{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:49:51Z","timestamp":1771951791827,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,8]]},"DOI":"10.1145\/3721145.3725766","type":"proceedings-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:57:17Z","timestamp":1755867437000},"page":"959-974","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Pearl: Automatic Code Optimization Using Deep Reinforcement Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6471-5732","authenticated-orcid":false,"given":"Djamel Rassem","family":"Lamouri","sequence":"first","affiliation":[{"name":"New York University Abu Dhabi, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9541-9179","authenticated-orcid":false,"given":"Iheb Nassim","family":"Aouadj","sequence":"additional","affiliation":[{"name":"New York University Abu Dhabi, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4644-2886","authenticated-orcid":false,"given":"Smail","family":"Kourta","sequence":"additional","affiliation":[{"name":"New York University Abu Dhabi, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9350-3998","authenticated-orcid":false,"given":"Riyadh","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"New York University Abu Dhabi, Abu Dhabi, United Arab Emirates"}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","unstructured":"Andrew Adams Karima Ma Luke Anderson Riyadh Baghdadi Tzu-Mao Li Micha\u00ebl Gharbi Benoit Steiner Steven Johnson Kayvon Fatahalian Fr\u00e9do Durand and Jonathan Ragan-Kelley. 2019. Learning to Optimize Halide with Tree Search and Random Programs. ACM Trans. Graph. 38 4 Article 121 (jul 2019) 12\u00a0pages. 10.1145\/3306346.3322967","DOI":"10.1145\/3306346.3322967"},{"key":"e_1_3_3_2_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"},{"key":"e_1_3_3_2_4_2","unstructured":"Mohamed\u00a0Riyadh Baghdadi. 2015. Improving tiling reducing compilation time and extending the scope of polyhedral compilation. Ph.\u00a0D. Dissertation. Paris 6."},{"key":"e_1_3_3_2_5_2","unstructured":"Riyadh Baghdadi Albert Cohen Cedric Bastoul Louis-Noel Pouchet and Lawrence Rauchwerger. 2011. The Potential of Synergistic Static Dynamic and Speculative Loop Nest Optimizations for Automatic Parallelization. arxiv:https:\/\/arXiv.org\/abs\/1111.6756\u00a0[cs.DC]"},{"key":"e_1_3_3_2_6_2","unstructured":"Riyadh Baghdadi Albert Cohen Tobias Grosser Sven Verdoolaege Javed Absar Sven Van\u00a0Haastregt Alexey Kravets Anton Lokhmotov and Alastair Donaldson. 2015. PENCIL Language Specification. Ph.\u00a0D. Dissertation. INRIA."},{"key":"e_1_3_3_2_7_2","unstructured":"Riyadh Baghdadi Albert Cohen Serge Guelton Sven Verdoolaege Jun Inoue Tobias Grosser Georgia Kouveli Alexey Kravets Anton Lokhmotov Cedric Nugteren et\u00a0al. 2013. PENCIL: Towards a platform-neutral compute intermediate language for DSLs. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1302.5586 (2013)."},{"key":"e_1_3_3_2_8_2","unstructured":"Riyadh Baghdadi Abdelkader\u00a0Nadir Debbagh Kamel Abdous Fatima\u00a0Zohra Benhamida Alex Renda Jonathan\u00a0Elliott Frankle Michael Carbin and Saman Amarasinghe. 2020. TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning. arxiv:https:\/\/arXiv.org\/abs\/2005.04091\u00a0[cs.DC]"},{"key":"e_1_3_3_2_9_2","unstructured":"Riyadh Baghdadi Massinissa Merouani Mohamed-Hicham Leghettas Kamel Abdous Taha Arbaoui Karima Benatchba et\u00a0al. 2021. A deep learning based cost model for automatic code optimization. Proceedings of Machine Learning and Systems 3 (2021) 181\u2013193."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2019.8661197"},{"key":"e_1_3_3_2_11_2","unstructured":"Riyadh Baghdadi Jessica Ray Malek\u00a0Ben Romdhane Emanuele Del\u00a0Sozzo Patricia Suriana Shoaib Kamil and Saman\u00a0P Amarasinghe. 2018. Tiramisu: A code optimization framework for high performance systems. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1804.10694 (2018)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/1375581.1375595"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/1375581.1375595"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/1375581.1375595"},{"key":"e_1_3_3_2_15_2","unstructured":"Alexander Brauckmann Andr\u00e9s Goens and Jeronimo Castrillon. 2021. A reinforcement learning environment for polyhedral optimizations. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2104.13732 (2021)."},{"key":"e_1_3_3_2_16_2","unstructured":"Shaked Brody Uri Alon and Eran Yahav. 2022. How Attentive are Graph Attention Networks? arxiv:https:\/\/arXiv.org\/abs\/2105.14491\u00a0[cs.LG]"},{"key":"e_1_3_3_2_17_2","unstructured":"Tianqi Chen Thierry Moreau Ziheng Jiang Lianmin Zheng Eddie Yan Meghan Cowan Haichen Shen Leyuan Wang Yuwei Hu Luis Ceze et\u00a0al. