{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:40:16Z","timestamp":1767987616921,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"vor","delay-in-days":440,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-2106771"],"award-info":[{"award-number":["CNS-2106771"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,8]]},"DOI":"10.1145\/3721145.3725765","type":"proceedings-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:57:17Z","timestamp":1755867437000},"page":"943-958","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DeCOS: Data-Efficient Reinforcement Learning for Compiler Optimization Selection Ignited by LLM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0515-1396","authenticated-orcid":false,"given":"Tianming","family":"Cui","sequence":"first","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9653-8777","authenticated-orcid":false,"given":"Pen-Chung","family":"Yew","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6859-9758","authenticated-orcid":false,"given":"Stephen","family":"McCamant","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-1415","authenticated-orcid":false,"given":"Antonia","family":"Zhai","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2006.37"},{"key":"e_1_3_3_1_3_2","unstructured":"Byung\u00a0Hoon Ahn Prannoy Pilligundla Amir Yazdanbakhsh and Hadi Esmaeilzadeh. 2020. Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. arxiv:https:\/\/arXiv.org\/abs\/2001.08743\u00a0[cs.LG]"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"L. Almagor Keith\u00a0D. Cooper Alexander Grosul Timothy\u00a0J. Harvey Steven\u00a0W. Reeves Devika Subramanian Linda Torczon and Todd Waterman. 2004. Finding Effective Compilation Sequences. SIGPLAN Not. 39 7 (jun 2004) 231\u2013239. https:\/\/doi.org\/10.1145\/998300.997196","DOI":"10.1145\/998300.997196"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Uri Alon Meital Zilberstein Omer Levy and Eran Yahav. 2018. code2vec: Learning Distributed Representations of Code. arxiv:https:\/\/arXiv.org\/abs\/1803.09473\u00a0[cs.LG]","DOI":"10.1145\/3290353"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Amir\u00a0H. Ashouri William Killian John Cavazos Gianluca Palermo and Cristina Silvano. 2018. A Survey on Compiler Autotuning Using Machine Learning. ACM Comput. Surv. 51 5 Article 96 (sep 2018) 42\u00a0pages. https:\/\/doi.org\/10.1145\/3197978","DOI":"10.1145\/3197978"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3185768.3185771"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2007.32"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Keith\u00a0D. Cooper Devika Subramanian and Linda Torczon. 2002. Adaptive Optimizing Compilers for the 21st Century. J. Supercomput. 23 1 (aug 2002) 7\u201322. https:\/\/doi.org\/10.1023\/A:1015729001611","DOI":"10.1023\/A:1015729001611"},{"key":"e_1_3_3_1_11_2","unstructured":"Chris Cummins Volker Seeker Dejan Grubisic Mostafa Elhoushi Youwei Liang Baptiste Roziere Jonas Gehring Fabian Gloeckle Kim Hazelwood Gabriel Synnaeve and Hugh Leather. 2023. Large Language Models for Compiler Optimization. arxiv:https:\/\/arXiv.org\/abs\/2309.07062\u00a0[cs.PL] https:\/\/arxiv.org\/abs\/2309.07062"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Chris Cummins Bram Wasti Jiadong Guo Brandon Cui Jason Ansel Sahir Gomez Somya Jain Jia Liu Olivier Teytaud Benoit Steiner Yuandong Tian and Hugh Leather. 2021. CompilerGym: Robust Performant Compiler Optimization Environments for AI Research. arxiv:https:\/\/arXiv.org\/abs\/2109.08267\u00a0[cs.PL]","DOI":"10.1109\/CGO53902.2022.9741258"},{"key":"e_1_3_3_1_13_2","volume-title":"GCC Summit","author":"Fursin Grigori","year":"2008","unstructured":"Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson, Edwin Bonilla, John Thomson, Hugh Leather, Chris Williams, Michael O\u2019Boyle, Phil Barnard, Elton Ashton, Eric Courtois, and Fran\u00e7ois Bodin. 2008. MILEPOST GCC: machine learning based research compiler. In GCC Summit. Ottawa, Canada."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE55347.2025.00021"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/WWC.2001.990739"},{"key":"e_1_3_3_1_16_2","unstructured":"Ameer Haj\u00a0Ali. 2021. Machine Learning in Compiler Optimization. Ph.