{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:49:55Z","timestamp":1771951795437,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":93,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,3,19]]},"DOI":"10.1145\/3497776.3517769","type":"proceedings-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T17:28:13Z","timestamp":1647624493000},"page":"129-143","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Automating reinforcement learning architecture design for code optimization"],"prefix":"10.1145","author":[{"given":"Huanting","family":"Wang","sequence":"first","affiliation":[{"name":"NorthWest University, China \/ University of Leeds, UK"}]},{"given":"Zhanyong","family":"Tang","sequence":"additional","affiliation":[{"name":"NorthWest University, China"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"NorthWest University, China"}]},{"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"NorthWest University, China"}]},{"given":"Chris","family":"Cummins","sequence":"additional","affiliation":[{"name":"Meta AI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0664-4176","authenticated-orcid":false,"given":"Hugh","family":"Leather","sequence":"additional","affiliation":[{"name":"Meta AI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6157-0662","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Leeds, UK"}]}],"member":"320","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. Collective Benchmark. https:\/\/ctuning.org\/wiki\/index.php\/CTools:CBench  [n. d.]. Collective Benchmark. https:\/\/ctuning.org\/wiki\/index.php\/CTools:CBench"},{"key":"e_1_3_2_1_2_1","unstructured":"[n. d.]. llvm-test-suite. https:\/\/github.com\/llvm\/llvm-test-suite  [n. d.]. llvm-test-suite. https:\/\/github.com\/llvm\/llvm-test-suite"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322967"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2006.37"},{"key":"e_1_3_2_1_5_1","volume-title":"Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. In International Conference on Learning Representations. ICLR\u201920","author":"Ahn Byung Hoon","year":"2019","unstructured":"Byung Hoon Ahn , Prannoy Pilligundla , Amir Yazdanbakhsh , and Hadi Esmaeilzadeh . 2019 . Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. In International Conference on Learning Representations. ICLR\u201920 . Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, and Hadi Esmaeilzadeh. 2019. Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. In International Conference on Learning Representations. ICLR\u201920."},{"key":"e_1_3_2_1_6_1","unstructured":"Ilge Akkaya Marcin Andrychowicz Maciek Chociej Mateusz Litwin Bob McGrew Arthur Petron Alex Paino Matthias Plappert Glenn Powell and Raphael Ribas. 2019. Solving rubik\u2019s cube with a robot hand. arXiv preprint arXiv:1910.07113.  Ilge Akkaya Marcin Andrychowicz Maciek Chociej Mateusz Litwin Bob McGrew Arthur Petron Alex Paino Matthias Plappert Glenn Powell and Raphael Ribas. 2019. Solving rubik\u2019s cube with a robot hand. arXiv preprint arXiv:1910.07113."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/998300.997196"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1542476.1542481"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3124452"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2928270"},{"key":"e_1_3_2_1_12_1","volume-title":"Alice Shoshana Jakobovits, and Torsten Hoefler","author":"Ben-Nun Tal","year":"2018","unstructured":"Tal Ben-Nun , Alice Shoshana Jakobovits, and Torsten Hoefler . 2018 . Neural Code Comprehension: A Learnable Representation of Code Semantics. In Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Inc ., 3588\u20133600. http:\/\/papers.nips.cc\/paper\/7617-neural-code-comprehension-a-learnable-representation-of-code-semantics.pdf Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural Code Comprehension: A Learnable Representation of Code Semantics. In Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Inc., 3588\u20133600. http:\/\/papers.nips.cc\/paper\/7617-neural-code-comprehension-a-learnable-representation-of-code-semantics.pdf"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/PACT52795.2021.00009"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2012.2186810"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2007.32"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2854038.2854044"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00110"},{"key":"e_1_3_2_1_18_1","volume-title":"13th $USENIX$ Symposium on Operating Systems Design and Implementation ($OSDI$ 18). 578\u2013594.","author":"Chen Tianqi","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Haichen Shen , Meghan Cowan , Leyuan Wang , Yuwei Hu , and Luis Ceze . 2018. $TVM$ : An automated end-to-end optimizing compiler for deep learning . In 13th $USENIX$ Symposium on Operating Systems Design and Implementation ($OSDI$ 18). 