{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T15:56:32Z","timestamp":1781970992246,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC-ERKJ422"],"award-info":[{"award-number":["DE-SC-ERKJ422"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,16]]},"DOI":"10.1145\/3732775.3733580","type":"proceedings-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T13:51:02Z","timestamp":1750427462000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7854-9153","authenticated-orcid":false,"given":"Jiayi","family":"Wang","sequence":"first","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-4982","authenticated-orcid":false,"given":"John","family":"Gounley","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0056-196X","authenticated-orcid":false,"given":"Heidi","family":"Hanson","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","volume-title":"Mitigating bias in federated learning. arXiv preprint arXiv:2012.02447","author":"Abay Annie","year":"2020","unstructured":"Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig. 2020. Mitigating bias in federated learning. arXiv preprint arXiv:2012.02447 (2020)."},{"key":"e_1_3_2_1_3_1","volume-title":"Yahya H Ezzeldin, Qingfeng Liu, Kee-Bong Song, Mostafa El-Khamy, and Salman Avestimehr.","author":"Babakniya Sara","year":"2023","unstructured":"Sara Babakniya, Ahmed Roushdy Elkordy, Yahya H Ezzeldin, Qingfeng Liu, Kee-Bong Song, Mostafa El-Khamy, and Salman Avestimehr. 2023. SLoRA: Federated parameter efficient fine-tuning of language models. arXiv preprint arXiv:2308.06522 (2023)."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 394\u2013403","author":"Balle Borja","year":"2018","unstructured":"Borja Balle and Yu-Xiang Wang. 2018. Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In International Conference on Machine Learning. PMLR, 394\u2013403."},{"key":"e_1_3_2_1_5_1","first-page":"1","article-title":"COMPARATIVE STUDY OF DIFFERENTIALLY PRIVATE SYNTHETIC DATA ALGORITHMS FROM THE NIST PSCR DIFFERENTIAL PRIVACY SYNTHETIC DATA CHALLENGE","volume":"11","author":"CLAIRE","year":"2021","unstructured":"CLAIRE MCKAY BOWEN and JOSHUA SNOKE. 2021. COMPARATIVE STUDY OF DIFFERENTIALLY PRIVATE SYNTHETIC DATA ALGORITHMS FROM THE NIST PSCR DIFFERENTIAL PRIVACY SYNTHETIC DATA CHALLENGE. Journal of Privacy and Confidentiality 11 (2021), 1.","journal-title":"Journal of Privacy and Confidentiality"},{"key":"e_1_3_2_1_6_1","first-page":"12480","article-title":"Don't generate me: Training differentially private generative models with sinkhorn divergence","volume":"34","author":"Cao Tianshi","year":"2021","unstructured":"Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, and Karsten Kreis. 2021. Don't generate me: Training differentially private generative models with sinkhorn divergence. Advances in Neural Information Processing Systems 34 (2021), 12480\u201312492.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_7_1","volume-title":"RBLA: Rank-Based-LoRA-Aggregation for Fine-Tuning Heterogeneous Models in FLaaS. In International Conference on Web Services. Springer, 47\u201362","author":"Chen Shuaijun","year":"2024","unstructured":"Shuaijun Chen, Omid Tavallaie, Niousha Nazemi, and Albert Y Zomaya. 2024. RBLA: Rank-Based-LoRA-Aggregation for Fine-Tuning Heterogeneous Models in FLaaS. In International Conference on Web Services. Springer, 47\u201362."},{"key":"e_1_3_2_1_8_1","volume-title":"Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. In International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS","author":"Cho Yae Jee","year":"2023","unstructured":"Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Matt Barnes, and Gauri Joshi. 2023. Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. In International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023. https:\/\/openreview.net\/forum?id=EmV9sGpZ7q"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 12903\u201312913","author":"Cho Yae Jee","year":"2024","unstructured":"Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, and Gauri Joshi. 2024. Heterogeneous lora for federated fine-tuning of on-device foundation models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 12903\u201312913."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 10351\u201310375","author":"Cho Yae Jee","year":"2022","unstructured":"Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2022. Towards understanding biased client selection in federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 10351\u201310375."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171\u20134186."},{"key":"e_1_3_2_1_12_1","unstructured":"Tim Dockhorn Tianshi Cao Arash Vahdat and Karsten Kreis. [n. d.]. Differentially Private Diffusion Models. Transactions on Machine Learning Research ([n. d.])."},{"key":"e_1_3_2_1_13_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_14_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Aaron Roth et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 9 3\u20134 (2014) 211\u2013407.","DOI":"10.1561\/0400000042"},{"key":"e_1_3_2_1_15_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017)."},{"key":"e_1_3_2_1_16_1","volume-title":"Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models. arXiv preprint arXiv:2410.13097","author":"Ghiasvand Sajjad","year":"2024","unstructured":"Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue, Mahnoosh Alizadeh, Zheng Zhang, and Ramtin Pedarsani. 