\u00a0D. Dissertation. EECS Department University of California Berkeley."},{"key":"e_1_3_3_1_17_2","unstructured":"Q. Huang A. W. Moses J. Xiang I. Stoica K. Asanovic and J. Wawrzynek. 2020. AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning. arxiv:https:\/\/arXiv.org\/abs\/2003.00671\u00a0[cs.DC]"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2019.00049"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2013.50"},{"key":"e_1_3_3_1_20_2","unstructured":"Timo Kaufmann Paul Weng Viktor Bengs and Eyke H\u00fcllermeier. 2024. A Survey of Reinforcement Learning from Human Feedback. arxiv:https:\/\/arXiv.org\/abs\/2312.14925\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2312.14925"},{"key":"e_1_3_3_1_21_2","unstructured":"Scott\u00a0Robert Ladd. 2004. ACOVEA (Analysis of Compiler Options via Evolutionary Algorithm). https:\/\/github.com\/Acovea\/libacovea"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Chris Lattner and Vikram Adve. 2004. LLVM: A Compilation Framework for Lifelong Program Analysis and Transformation. San Jose CA USA 75\u201388.","DOI":"10.1109\/CGO.2004.1281665"},{"key":"e_1_3_3_1_23_2","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. https:\/\/doi.org\/10.48550\/ARXIV.1301.3781"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.5555\/2371238"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Nicholas Nethercote and Julian Seward. 2007. Valgrind: A Framework for Heavyweight Dynamic Binary Instrumentation. SIGPLAN Not. 42 6 (jun 2007) 89\u2013100. https:\/\/doi.org\/10.1145\/1273442.1250746","DOI":"10.1145\/1273442.1250746"},{"key":"e_1_3_3_1_26_2","unstructured":"OpenAI Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia\u00a0Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat Red Avila Igor Babuschkin Suchir Balaji Valerie Balcom Paul Baltescu Haiming Bao Mohammad Bavarian Jeff Belgum Irwan Bello Jake Berdine Gabriel Bernadett-Shapiro Christopher Berner Lenny Bogdonoff Oleg Boiko Madelaine Boyd Anna-Luisa Brakman Greg Brockman Tim Brooks Miles Brundage Kevin Button Trevor Cai Rosie Campbell Andrew Cann Brittany Carey Chelsea Carlson Rory Carmichael Brooke Chan Che Chang Fotis Chantzis Derek Chen Sully Chen Ruby Chen Jason Chen Mark Chen Ben Chess Chester Cho Casey Chu Hyung\u00a0Won Chung Dave Cummings Jeremiah Currier Yunxing Dai Cory Decareaux Thomas Degry Noah Deutsch Damien Deville Arka Dhar David Dohan Steve Dowling Sheila Dunning Adrien Ecoffet Atty Eleti Tyna Eloundou David Farhi Liam Fedus Niko Felix Sim\u00f3n\u00a0Posada Fishman Juston Forte Isabella Fulford Leo Gao Elie Georges Christian Gibson Vik Goel Tarun Gogineni Gabriel Goh Rapha Gontijo-Lopes Jonathan Gordon Morgan Grafstein Scott Gray Ryan Greene Joshua Gross Shixiang\u00a0Shane Gu Yufei Guo Chris Hallacy Jesse Han Jeff Harris Yuchen He Mike Heaton Johannes Heidecke Chris Hesse Alan Hickey Wade Hickey Peter Hoeschele Brandon Houghton Kenny Hsu Shengli Hu Xin Hu Joost Huizinga Shantanu Jain Shawn Jain Joanne Jang Angela Jiang Roger Jiang Haozhun Jin Denny Jin Shino Jomoto Billie Jonn Heewoo Jun Tomer Kaftan \u0141ukasz Kaiser Ali Kamali Ingmar Kanitscheider Nitish\u00a0Shirish Keskar Tabarak Khan Logan Kilpatrick Jong\u00a0Wook Kim Christina Kim Yongjik Kim Jan\u00a0Hendrik Kirchner Jamie Kiros Matt Knight Daniel Kokotajlo \u0141ukasz Kondraciuk Andrew Kondrich Aris Konstantinidis Kyle Kosic Gretchen Krueger Vishal Kuo Michael Lampe Ikai Lan Teddy Lee Jan Leike Jade Leung Daniel Levy Chak\u00a0Ming Li Rachel Lim Molly Lin Stephanie Lin Mateusz Litwin Theresa Lopez Ryan Lowe Patricia Lue Anna Makanju Kim Malfacini Sam Manning Todor Markov Yaniv Markovski Bianca Martin Katie Mayer Andrew Mayne Bob McGrew Scott\u00a0Mayer McKinney Christine McLeavey Paul McMillan Jake McNeil David Medina Aalok Mehta Jacob Menick Luke Metz Andrey Mishchenko Pamela Mishkin Vinnie Monaco Evan Morikawa Daniel Mossing Tong Mu Mira Murati Oleg Murk David M\u00e9ly Ashvin Nair Reiichiro Nakano Rajeev Nayak Arvind Neelakantan Richard Ngo Hyeonwoo Noh Long Ouyang Cullen O\u2019Keefe Jakub Pachocki Alex Paino Joe Palermo Ashley Pantuliano Giambattista Parascandolo Joel Parish Emy