578\u2013594. Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, and Luis Ceze. 2018. $TVM$: An automated end-to-end optimizing compiler for deep learning. In 13th $USENIX$ Symposium on Operating Systems Design and Implementation ($OSDI$ 18). 578\u2013594."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327144.3327258"},{"key":"e_1_3_2_1_20_1","unstructured":"Concertio. [n. d.]. Concertio: Autonomous Optimization Platform.  Concertio. [n. d.]. Concertio: Autonomous Optimization Platform."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2017.24"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Chris Cummins Pavlos Petoumenos Zheng Wang and Hugh Leather. 2017. Synthesizing Benchmarks for Predictive Modeling. In CGO.  Chris Cummins Pavlos Petoumenos Zheng Wang and Hugh Leather. 2017. Synthesizing Benchmarks for Predictive Modeling. In CGO.","DOI":"10.1109\/CGO.2017.7863731"},{"key":"e_1_3_2_1_23_1","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.  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.","DOI":"10.1109\/CGO53902.2022.9741258"},{"key":"e_1_3_2_1_24_1","volume-title":"ANGHABENCH: A Suite with One Million Compilable C Benchmarks for Code-Size Reduction. In 2021 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO). 378\u2013390","author":"da Silva Anderson Faustino","year":"2021","unstructured":"Anderson Faustino da Silva , Bruno Conde Kind , Jos\u00e9 Wesley de Souza Magalh\u00e3es , Jer\u00f4nimo Nunes Rocha , Breno Campos Ferreira Guimaraes , and Fernando Magno Quin\u00e3o Pereira . 2021 . ANGHABENCH: A Suite with One Million Compilable C Benchmarks for Code-Size Reduction. In 2021 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO). 378\u2013390 . Anderson Faustino da Silva, Bruno Conde Kind, Jos\u00e9 Wesley de Souza Magalh\u00e3es, Jer\u00f4nimo Nunes Rocha, Breno Campos Ferreira Guimaraes, and Fernando Magno Quin\u00e3o Pereira. 2021. ANGHABENCH: A Suite with One Million Compilable C Benchmarks for Code-Size Reduction. In 2021 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO). 378\u2013390."},{"key":"e_1_3_2_1_25_1","volume-title":"International Symposium on Code Generation and Optimization (CGO\u201907)","author":"Davidson JW","year":"2007","unstructured":"JW Davidson , Gary S Tyson , DB Whalley , and PA Kulkarni . 2007 . Evaluating heuristic optimization phase order search algorithms . In International Symposium on Code Generation and Optimization (CGO\u201907) . 157\u2013169. JW Davidson, Gary S Tyson, DB Whalley, and PA Kulkarni. 2007. Evaluating heuristic optimization phase order search algorithms. In International Symposium on Code Generation and Optimization (CGO\u201907). 157\u2013169."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2541940.2541941"},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation.","author":"Ding Yufei","unstructured":"Yufei Ding , Jason Ansel , Kalyan Veeramachaneni , Xipeng Shen , Una-May O\u2019Reilly , and Saman P. Amarasinghe . 2015. Autotuning algorithmic choice for input sensitivity . Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation. Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O\u2019Reilly, and Saman P. Amarasinghe. 2015. Autotuning algorithmic choice for input sensitivity. Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/3322706.3361996"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-010-9213-y"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2004.840301"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2013.6494993"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314221.3314652"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368826.3377928"},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of Machine Learning and Systems","author":"Haj-Ali Ameer","year":"2020","unstructured":"Ameer Haj-Ali , Qijing (Jenny) Huang , William S. Moses , John Xiang , Krste Asanovic , John Wawrzynek , and Ion Stoica . 2020 . AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning . In Proceedings of Machine Learning and Systems 2020, MLSys, Inderjit S. Dhillon, Dimitris S. Papailiopoulos, and Vivienne Sze (Eds.). Ameer Haj-Ali, Qijing (Jenny) Huang, William S. Moses, John Xiang, Krste Asanovic, John Wawrzynek, and Ion Stoica. 2020. AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning. In Proceedings of Machine Learning and Systems 2020, MLSys, Inderjit S. Dhillon, Dimitris S. Papailiopoulos, and Vivienne Sze (Eds.)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013796"},{"key":"e_1_3_2_1_39_1","volume-title":"Long short-term memory. Neural computation, 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation, 9, 8 ( 1997 ), 1735\u20131780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9, 8 (1997), 1735\u20131780."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Qijing Huang Ameer Haj-Ali William Moses John Xiang Ion Stoica Krste Asanovic and John Wawrzynek. 