupan Chen, Cuiyun Gao, Hongyu Zhang, Jie M. Zhang, and Qing Liao. 2023. Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning. In IEEE\/ACM International Conference on Software Engineering (ICSE). 2275--2286."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.217"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476170"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709424","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709424","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:36:09Z","timestamp":1755358569000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":48,"alternative-id":["10.1145\/3690624.3709424","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709424","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}