2024. Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models. arXiv preprint arXiv:2410.13097 (2024)."},{"key":"e_1_3_2_1_17_1","volume-title":"Multimodal-gpt: A vision and language model for dialogue with humans. arXiv preprint arXiv:2305.04790","author":"Gong Tao","year":"2023","unstructured":"Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, and Kai Chen. 2023. Multimodal-gpt: A vision and language model for dialogue with humans. arXiv preprint arXiv:2305.04790 (2023)."},{"key":"e_1_3_2_1_18_1","volume-title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. arXiv preprint arXiv:2406.02958","author":"Hou Charlie","year":"2024","unstructured":"Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, and Daniel Lazar. 2024. PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. arXiv preprint arXiv:2406.02958 (2024)."},{"key":"e_1_3_2_1_19_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=nZeVKeeFYf9","author":"Hu Edward J","year":"2022","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"e_1_3_2_1_20_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and open problems in federated learning. Foundations and trends\u00ae in machine learning 14, 1\u20132 (2021), 1\u2013210."},{"key":"e_1_3_2_1_21_1","volume-title":"International conference on machine learning. PMLR, 5132\u20135143","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International conference on machine learning. PMLR, 5132\u20135143."},{"key":"e_1_3_2_1_22_1","first-page":"1159","article-title":"Client-customized adaptation for parameter-efficient federated learning","volume":"2023","author":"Kim Yeachan","year":"2023","unstructured":"Yeachan Kim, Junho Kim, Wing-Lam Mok, Jun-Hyung Park, and SangKeun Lee. 2023. Client-customized adaptation for parameter-efficient federated learning. In Findings of the Association for Computational Linguistics: ACL 2023. 1159\u20131172.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_3_2_1_23_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2017","unstructured":"Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG] https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_1_24_1","unstructured":"Jabin Koo Minwoo Jang and Jungseul Ok. 2024. Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients. arXiv:2410.22815 [cs.LG] https:\/\/arxiv.org\/abs\/2410.22815"},{"key":"e_1_3_2_1_25_1","volume-title":"Federated LoRA with Sparse Communication. arXiv preprint arXiv:2406.05233","author":"Kuo Kevin","year":"2024","unstructured":"Kevin Kuo, Arian Raje, Kousik Rajesh, and Virginia Smith. 2024. Federated LoRA with Sparse Communication. arXiv preprint arXiv:2406.05233 (2024)."},{"key":"e_1_3_2_1_26_1","volume-title":"Large Language Models Can Be Strong Differentially Private Learners. In International Conference on Learning Representations.","author":"Li Xuechen","unstructured":"Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. [n. d.]. Large Language Models Can Be Strong Differentially Private Learners. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 1522\u20131530","author":"Lin Zinan","year":"2021","unstructured":"Zinan Lin, Vyas Sekar, and Giulia Fanti. 2021. On the privacy properties of gan-generated samples. In International Conference on Artificial Intelligence and Statistics. PMLR, 1522\u20131530."},{"key":"e_1_3_2_1_28_1","volume-title":"Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101","author":"Loshchilov I","year":"2017","unstructured":"I Loshchilov. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"e_1_3_2_1_29_1","volume-title":"Fairness in federated learning for spatial-temporal applications. arXiv preprint arXiv:2201.06598","author":"Mashhadi Afra","year":"2022","unstructured":"Afra Mashhadi, Alex Kyllo, and Reza M Parizi. 2022. Fairness in federated learning for spatial-temporal applications. arXiv preprint arXiv:2201.06598 (2022)."},{"key":"e_1_3_2_1_30_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273\u20131282."},{"key":"e_1_3_2_1_31_1","volume-title":"Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian.","author":"Naveed Humza","year":"2023","unstructured":"Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2023. A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435 (2023)."},{"key":"e_1_3_2_1_32_1","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022) 27730\u201327744."},{"key":"e_1_3_2_1_33_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners. (2019). https:\/\/cdn.openai.com\/better-language-models\/language_models_are_unsupervised_multitask_learners.pdf OpenAI."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","first-page":"19449","DOI":"10.1038\/s41598-023-46887-2","article-title":"Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients","volume":"13","author":"Raimondi Daniele","year":"2023","unstructured":"Daniele Raimondi, Haleh Chizari, Nora Verplaetse, Britt-Sabina L\u00f6scher, Andre Franke, and Yves Moreau. 2023. Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients. Scientific Reports 13, 1 (2023), 19449.","journal-title":"Scientific Reports"},{"key":"e_1_3_2_1_35_1","volume-title":"Adaptive Federated Optimization. In International Conference on Learning Representations.","author":"Reddi Sashank J","unstructured":"Sashank J Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dn\u1ef3, Sanjiv Kumar, and Hugh Brendan McMahan. [n. d.]. Adaptive Federated Optimization. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Micah J Sheller Brandon Edwards G Anthony Reina Jason Martin Sarthak Pati Aikaterini Kotrotsou Mikhail Milchenko Weilin Xu Daniel Marcus Rivka R Colen et al. 2020. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific reports 10 1 (2020) 12598.","DOI":"10.1038\/s41598-020-69250-1"},{"key":"e_1_3_2_1_37_1","volume-title":"Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models. arXiv preprint arXiv:2410.09432","author":"Singhal Raghav","year":"2024","unstructured":"Raghav Singhal, Kaustubh Ponkshe, and Praneeth Vepakomma. 2024. Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models. arXiv preprint arXiv:2410.09432 (2024)."},{"key":"e_1_3_2_1_38_1","volume-title":"Improving loRA in privacy-preserving federated learning. arXiv preprint arXiv:2403.12313","author":"Sun Youbang","year":"2024","unstructured":"Youbang Sun, Zitao Li, Yaliang Li, and Bolin Ding. 2024. Improving loRA in privacy-preserving federated learning. arXiv preprint arXiv:2403.12313 (2024)."},{"key":"e_1_3_2_1_39_1","unstructured":"Ziyao Wang Zheyu Shen Yexiao He Guoheng Sun Hongyi Wang Lingjuan Lyu and Ang Li. 2024. FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. arXiv:2409.05976 [cs.LG] https:\/\/arxiv.org\/abs\/2409.05976"},{"key":"e_1_3_2_1_40_1","volume-title":"Tony QS Quek, and H Vincent Poor","author":"Wei Kang","year":"2020","unstructured":"Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor. 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE transactions on information forensics and security 15 (2020), 3454\u20133469."},{"key":"e_1_3_2_1_41_1","unstructured":"Huiyu Wu and Diego Klabjan. 2024. LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples. arXiv preprint arXiv:2410.19114 (2024)."},{"key":"e_1_3_2_1_42_1","volume-title":"Yin Tat Lee, et al","author":"Xie Chulin","year":"2024","unstructured":"Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, et al. 2024. Differentially private synthetic data via foundation model apis 2: Text. arXiv preprint arXiv:2403.01749 (2024)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100595"},{"key":"e_1_3_2_1_44_1","volume-title":"Bigtranslate: Augmenting large language models with multilingual translation capability over 100 languages. arXiv preprint arXiv:2305.18098","author":"Yang Wen","year":"2023","unstructured":"Wen Yang, Chong Li, Jiajun Zhang, and Chengqing Zong. 2023. Bigtranslate: Augmenting large language models with multilingual translation capability over 100 languages. arXiv preprint arXiv:2305.18098 (2023)."},{"key":"e_1_3_2_1_45_1","unstructured":"Yelp. 2023. Yelp Open Dataset. https:\/\/www.yelp.com\/dataset."},{"key":"e_1_3_2_1_46_1","unstructured":"Da Yu Arturs Backurs Sivakanth Gopi Huseyin Inan Janardhan Kulkarni Zinan Lin Chulin Xie Huishuai Zhang and Wanrong Zhang. 2023. Training private and efficient language models with synthetic data from llms. In Socially Responsible Language Modelling Research."},{"key":"e_1_3_2_1_47_1","volume-title":"International Conference on Machine Learning. PMLR, 7184\u20137193","author":"Yu Hao","year":"2019","unstructured":"Hao Yu, Rong Jin, and Sen Yang. 2019. On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization. In International Conference on Machine Learning. PMLR, 7184\u20137193."},{"key":"e_1_3_2_1_48_1","volume-title":"International Conference on Machine Learning, ICML","author":"Zhang Xinwei","year":"2022","unstructured":"Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Jinfeng Yi. 2022. Understanding clipping for federated learning: Convergence and client-level differential privacy. In International Conference on Machine Learning, ICML 2022."},{"key":"e_1_3_2_1_49_1","volume-title":"Fed-piLot: Optimizing LoRA Assignment for Efficient Federated Foundation Model Fine-Tuning. arXiv preprint arXiv:2410.10200","author":"Zhang Zikai","year":"2024","unstructured":"Zikai Zhang, Jiahao Xu, Ping Liu, and Rui Hu. 2024. Fed-piLot: Optimizing LoRA Assignment for Efficient Federated Foundation Model Fine-Tuning. arXiv preprint arXiv:2410.10200 (2024)."},{"key":"e_1_3_2_1_50_1","unstructured":"Rosie Zhao Depen Morwani David Brandfonbrener Nikhil Vyas and Sham Kakade. 2024. Deconstructing What Makes a Good Optimizer for Language Models. arXiv:2407.07972 [cs.LG] https:\/\/arxiv.org\/abs\/2407.07972"},{"key":"e_1_3_2_1_51_1","unstructured":"Wayne Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang Zican Dong et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)."}],"event":{"name":"PASC '25: Platform for Advanced Scientific Computing Conference","location":"FHNW University of Applied Sciences and Arts Northwestern Switzerland Brugg-Windisch Switzerland","acronym":"PASC '25","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","ETH Zurich \/ CSCS"]},"container-title":["Proceedings of the Platform for Advanced Scientific Computing Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3732775.3733580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T13:51:21Z","timestamp":1750427481000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3732775.3733580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":51,"alternative-id":["10.1145\/3732775.3733580","10.1145\/3732775"],"URL":"https:\/\/doi.org\/10.1145\/3732775.3733580","relation":{},"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"2025-06-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}