Parparita Alex Passos Mikhail Pavlov Andrew Peng Adam Perelman Filipe de Avila Belbute\u00a0Peres Michael Petrov Henrique\u00a0Ponde de Oliveira\u00a0Pinto Michael Pokorny Michelle Pokrass Vitchyr\u00a0H. Pong Tolly Powell Alethea Power Boris Power Elizabeth Proehl Raul Puri Alec Radford Jack Rae Aditya Ramesh Cameron Raymond Francis Real Kendra Rimbach Carl Ross Bob Rotsted Henri Roussez Nick Ryder Mario Saltarelli Ted Sanders Shibani Santurkar Girish Sastry Heather Schmidt David Schnurr John Schulman Daniel Selsam Kyla Sheppard Toki Sherbakov Jessica Shieh Sarah Shoker Pranav Shyam Szymon Sidor Eric Sigler Maddie Simens Jordan Sitkin Katarina Slama Ian Sohl Benjamin Sokolowsky Yang Song Natalie Staudacher Felipe\u00a0Petroski Such Natalie Summers Ilya Sutskever Jie Tang Nikolas Tezak Madeleine\u00a0B. Thompson Phil Tillet Amin Tootoonchian Elizabeth Tseng Preston Tuggle Nick Turley Jerry Tworek Juan Felipe\u00a0Cer\u00f3n Uribe Andrea Vallone Arun Vijayvergiya Chelsea Voss Carroll Wainwright Justin\u00a0Jay Wang Alvin Wang Ben Wang Jonathan Ward Jason Wei CJ Weinmann Akila Welihinda Peter Welinder Jiayi Weng Lilian Weng Matt Wiethoff Dave Willner Clemens Winter Samuel Wolrich Hannah Wong Lauren Workman Sherwin Wu Jeff Wu Michael Wu Kai Xiao Tao Xu Sarah Yoo Kevin Yu Qiming Yuan Wojciech Zaremba Rowan Zellers Chong Zhang Marvin Zhang Shengjia Zhao Tianhao Zheng Juntang Zhuang William Zhuk and Barret Zoph. 2024. GPT-4 Technical Report. arxiv:https:\/\/arXiv.org\/abs\/2303.08774\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"e_1_3_3_1_27_2","volume-title":"Artificial Intelligence: A Modern Approach (3rd ed.)","author":"Russell Stuart","year":"2009","unstructured":"Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, USA."},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2016.7482078"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2005.29"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"R.S. Sutton and A.G. Barto. 1998. Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks 9 5 (1998) 1054\u20131054. https:\/\/doi.org\/10.1109\/TNN.1998.712192","DOI":"10.1109\/TNN.1998.712192"},{"key":"e_1_3_3_1_31_2","unstructured":"Burak Ta\u011ftekin Berkan H\u00f6ke Mert\u00a0Kutay Sezer and Mahiye\u00a0Uluya\u011fmur \u00d6zt\u00fcrk. 2021. FOGA: Flag Optimization with Genetic Algorithm. arxiv:https:\/\/arXiv.org\/abs\/2105.07202\u00a0[cs.NE]"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2003.1191546"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"S. VenkataKeerthy Rohit Aggarwal Shalini Jain Maunendra\u00a0Sankar Desarkar Ramakrishna Upadrasta and Y.\u00a0N. Srikant. 2020. IR2VEC: LLVM IR Based Scalable Program Embeddings. ACM Trans. Archit. Code Optim. 17 4 Article 32 (dec 2020) 27\u00a0pages. https:\/\/doi.org\/10.1145\/3418463","DOI":"10.1145\/3418463"},{"key":"e_1_3_3_1_34_2","unstructured":"Zheng Wang and Michael O\u2019Boyle. 2018. Machine Learning in Compiler Optimisation. arxiv:https:\/\/arXiv.org\/abs\/1805.03441\u00a0[cs.PL]"},{"key":"e_1_3_3_1_35_2","volume-title":"International Conference on Learning Representations","author":"Zhang Minjia","year":"2021","unstructured":"Minjia Zhang, Menghao Li, Chi Wang, and Mingqin Li. 2021. DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation. In International Conference on Learning Representations."}],"event":{"name":"ICS '25: 2025 International Conference on Supercomputing","location":"Salt Lake City USA","acronym":"ICS '25","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 39th ACM International Conference on Supercomputing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721145.3725765","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721145.3725765","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:00:43Z","timestamp":1755867643000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721145.3725765"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":34,"alternative-id":["10.1145\/3721145.3725765","10.1145\/3721145"],"URL":"https:\/\/doi.org\/10.1145\/3721145.3725765","relation":{},"subject":[],"published":{"date-parts":[[2025,6,8]]},"assertion":[{"value":"2025-08-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}