{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T17:14:28Z","timestamp":1758474868084,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,17]]},"DOI":"10.1145\/3578360.3580272","type":"proceedings-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T20:20:06Z","timestamp":1676665206000},"page":"37-49","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficiently Learning Locality Optimizations by Decomposing Transformation Domains"],"prefix":"10.1145","author":[{"given":"Tharindu R.","family":"Patabandi","sequence":"first","affiliation":[{"name":"University of Utah, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary","family":"Hall","sequence":"additional","affiliation":[{"name":"University of Utah, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org \t\t\t\t  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322967"},{"key":"e_1_3_2_1_3_1","unstructured":"Amit Agarwal Eldar Akchurin Chris Basoglu Guoguo Chen Scott Cyphers Jasha Droppo Adam Eversole Brian Guenter Mark Hillebrand Ryan Hoens Xuedong Huang Zhiheng Huang Vladimir Ivanov Alexey Kamenev Philipp Kranen Oleksii Kuchaiev Wolfgang Manousek Avner May Bhaskar Mitra Olivier Nano Gaizka Navarro Alexey Orlov Marko Padmilac Hari Parthasarathi Baolin Peng Alexey Reznichenko Frank Seide Michael L. Seltzer Malcolm Slaney Andreas Stolcke Yongqiang Wang Huaming Wang Kaisheng Yao Dong Yu Yu Zhang and Geoffrey Zweig. 2014. An introduction to computational networks and the computational network toolkit. Redmond WA. \t\t\t\t  Amit Agarwal Eldar Akchurin Chris Basoglu Guoguo Chen Scott Cyphers Jasha Droppo Adam Eversole Brian Guenter Mark Hillebrand Ryan Hoens Xuedong Huang Zhiheng Huang Vladimir Ivanov Alexey Kamenev Philipp Kranen Oleksii Kuchaiev Wolfgang Manousek Avner May Bhaskar Mitra Olivier Nano Gaizka Navarro Alexey Orlov Marko Padmilac Hari Parthasarathi Baolin Peng Alexey Reznichenko Frank Seide Michael L. Seltzer Malcolm Slaney Andreas Stolcke Yongqiang Wang Huaming Wang Kaisheng Yao Dong Yu Yu Zhang and Geoffrey Zweig. 2014. An introduction to computational networks and the computational network toolkit. Redmond WA."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290353"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.). 3, 181\u2013193","author":"Baghdadi Riyadh","year":"2021","unstructured":"Riyadh Baghdadi , Massinissa Merouani , Mohamed-Hicham Leghettas , Kamel Abdous , Taha Arbaoui , Karima Benatchba , and Saman amarasinghe. 2021 . A Deep Learning Based Cost Model for Automatic Code Optimization . In Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.). 3, 181\u2013193 . https:\/\/proceedings.mlsys.org\/paper\/2021\/file\/3def184ad8f4755ff269862ea77393dd-Paper.pdf Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, and Saman amarasinghe. 2021. A Deep Learning Based Cost Model for Automatic Code Optimization. In Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.). 3, 181\u2013193. https:\/\/proceedings.mlsys.org\/paper\/2021\/file\/3def184ad8f4755ff269862ea77393dd-Paper.pdf"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTER.2013.6702683"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3233\/978-1-61499-381-0-646"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.25080\/majora-92bf1922-003"},{"key":"e_1_3_2_1_9_1","unstructured":"John Cavazos and Darko Stefanovic. 1997. Adaptive Prefetching using Neural Networks. Proposal to NEC. \t\t\t\t  John Cavazos and Darko Stefanovic. 1997. Adaptive Prefetching using Neural Networks. Proposal to NEC."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410463.3414635"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Meghan Cowan , Haichen Shen , Leyuan Wang , Yuwei Hu , Luis Ceze , Carlos Guestrin , and Arvind Krishnamurthy . 2018 . TVM: An automated end-to-end optimizing compiler for deep learning . In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918) . USENIX Association, USA. 579\u2013594. isbn:978 1931971478 Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (OSDI\u201918). USENIX Association, USA. 579\u2013594. isbn:9781931971478"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327144.3327258"},{"key":"#cr-split#-e_1_3_2_1_13_1.1","unstructured":"Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cuDNN: Efficient primitives for deep learning. https:\/\/doi.org\/10.48550\/ARXIV.1410.0759 arxiv:1410.0759. 10.48550\/ARXIV.1410.0759"},{"key":"#cr-split#-e_1_3_2_1_13_1.2","unstructured":"Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cuDNN: Efficient primitives for deep learning. https:\/\/doi.org\/10.48550\/ARXIV.1410.0759 arxiv:1410.0759."