2020. AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning. arxiv:2003.00671.  Qijing Huang Ameer Haj-Ali William Moses John Xiang Ion Stoica Krste Asanovic and John Wawrzynek. 2020. AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning. arxiv:2003.00671.","DOI":"10.1109\/FCCM.2019.00049"},{"key":"e_1_3_2_1_41_1","unstructured":"Max Jaderberg Valentin Dalibard Simon Osindero Wojciech M Czarnecki Jeff Donahue Ali Razavi Oriol Vinyals Tim Green Iain Dunning and Karen Simonyan. 2017. Population based training of neural networks. arXiv preprint arXiv:1711.09846.  Max Jaderberg Valentin Dalibard Simon Osindero Wojciech M Czarnecki Jeff Donahue Ali Razavi Oriol Vinyals Tim Green Iain Dunning and Karen Simonyan. 2017. Population based training of neural networks. arXiv preprint arXiv:1711.09846."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458744.3473355"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/512529.512566"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622737.1622748"},{"key":"e_1_3_2_1_45_1","unstructured":"Shauharda Khadka Estelle Aflalo Mattias Marder Avrech Ben-David Santiago Miret Shie Mannor Tamir Hazan Hanlin Tang and Somdeb Majumdar. 2021. Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. ICLR.  Shauharda Khadka Estelle Aflalo Mattias Marder Avrech Ben-David Santiago Miret Shie Mannor Tamir Hazan Hanlin Tang and Somdeb Majumdar. 2021. Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning. ICLR."},{"key":"e_1_3_2_1_46_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25 ( 2012 ), 1097\u20131105. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25 (2012), 1097\u20131105."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/996893.996863"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2384616.2384628"},{"key":"e_1_3_2_1_49_1","volume-title":"Proceedings of the 31st International Conference on Machine Learning, Eric P. Xing and Tony Jebara (Eds.) (Proceedings of Machine Learning Research","volume":"1196","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov . 2014 . Distributed Representations of Sentences and Documents . In Proceedings of the 31st International Conference on Machine Learning, Eric P. Xing and Tony Jebara (Eds.) (Proceedings of Machine Learning Research , Vol. 32). PMLR, Bejing, China. 1188\u2013 1196 . Quoc Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. In Proceedings of the 31st International Conference on Machine Learning, Eric P. Xing and Tony Jebara (Eds.) (Proceedings of Machine Learning Research, Vol. 32). PMLR, Bejing, China. 1188\u20131196."},{"key":"e_1_3_2_1_50_1","volume-title":"AdaTune: Adaptive Tensor Program Compilation Made Efficient. Advances in Neural Information Processing Systems, 33","author":"Li Menghao","year":"2020","unstructured":"Menghao Li , Minjia Zhang , Chi Wang , and Mingqin Li. 2020. AdaTune: Adaptive Tensor Program Compilation Made Efficient. Advances in Neural Information Processing Systems, 33 ( 2020 ). Menghao Li, Minjia Zhang, Chi Wang, and Mingqin Li. 2020. AdaTune: Adaptive Tensor Program Compilation Made Efficient. Advances in Neural Information Processing Systems, 33 (2020)."},{"key":"e_1_3_2_1_51_1","volume-title":"International Conference on Machine Learning. 3053\u20133062","author":"Liang Eric","year":"2018","unstructured":"Eric Liang , Richard Liaw , Robert Nishihara , Philipp Moritz , Roy Fox , Ken Goldberg , Joseph Gonzalez , Michael Jordan , and Ion Stoica . 2018 . RLlib: Abstractions for distributed reinforcement learning . In International Conference on Machine Learning. 3053\u20133062 . Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. 2018. RLlib: Abstractions for distributed reinforcement learning. In International Conference on Machine Learning. 3053\u20133062."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441621"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/LLVMHPCHiPar51896.2020.00006"},{"key":"e_1_3_2_1_54_1","volume-title":"Zili Meng, and Mohammad Alizadeh.","author":"Mao Hongzi","year":"2019","unstructured":"Hongzi Mao , Malte Schwarzkopf , Shaileshh Bojja Venkatakrishnan , Zili Meng, and Mohammad Alizadeh. 2019 . Learning Scheduling Algorithms for Data Processing Clusters. In Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM \u201919). 270\u2013288. Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2019. Learning Scheduling Algorithms for Data Processing Clusters. In Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM \u201919). 270\u2013288."