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1802.04799 (2018)."},{"key":"e_1_3_3_2_18_2","unstructured":"Tianqi Chen Lianmin Zheng Eddie Yan Ziheng Jiang Thierry Moreau Luis Ceze Carlos Guestrin and Arvind Krishnamurthy. 2019. Learning to Optimize Tensor Programs. arxiv:https:\/\/arXiv.org\/abs\/1805.08166"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CGO53902.2022.9741258"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","unstructured":"Alain Darte and Guillaume Huard. 2005. New Complexity Results on Array Contraction and Related Problems. J. VLSI Signal Process. Syst. 40 1 (May 2005) 35\u201355. 10.1007\/s11265-005-4937-3","DOI":"10.1007\/s11265-005-4937-3"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/55364.55406"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-09766-4_502"},{"key":"e_1_3_3_2_23_2","first-page":"1263","volume-title":"International conference on machine learning","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel\u00a0S Schoenholz, Patrick\u00a0F Riley, Oriol Vinyals, and George\u00a0E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263\u20131272."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/2581122.2544160"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Tobias Grosser Armin Groslinger and Christian Lengauer. 2012. Polly - Performing Polyhedral Optimizations on a Low-Level Intermediate Representation. Parallel Processing Letters 22 4 (2012). http:\/\/dblp.uni-trier.de\/db\/journals\/ppl\/ppl22.html#GrosserGL12","DOI":"10.1142\/S0129626412500107"},{"key":"e_1_3_3_2_26_2","unstructured":"Ameer Haj-Ali Hasan Genc Qijing Huang William Moses John Wawrzynek Krste Asanovi\u0107 and Ion Stoica. 2020. Protuner: tuning programs with monte carlo tree search. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2005.13685 (2020)."},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Yacine Hakimi Riyadh Baghdadi and Yacine Challal. 2023. A hybrid machine learning model for code optimization. International Journal of Parallel Programming 51 6 (2023) 309\u2013331.","DOI":"10.1007\/s10766-023-00758-5"},{"key":"e_1_3_3_2_28_2","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_29_2","unstructured":"Guoliang He Sean Parker and Eiko Yoneki. 2023. X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation. arxiv:https:\/\/arXiv.org\/abs\/2304.14698\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2304.14698"},{"key":"e_1_3_3_2_30_2","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 preprint arXiv:https:\/\/arXiv.org\/abs\/2003.00671 (2020)."},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3497776.3517769"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/73560.73588"},{"key":"e_1_3_3_2_33_2","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1609.02907 (2016)."},{"key":"e_1_3_3_2_34_2","unstructured":"G\u00fcnter Klambauer Thomas Unterthiner Andreas Mayr and Sepp Hochreiter. 2017. Self-normalizing neural networks. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","unstructured":"Vincent Lefebvre and Paul Feautrier. 1998. Automatic storage management for parallel programs. Parallel Comput. 24 (1998) 649\u2013671. 10.1016\/S0167-8191(98)00029-5","DOI":"10.1016\/S0167-8191(98)00029-5"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3708493.3712683"},{"key":"e_1_3_3_2_37_2","first-page":"1025","volume-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Liu Yizhi","year":"2019","unstructured":"Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang. 2019. Optimizing { CNN} model inference on { CPUs}. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). 1025\u20131040."},{"key":"e_1_3_3_2_38_2","unstructured":"Moshe Looks Marcello Herreshoff DeLesley Hutchins and Peter Norvig. 2017. Deep Learning with Dynamic Computation Graphs. arxiv:https:\/\/arXiv.org\/abs\/1702.02181"},{"key":"e_1_3_3_2_39_2","unstructured":"Massinissa Merouani Khaled\u00a0Afif Boudaoud Iheb\u00a0Nassim Aouadj Nassim Tchoulak Islem\u00a0Kara Bernou Hamza Benyamina Fatima Benbouzid-Si Tayeb Karima Benatchba Hugh Leather and Riyadh Baghdadi. 2024. LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.11522 (2024)."},{"key":"e_1_3_3_2_40_2","unstructured":"Massinissa Merouani Mohamed-Hicham Leghettas Riyadh Baghdadi Taha Arbaoui and Karima Benatchba. 2020. A deep learning based cost model for automatic code optimization in tiramisu. Ph.\u00a0D. Dissertation. PhD thesis 10 2020."},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3578360.3580257"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","unstructured":"Ravi\u00a0Teja Mullapudi Andrew Adams Dillon Sharlet Jonathan Ragan-Kelley and Kayvon Fatahalian. 2016. Automatically Scheduling Halide Image Processing Pipelines. ACM Trans. Graph. 35 4 Article 83 (jul 2016) 11\u00a0pages. 