},{"key":"#cr-split#-e_1_3_2_1_14_1.1","unstructured":"Chris Cummins Zacharias V. Fisches Tal Ben-Nun Torsten Hoefler and Hugh Leather. 2020. ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. https:\/\/doi.org\/10.48550\/ARXIV.2003.10536 10.48550\/ARXIV.2003.10536"},{"key":"#cr-split#-e_1_3_2_1_14_1.2","unstructured":"Chris Cummins Zacharias V. Fisches Tal Ben-Nun Torsten Hoefler and Hugh Leather. 2020. ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. https:\/\/doi.org\/10.48550\/ARXIV.2003.10536"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2017.24"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the GCC Developers\u2019 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 , Christopher K. I. Williams , Michael O\u2019Boyle , Phil Barnard , Elton Ashton , Eric Courtois , and Francois Bodin . 2008 . MILEPOST GCC: Machine learning based research compiler . In Proceedings of the GCC Developers\u2019 Summit . Ottawa, Ontario, Canada. 13 pages. Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson, Edwin Bonilla, John Thomson, Hugh Leather, Christopher K. I. Williams, Michael O\u2019Boyle, Phil Barnard, Elton Ashton, Eric Courtois, and Francois Bodin. 2008. MILEPOST GCC: Machine learning based research compiler. In Proceedings of the GCC Developers\u2019 Summit. Ottawa, Ontario, Canada. 13 pages."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3211346.3211355"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368826.3377928"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-014-0163-1"},{"key":"e_1_3_2_1_20_1","unstructured":"Intel. 2020. oneAPI Deep Neural Network Library (oneDNN). https:\/\/github.com\/oneapi-src\/oneDNN Accessed: 2022-04-13 \t\t\t\t  Intel. 2020. oneAPI Deep Neural Network Library (oneDNN). https:\/\/github.com\/oneapi-src\/oneDNN Accessed: 2022-04-13"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"#cr-split#-e_1_3_2_1_22_1.1","unstructured":"Navdeep Katel Vivek Khandelwal and Uday Bondhugula. 2021. High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results. https:\/\/doi.org\/10.48550\/ARXIV.2108.13191 10.48550\/ARXIV.2108.13191"},{"key":"#cr-split#-e_1_3_2_1_22_1.2","unstructured":"Navdeep Katel Vivek Khandelwal and Uday Bondhugula. 2021. High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results. https:\/\/doi.org\/10.48550\/ARXIV.2108.13191"},{"key":"e_1_3_2_1_23_1","volume-title":"Allen","author":"Kennedy Ken","year":"2001","unstructured":"Ken Kennedy and John R . Allen . 2001 . Optimizing compilers for modern architectures: A dependence-based approach. Morgan Kaufmann Publishers Inc ., San Francisco, CA, USA. isbn:1558602860 Ken Kennedy and John R. Allen. 2001. Optimizing compilers for modern architectures: A dependence-based approach. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. isbn:1558602860"},{"key":"e_1_3_2_1_24_1","volume-title":"Proc. CPC\u201910 (Compilers for Parallel Computers). Citeseer, 35\u201344","author":"Kisuki Toru","year":"2000","unstructured":"Toru Kisuki , P Knijnenburg , M O\u2019Boyle , and H Wijshoff . 2000 . Iterative compilation in program optimization . In Proc. CPC\u201910 (Compilers for Parallel Computers). Citeseer, 35\u201344 . Toru Kisuki, P Knijnenburg, M O\u2019Boyle, and H Wijshoff. 2000. Iterative compilation in program optimization. In Proc. CPC\u201910 (Compilers for Parallel Computers). Citeseer, 35\u201344."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2384616.2384628"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2011.311"},{"key":"e_1_3_2_1_27_1","volume-title":"MLIR: A Compiler Infrastructure for the End of Moore\u2019s Law. arxiv:2002.11054.","author":"Lattner Chris","year":"2020","unstructured":"Chris Lattner , Mehdi Amini , Uday Bondhugula , Albert Cohen , Andy Davis , Jacques Pienaar , River Riddle , Tatiana Shpeisman , Nicolas Vasilache , and Oleksandr Zinenko . 2020 . MLIR: A Compiler Infrastructure for the End of Moore\u2019s Law. arxiv:2002.11054. Chris Lattner, Mehdi Amini, Uday Bondhugula, Albert Cohen, Andy Davis, Jacques Pienaar, River Riddle, Tatiana Shpeisman, Nicolas Vasilache, and Oleksandr Zinenko. 2020. MLIR: A Compiler Infrastructure for the End of Moore\u2019s Law. arxiv:2002.11054."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446759"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2018.06.005"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2012.214"},{"key":"e_1_3_2_1_31_1","unstructured":"Kewen Meng and Boyana Norris. 2020. Guiding Optimizations with Meliora: A Deep Walk down Memory Lane. arXiv preprint arXiv:2006.09473. \t\t\t\t  Kewen Meng and Boyana Norris. 