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/36177.36194"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00090"},{"key":"e_1_3_2_1_57_1","volume-title":"1st International Conference on Learning Representations, ICLR","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013. Efficient Estimation of Word Representations in Vector Space . In 1st International Conference on Learning Representations, ICLR 2013 , Scottsdale, Arizona, USA , May 2-4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). arxiv:1301.3781 Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). arxiv:1301.3781"},{"key":"e_1_3_2_1_58_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arxiv:1301.3781.  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arxiv:1301.3781."},{"key":"e_1_3_2_1_59_1","volume-title":"International conference on machine learning. 1928\u20131937","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih , Adria Puigdomenech Badia , Mehdi Mirza , Alex Graves , Timothy Lillicrap , Tim Harley , David Silver , and Koray Kavukcuoglu . 2016 . Asynchronous methods for deep reinforcement learning . In International conference on machine learning. 1928\u20131937 . Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. 1928\u20131937."},{"key":"e_1_3_2_1_60_1","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.  Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437984.3458840"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925952"},{"key":"e_1_3_2_1_63_1","volume-title":"International Workshop on Languages and Compilers for Parallel Computing. 146\u2013160","author":"Ogilvie William F","year":"2014","unstructured":"William F Ogilvie , Pavlos Petoumenos , Zheng Wang , and Hugh Leather . 2014 . Fast automatic heuristic construction using active learning . In International Workshop on Languages and Compilers for Parallel Computing. 146\u2013160 . William F Ogilvie, Pavlos Petoumenos, Zheng Wang, and Hugh Leather. 2014. Fast automatic heuristic construction using active learning. In International Workshop on Languages and Compilers for Parallel Computing. 146\u2013160."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/3049832.3049859"},{"key":"e_1_3_2_1_65_1","unstructured":"OpenAI. 2020. Gym: a toolkit for developing and comparing reinforcement learning algorithms. https:\/\/gym.openai.com\/  OpenAI. 2020. Gym: a toolkit for developing and comparing reinforcement learning algorithms. https:\/\/gym.openai.com\/"},{"key":"e_1_3_2_1_66_1","volume-title":"Openai five","author":"Pachocki Jakub","year":"2018","unstructured":"Jakub Pachocki , Greg Brockman , Jonathan Raiman , Susan Zhang , Henrique Pond\u00e9 , Jie Tang , Filip Wolski , Christy Dennison , Rafal Jozefowicz , and Przemyslaw Debiak . 2018. Openai five , 2018 . URL https:\/\/blog. openai. com\/openai-five. Jakub Pachocki, Greg Brockman, Jonathan Raiman, Susan Zhang, Henrique Pond\u00e9, Jie Tang, Filip Wolski, Christy Dennison, Rafal Jozefowicz, and Przemyslaw Debiak. 2018. Openai five, 2018. URL https:\/\/blog. openai. com\/openai-five."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10766-013-0241-1"},{"key":"e_1_3_2_1_68_1","volume-title":"Andr\u00e9 Murbach Maidl, and Daniel Weingaertner","author":"Pecenin Marcelo","year":"2019","unstructured":"Marcelo Pecenin , Andr\u00e9 Murbach Maidl, and Daniel Weingaertner . 2019 . Optimization of Halide Image Processing Schedules with Reinforcement Learning. In Anais do XX Simp\u00f3sio em Sistemas Computacionais de Alto Desempenho . 37\u201348. Marcelo Pecenin, Andr\u00e9 Murbach Maidl, and Daniel Weingaertner. 2019. Optimization of Halide Image Processing Schedules with Reinforcement Learning. In Anais do XX Simp\u00f3sio em Sistemas Computacionais de Alto Desempenho. 37\u201348."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_3_2_1_70_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC \u201918)","author":"Rawat Prashant Singh","unstructured":"Prashant Singh Rawat , Aravind Sukumaran-Rajam , Atanas Rountev , Fabrice Rastello , Louis-No\u00ebl Pouchet , and P. Sadayappan . 2018. Associative Instruction Reordering to Alleviate Register Pressure . In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC \u201918) . IEEE Press, Article 46, 13 pages. Prashant Singh Rawat, Aravind Sukumaran-Rajam, Atanas Rountev, Fabrice Rastello, Louis-No\u00ebl Pouchet, and P. Sadayappan. 2018. Associative Instruction Reordering to Alleviate Register Pressure. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC \u201918). IEEE Press, Article 46, 13 pages."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155489"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461648.3463852"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.5555\/3314872.3314892"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385412.3386030"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454109"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/2451116.2451150"},{"key":"e_1_3_2_1_77_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.  John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347."},{"key":"e_1_3_2_1_78_1","unstructured":"David Silver Thomas Hubert Julian Schrittwieser Ioannis Antonoglou Matthew Lai Arthur Guez Marc Lanctot Laurent Sifre Dharshan Kumaran and Thore Graepel. 