{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T11:04:59Z","timestamp":1784718299965,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,26]]},"DOI":"10.1145\/3785462.3815796","type":"proceedings-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T10:41:43Z","timestamp":1784716903000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study on LLM-enabled HPC User Support"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3386-3463","authenticated-orcid":false,"given":"Mingkai","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1174-4347","authenticated-orcid":false,"given":"Fangru","family":"Linghu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9595-1105","authenticated-orcid":false,"given":"Sikan","family":"Li","sequence":"additional","affiliation":[{"name":"Texas Advanced Computing Center, Texas Advanced Computing Center, Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0721-025X","authenticated-orcid":false,"given":"Marty Charles","family":"Kandes","sequence":"additional","affiliation":[{"name":"San Diego Supercomputer Center, San Diego Supercomputer Center, La Jolla, CA, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3668-9853","authenticated-orcid":false,"given":"Niall","family":"Gaffney","sequence":"additional","affiliation":[{"name":"Texas Advanced Computing Center, Texas Advanced Computing Center, Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-5269","authenticated-orcid":false,"given":"Ian","family":"Foster","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Chicago, Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5921-0035","authenticated-orcid":false,"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,26]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"Angels Balaguer Vinamra Benara Renato\u00a0Luiz de Freitas\u00a0Cunha Roberto de M.\u00a0Estev\u00e3o\u00a0Filho Todd Hendry Daniel Holstein Jennifer Marsman Nick Mecklenburg Sara Malvar Leonardo\u00a0O. Nunes Rafael Padilha Morris Sharp Bruno Silva Swati Sharma Vijay Aski and Ranveer Chandra. 2024. RAG vs Fine-tuning: Pipelines Tradeoffs and a Case Study on Agriculture. arxiv:https:\/\/arXiv.org\/abs\/2401.08406\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2401.08406"},{"key":"e_1_3_3_1_3_2","volume-title":"Supplementing HPC Support with a Science Gateway AI Assistant","author":"Biggs Brandon\u00a0S","year":"2024","unstructured":"Brandon\u00a0S Biggs and Kaylee Dalton. 2024. Supplementing HPC Support with a Science Gateway AI Assistant. Technical Report. Idaho National Laboratory (INL), Idaho Falls, ID (United States)."},{"key":"e_1_3_3_1_4_2","unstructured":"Tom\u00a0B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel\u00a0M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:https:\/\/arXiv.org\/abs\/2005.14165\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2005.14165"},{"key":"e_1_3_3_1_5_2","unstructured":"Franck Cappello Sandeep Madireddy Robert Underwood Neil Getty Nicholas Lee-Ping Chia Nesar Ramachandra Josh Nguyen Murat Keceli Tanwi Mallick Zilinghan Li et\u00a0al. 2025. EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.20309 (2025)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"e_1_3_3_1_7_2","unstructured":"Andrew\u00a0M. Dai and Quoc\u00a0V. Le. 2015. Semi-supervised Sequence Learning. arxiv:https:\/\/arXiv.org\/abs\/1511.01432\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1511.01432"},{"key":"e_1_3_3_1_8_2","unstructured":"DeepSeek-AI. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arxiv:https:\/\/arXiv.org\/abs\/2501.12948\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2501.12948"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Jonas Degrave Federico Felici Jonas Buchli Michael Neunert Brendan Tracey Francesco Carpanese Timo Ewalds Roland Hafner Abbas Abdolmaleki Diego de Las\u00a0Casas Craig Donner Leslie Fritz Cristian Galperti Andrea Huber James Keeling Maria Tsimpoukelli Jackie Kay Antoine Merle Jean-Marc Moret Seb Noury Federico Pesamosca David Pfau Olivier Sauter Cristian Sommariva Stefano Coda Basil Duval Ambrogio Fasoli Pushmeet Kohli Koray Kavukcuoglu Demis Hassabis and Martin Riedmiller. 2022. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602 7897 (2022) 414\u2013419.","DOI":"10.1038\/s41586-021-04301-9"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3624062.3624172"},{"key":"e_1_3_3_1_11_2","first-page":"18","volume-title":"OpenMP: Advanced Task-Based, Device and Compiler Programming: 19th International Workshop on OpenMP, IWOMP 2023, Bristol, UK, September 13\u201315, 2023, Proceedings","volume":"14114","author":"Emani Murali","year":"2023","unstructured":"Murali Emani and Bronis de Supinski. 2023. LM4HPC: Towards Effective Language Model Application in High-Performance Computing. In OpenMP: Advanced Task-Based, Device and Compiler Programming: 19th International Workshop on OpenMP, IWOMP 2023, Bristol, UK, September 13\u201315, 2023, Proceedings , Vol.\u00a014114. Springer Nature, 18."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Linjing Fang Fred Monroe Sammy\u00a0Weiser Novak Lyndsey Kirk Cara\u00a0R. Schiavon Seungyoon\u00a0B. Yu Tong Zhang Melissa Wu Kyle Kastner Yoshiyuki Kubota Zhao Zhang Gulcin Pekkurnaz John Mendenhall Kristen Harris Jeremy Howard and Uri Manor. 2021. Deep learning-based point-scanning super-resolution imaging. Nature Methods 18 4 (2021) 406\u2013416.","DOI":"10.1038\/s41592-021-01080-z"},{"key":"e_1_3_3_1_13_2","unstructured":"Aaron Grattafiori and et al. 2024. The Llama 3 Herd of Models. arxiv:https:\/\/arXiv.org\/abs\/2407.21783\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437359.3465565"},{"key":"e_1_3_3_1_15_2","volume-title":"deepeval","author":"Ip Jeffrey","year":"2025","unstructured":"Jeffrey Ip and Kritin Vongthongsri. 2025. deepeval. https:\/\/github.com\/confident-ai\/deepeval"},{"key":"e_1_3_3_1_16_2","unstructured":"Hongwei Jin George Papadimitriou Krishnan Raghavan Pawel Zuk Prasanna Balaprakash Cong Wang Anirban Mandal and Ewa Deelman. 2024. Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning. arxiv:https:\/\/arXiv.org\/abs\/2407.17545\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2407.17545"},{"key":"e_1_3_3_1_17_2","unstructured":"Patrick Lewis Ethan Perez Aleksandra Piktus Fabio Petroni Vladimir Karpukhin Naman Goyal Heinrich K\u00fcttler Mike Lewis Wen tau Yih Tim Rockt\u00e4schel Sebastian Riedel and Douwe Kiela. 2021. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arxiv:https:\/\/arXiv.org\/abs\/2005.11401\u00a0[cs.CL]"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Yusuke Miyashita Patrick Kin\u00a0Man Tung and Johan Barth\u00e9lemy. 2024. LLM as HPC Expert: Extending RAG Architecture for HPC Data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2501.14733 (2024).","DOI":"10.32388\/IMKKVR"},{"key":"e_1_3_3_1_19_2","unstructured":"Marius Mosbach Tiago Pimentel Shauli Ravfogel Dietrich Klakow and Yanai Elazar. 2023. Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation. arxiv:https:\/\/arXiv.org\/abs\/2305.16938\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2305.16938"},{"key":"e_1_3_3_1_20_2","unstructured":"Zach Nussbaum John\u00a0X. Morris Brandon Duderstadt and Andriy Mulyar. 2025. Nomic Embed: Training a Reproducible Long Context Text Embedder. arxiv:https:\/\/arXiv.org\/abs\/2402.01613\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2402.01613"},{"key":"e_1_3_3_1_21_2","unstructured":"Long Ouyang and et al. 2022. Training language models to follow instructions with human feedback. arxiv:https:\/\/arXiv.org\/abs\/2203.02155\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2203.02155"},{"key":"e_1_3_3_1_22_2","unstructured":"Fabian Pedregosa Ga\u00ebl Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron Weiss Vincent Dubourg Jake Vanderplas Alexandre Passos David Cournapeau Matthieu Brucher Matthieu Perrot and \u00c9douard Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011) 2825\u20132830. https:\/\/jmlr.csail.mit.edu\/papers\/v12\/pedregosa11a.html"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"James\u00a0C Phillips Rosemary Braun Wei Wang James Gumbart Emad Tajkhorshid Elizabeth Villa Christophe Chipot Robert\u00a0D Skeel Laxmikant Kale and Klaus Schulten. 2005. Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26 16 (2005) 1781\u20131802.","DOI":"10.1002\/jcc.20289"},{"key":"e_1_3_3_1_24_2","unstructured":"Alec Radford and Karthik Narasimhan. 