2020. Guiding Optimizations with Meliora: A Deep Walk down Memory Lane. arXiv preprint arXiv:2006.09473."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/3008904.3009034"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925952"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17473-0_10"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2017.7863744"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2259016.2259042"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2038698.2038711"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates, Inc.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019 . PyTorch: An imperative style, high-performance deep learning library . In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates, Inc. , Red Hook, NY, USA. 8026\u20138037. https:\/\/dl.acm.org\/doi\/10.5555\/3454287.3455008 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates, Inc., Red Hook, NY, USA. 8026\u20138037. https:\/\/dl.acm.org\/doi\/10.5555\/3454287.3455008"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72789-5_9"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460945.3464955"},{"volume-title":"5th International Conference on Learning Representations (ICLR). Online. https:\/\/openreview.net\/pdf?id=SyVVJ85lg","author":"Sparks Evan R.","key":"e_1_3_2_1_41_1","unstructured":"Qi, Evan R. Sparks , and Ameet S. Talwalkar . 2017. Paleo: A performance model for deep neural networks . In 5th International Conference on Learning Representations (ICLR). Online. https:\/\/openreview.net\/pdf?id=SyVVJ85lg Qi, Evan R. Sparks, and Ameet S. Talwalkar. 2017. Paleo: A performance model for deep neural networks. In 5th International Conference on Learning Representations (ICLR). Online. https:\/\/openreview.net\/pdf?id=SyVVJ85lg"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3497776.3517774"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3205289.3205321"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2908080.2908105"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342019866247"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/113445.113449"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/645818.669220"},{"key":"e_1_3_2_1_49_1","volume-title":"International Workshop on Languages and Compilers for Parallel Computing. 17\u201331","author":"Yuki Tomofumi","year":"2012","unstructured":"Tomofumi Yuki , Gautam Gupta , DaeGon Kim , Tanveer Pathan , and Sanjay Rajopadhye . 2012 . Alphaz: A system for design space exploration in the polyhedral model . In International Workshop on Languages and Compilers for Parallel Computing. 17\u201331 . Tomofumi Yuki, Gautam Gupta, DaeGon Kim, Tanveer Pathan, and Sanjay Rajopadhye. 2012. Alphaz: A system for design space exploration in the polyhedral model. In International Workshop on Languages and Compilers for Parallel Computing. 17\u201331."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378508"},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML\u201913","volume":"470","author":"Zuluaga Marcela","year":"2013","unstructured":"Marcela Zuluaga , Guillaume Sergent , Andreas Krause , and Markus P\u00fcschel . 2013 . Active learning for multi-objective optimization . In Proceedings of the 30th International Conference on Machine Learning (ICML\u201913 , Vol. 28). JMLR.org, Online. 462\u2013 470 . issn:1533-7928 http:\/\/proceedings.mlr.press\/v28\/zuluaga13.pdf Marcela Zuluaga, Guillaume Sergent, Andreas Krause, and Markus P\u00fcschel. 2013. Active learning for multi-objective optimization. In Proceedings of the 30th International Conference on Machine Learning (ICML\u201913, Vol. 28). JMLR.org, Online. 462\u2013470. issn:1533-7928 http:\/\/proceedings.mlr.press\/v28\/zuluaga13.pdf"}],"event":{"name":"CC '23: 32nd ACM SIGPLAN International Conference on Compiler Construction","sponsor":["SIGPLAN ACM Special Interest Group on Programming Languages"],"location":"Montr\u00e9al QC Canada","acronym":"CC '23"},"container-title":["Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578360.3580272","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3578360.3580272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:52Z","timestamp":1750178812000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3578360.3580272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":54,"alternative-id":["10.1145\/3578360.3580272","10.1145\/3578360"],"URL":"https:\/\/doi.org\/10.1145\/3578360.3580272","relation":{},"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"2023-02-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}