2017. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815.  David Silver Thomas Hubert Julian Schrittwieser Ioannis Antonoglou Matthew Lai Arthur Guez Marc Lanctot Laurent Sifre Dharshan Kumaran and Thore Graepel. 2017. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6404"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/781131.781141"},{"key":"e_1_3_2_1_81_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 2018. Reinforcement learning: An introduction . MIT press . Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"e_1_3_2_1_83_1","unstructured":"Mircea Trofin Yundi Qian Eugene Brevdo Zinan Lin Krzysztof Choromanski and David Li. 2021. MLGO: a Machine Learning Guided Compiler Optimizations Framework. arXiv preprint arXiv:2101.04808.  Mircea Trofin Yundi Qian Eugene Brevdo Zinan Lin Krzysztof Choromanski and David Li. 2021. MLGO: a Machine Learning Guided Compiler Optimizations Framework. arXiv preprint arXiv:2101.04808."},{"key":"e_1_3_2_1_84_1","unstructured":"Hado P van Hasselt Arthur Guez Arthur Guez Matteo Hessel Volodymyr Mnih and David Silver. 2016. Learning values across many orders of magnitude. In Advances in Neural Information Processing Systems D. Lee M. Sugiyama U. Luxburg I. Guyon and R. Garnett (Eds.). 29.  Hado P van Hasselt Arthur Guez Arthur Guez Matteo Hessel Volodymyr Mnih and David Silver. 2016. Learning values across many orders of magnitude. In Advances in Neural Information Processing Systems D. Lee M. Sugiyama U. Luxburg I. Guyon and R. Garnett (Eds.). 29."},{"key":"e_1_3_2_1_85_1","article-title":"Dynamic GPU Energy Optimization for Machine Learning Training Workloads","author":"Wang Farui","year":"2021","unstructured":"Farui Wang , Weizhe Zhang , Shichao Lai , Meng Hao , and Zheng Wang . 2021 . Dynamic GPU Energy Optimization for Machine Learning Training Workloads . IEEE Transactions on Parallel and Distributed Systems. Farui Wang, Weizhe Zhang, Shichao Lai, Meng Hao, and Zheng Wang. 2021. Dynamic GPU Energy Optimization for Machine Learning Training Workloads. IEEE Transactions on Parallel and Distributed Systems.","journal-title":"IEEE Transactions on Parallel and Distributed Systems."},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3044773"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3092270"},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2817118"},{"key":"e_1_3_2_1_89_1","volume-title":"SC\u201998: Proceedings of the 1998 ACM\/IEEE conference on Supercomputing. 38\u201338","author":"Clinton Whaley R","year":"1998","unstructured":"R Clinton Whaley and Jack J Dongarra . 1998 . Automatically tuned linear algebra software . In SC\u201998: Proceedings of the 1998 ACM\/IEEE conference on Supercomputing. 38\u201338 . R Clinton Whaley and Jack J Dongarra. 1998. Automatically tuned linear algebra software. In SC\u201998: Proceedings of the 1998 ACM\/IEEE conference on Supercomputing. 38\u201338."},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410463.3414670"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483253"},{"key":"e_1_3_2_1_92_1","volume-title":"Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, and Koushik Sen.","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng , Chengfan Jia , Minmin Sun , Zhao Wu , Cody Hao Yu , Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, and Koushik Sen. 2020 . Ansor : Generating high-performance tensor programs for deep learning. In 14th $USENIX$ Symposium on Operating Systems Design and Implementation ( $OSDI$ 20). 863\u2013879. Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, and Koushik Sen. 2020. Ansor: Generating high-performance tensor programs for deep learning. In 14th $USENIX$ Symposium on Operating Systems Design and Implementation ($OSDI$ 20). 863\u2013879."},{"key":"e_1_3_2_1_93_1","unstructured":"Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578.  Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578."}],"event":{"name":"CC '22: 31st ACM SIGPLAN International Conference on Compiler Construction","location":"Seoul South Korea","acronym":"CC '22","sponsor":["SIGPLAN ACM Special Interest Group on Programming Languages"]},"container-title":["Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3497776.3517769","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3497776.3517769","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:26Z","timestamp":1750193366000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3497776.3517769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":93,"alternative-id":["10.1145\/3497776.3517769","10.1145\/3497776"],"URL":"https:\/\/doi.org\/10.1145\/3497776.3517769","relation":{},"subject":[],"published":{"date-parts":[[2022,3,18]]},"assertion":[{"value":"2022-03-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}