10.1145\/2897824.2925952","DOI":"10.1145\/2897824.2925952"},{"key":"e_1_3_3_2_43_2","unstructured":"Aditya Paliwal Felix Gimeno Vinod Nair Yujia Li Miles Lubin Pushmeet Kohli and Oriol Vinyals. 2020. Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. arxiv:https:\/\/arXiv.org\/abs\/1905.02494"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.5753\/wscad.2019.8655"},{"key":"e_1_3_3_2_45_2","unstructured":"Louis-No\u00ebl Pouchet et\u00a0al. 2012. Polybench: The polyhedral benchmark suite. URL: http:\/\/www. cs. ucla. edu\/pouchet\/software\/polybench 437 (2012) 1\u20131."},{"key":"e_1_3_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/1926385.1926449"},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"crossref","unstructured":"F. Quiller\u00e9 and S. Rajopadhye. 2000. Optimizing Memory Usage in the Polyhedral Model. ACM Trans. on Programming Languages and Systems 22 5 (Sept. 2000) 773\u2013815.","DOI":"10.1145\/365151.365152"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"publisher","unstructured":"Jonathan Ragan-Kelley Connelly Barnes Andrew Adams Sylvain Paris Fr\u00e9do Durand and Saman Amarasinghe. 2013. Halide: A Language and Compiler for Optimizing Parallelism Locality and Recomputation in Image Processing Pipelines. SIGPLAN Not. 48 6 (jun 2013) 519\u2013530. 10.1145\/2499370.2462176","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_3_3_2_49_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1707.06347 (2017)."},{"key":"e_1_3_3_2_50_2","volume-title":"Reinforcement Learning, second edition: An Introduction","author":"Sutton R.S.","year":"2018","unstructured":"R.S. Sutton and A.G. Barto. 2018. Reinforcement Learning, second edition: An Introduction. MIT Press. https:\/\/books.google.dz\/books?id=sWV0DwAAQBAJ"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/378795.378852"},{"key":"e_1_3_3_2_52_2","unstructured":"Konrad Trifunovic Albert Cohen David Edelsohn Feng Li Tobias Grosser Harsha Jagasia Razya Ladelsky Sebastian Pop Jan Sjodin and Ramakrishna Upadrasta. 2010. GRAPHITE Two Years After: First Lessons Learned From Real-World Polyhedral Compilation."},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/1183401.1183448"},{"key":"e_1_3_3_2_54_2","unstructured":"Nicolas Vasilache Oleksandr Zinenko Theodoros Theodoridis Priya Goyal Zach DeVito William\u00a0S. Moses Sven Verdoolaege Andrew Adams and Albert Cohen. 2018. Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions. CoRR abs\/1802.04730 (2018)."},{"key":"e_1_3_3_2_55_2","unstructured":"Nicolas Vasilache Oleksandr Zinenko Theodoros Theodoridis Priya Goyal Zachary DeVito William\u00a0S Moses Sven Verdoolaege Andrew Adams and Albert Cohen. 2018. Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1802.04730 (2018)."},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"publisher","unstructured":"Sven Verdoolaege Juan Carlos\u00a0Juega Albert Cohen Jos\u00e9 Ignacio\u00a0G\u00f3mez Christian Tenllado and Francky Catthoor. 2013. Polyhedral parallel code generation for CUDA. ACM Trans. Archit. Code Optim. 9 4 Article 54 (jan 2013) 23\u00a0pages. 10.1145\/2400682.2400713","DOI":"10.1145\/2400682.2400713"},{"key":"e_1_3_3_2_57_2","doi-asserted-by":"crossref","unstructured":"Michael\u00a0E Wolf and Monica\u00a0S Lam. 1991. A loop transformation theory and an algorithm to maximize parallelism. IEEE transactions on parallel and distributed systems 2 4 (1991) 452\u2013471.","DOI":"10.1109\/71.97902"},{"key":"e_1_3_3_2_58_2","first-page":"863","volume-title":"14th USENIX symposium on operating systems design and implementation (OSDI 20)","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody\u00a0Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et\u00a0al. 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_3_2_59_2","unstructured":"Lianmin Zheng Chengfan Jia Minmin Sun Zhao Wu Cody\u00a0Hao Yu Ameer Haj-Ali Yida Wang Jun Yang Danyang Zhuo Koushik Sen Joseph\u00a0E. Gonzalez and Ion Stoica. 2023. Ansor: Generating High-Performance Tensor Programs for Deep Learning. arxiv:https:\/\/arXiv.org\/abs\/2006.06762"},{"key":"e_1_3_3_2_60_2","unstructured":"Zhen Zheng Pengzhan Zhao Guoping Long Feiwen Zhu Kai Zhu Wenyi Zhao Lansong Diao Jun Yang and Wei Lin. 2021. FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads. arxiv:https:\/\/arXiv.org\/abs\/2009.10924"}],"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\/3721145.3725766","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:00:56Z","timestamp":1755867656000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721145.3725766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":59,"alternative-id":["10.1145\/3721145.3725766","10.1145\/3721145"],"URL":"https:\/\/doi.org\/10.1145\/3721145.3725766","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"}}]}}