2018. Improving Language Understanding by Generative Pre-Training. https:\/\/api.semanticscholar.org\/CorpusID:49313245"},{"key":"e_1_3_3_1_25_2","unstructured":"Samyam Rajbhandari Jeff Rasley Olatunji Ruwase and Yuxiong He. 2020. ZeRO: Memory Optimizations Toward Training Trillion Parameter Models. arxiv:https:\/\/arXiv.org\/abs\/1910.02054\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1910.02054"},{"key":"e_1_3_3_1_26_2","unstructured":"Jie Ren Samyam Rajbhandari Reza\u00a0Yazdani Aminabadi Olatunji Ruwase Shuangyan Yang Minjia Zhang Dong Li and Yuxiong He. 2021. ZeRO-Offload: Democratizing Billion-Scale Model Training. arxiv:https:\/\/arXiv.org\/abs\/2101.06840\u00a0[cs.DC] https:\/\/arxiv.org\/abs\/2101.06840"},{"key":"e_1_3_3_1_27_2","first-page":"J11\u2013J15","volume-title":"Preprints, Ninth Conf. Mesoscale Processes","author":"Skamarock William\u00a0C","year":"2001","unstructured":"William\u00a0C Skamarock, Joseph\u00a0B Klemp, and Jimy Dudhia. 2001. Prototypes for the WRF (Weather Research and Forecasting) model. In Preprints, Ninth Conf. Mesoscale Processes. Amer. Meteorol. Soc., J11\u2013J15."},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3673791.3698415"},{"key":"e_1_3_3_1_29_2","unstructured":"Tianhua Tao Junbo Li Bowen Tan Hongyi Wang William Marshall Bhargav\u00a0M Kanakiya Joel Hestness Natalia Vassilieva Zhiqiang Shen Eric\u00a0P. Xing and Zhengzhong Liu. 2024. Crystal: Illuminating LLM Abilities on Language and Code. arxiv:https:\/\/arXiv.org\/abs\/2411.04156\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2411.04156"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1101"},{"key":"e_1_3_3_1_31_2","unstructured":"Zhewei Yao Reza\u00a0Yazdani Aminabadi Olatunji Ruwase Samyam Rajbhandari Xiaoxia Wu Ammar\u00a0Ahmad Awan Jeff Rasley Minjia Zhang Conglong Li Connor Holmes Zhongzhu Zhou Michael Wyatt Molly Smith Lev Kurilenko Heyang Qin Masahiro Tanaka Shuai Che Shuaiwen\u00a0Leon Song and Yuxiong He. 2023. DeepSpeed-Chat: Easy Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales. arxiv:https:\/\/arXiv.org\/abs\/2308.01320\u00a0[cs.LG]"},{"key":"e_1_3_3_1_32_2","unstructured":"Susan Zhang Stephen Roller Naman Goyal Mikel Artetxe Moya Chen Shuohui Chen Christopher Dewan Mona Diab Xian Li Xi\u00a0Victoria Lin Todor Mihaylov Myle Ott Sam Shleifer Kurt Shuster Daniel Simig Punit\u00a0Singh Koura Anjali Sridhar Tianlu Wang and Luke Zettlemoyer. 2022. OPT: Open Pre-trained Transformer Language Models. arxiv:https:\/\/arXiv.org\/abs\/2205.01068\u00a0[cs.CL]"},{"key":"e_1_3_3_1_33_2","unstructured":"Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu Yonghao Zhuang Zi Lin Zhuohan Li Dacheng Li Eric\u00a0P. Xing Hao Zhang Joseph\u00a0E. Gonzalez and Ion Stoica. 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. arxiv:https:\/\/arXiv.org\/abs\/2306.05685\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2306.05685"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"Chunting Zhou Pengfei Liu Puxin Xu Srini Iyer Jiao Sun Yuning Mao Xuezhe Ma Avia Efrat Ping Yu Lili Yu Susan Zhang Gargi Ghosh Mike Lewis Luke Zettlemoyer and Omer Levy. 2023. LIMA: Less Is More for Alignment. arxiv:https:\/\/arXiv.org\/abs\/2305.11206\u00a0[cs.CL]","DOI":"10.52202\/075280-2400"}],"event":{"name":"PEARC '26: Practice and Experience in Advanced Research Computing","location":"Minneapolis MN USA","acronym":"PEARC '26","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","SIGAPP ACM Special Interest Group on Applied Computing"]},"container-title":["Proceedings of the Practice and Experience in Advanced Research Computing 2026: Resilient Roots + Empowered Communities"],"original-title":[],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T10:46:02Z","timestamp":1784717162000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3785462.3815796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,26]]},"references-count":33,"alternative-id":["10.1145\/3785462.3815796","10.1145\/3785462"],"URL":"https:\/\/doi.org\/10.1145\/3785462.3815796","relation":{},"subject":[],"published":{"date-parts":[[2026,7,26]]},"assertion":[{"value":"2026-07-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}