{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:53:46Z","timestamp":1775598826515,"version":"3.50.1"},"reference-count":61,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,2]]},"DOI":"10.1109\/cgo53902.2022.9741258","type":"proceedings-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T19:51:20Z","timestamp":1648583480000},"page":"92-105","source":"Crossref","is-referenced-by-count":64,"title":["CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research"],"prefix":"10.1109","author":[{"given":"Chris","family":"Cummins","sequence":"first","affiliation":[{"name":"Meta,USA"}]},{"given":"Bram","family":"Wasti","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Jiadong","family":"Guo","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Brandon","family":"Cui","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Jason","family":"Ansel","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Sahir","family":"Gomez","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Somya","family":"Jain","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Olivier","family":"Teytaud","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Benoit","family":"Steiner","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Yuandong","family":"Tian","sequence":"additional","affiliation":[{"name":"Meta,USA"}]},{"given":"Hugh","family":"Leather","sequence":"additional","affiliation":[{"name":"Meta,USA"}]}],"member":"263","reference":[{"key":"ref39","article-title":"PyTorch: An Imperative Style, High-performance Deep Learning Library","author":"paszke","year":"2019","journal-title":"arXiv 1912 01703"},{"key":"ref38","article-title":"Gated Graph Sequence Neural Networks","author":"li","year":"2015","journal-title":"arXiv 1511 05493"},{"key":"ref33","article-title":"Monte-Carlo Tree Search: A New Framework for Game AI","volume":"8","author":"chaslot","year":"2008","journal-title":"AIIDE"},{"key":"ref32","article-title":"Learning Search Space Partition for Black-Box Optimization using Monte Carlo Tree Search","author":"wang","year":"2020","journal-title":"NeurIPS"},{"key":"ref31","article-title":"Value Learning for Throughput Optimization of Deep Learning Workloads","author":"steiner","year":"2021","journal-title":"MLSys"},{"key":"ref30","article-title":"Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks","author":"mendis","year":"2019","journal-title":"ICML"},{"key":"ref37","article-title":"Reinforcement Learning with Unsupervised Auxiliary Tasks","author":"jaderberg","year":"2016","journal-title":"CoRR abs\/1611 05397"},{"key":"ref36","article-title":"A Geometric Perspective on Optimal Representations for Reinforcement Learning","author":"bellemare","year":"2019","journal-title":"CoRR"},{"key":"ref35","author":"solgi","year":"2020","journal-title":"GeneticAlgorithm"},{"key":"ref34","article-title":"Nevergrad - A Gradient-Free Optimization Platform","author":"rapin","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2017.24"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3460945.3464952"},{"key":"ref28","article-title":"Actor-Critic Algorithms","author":"konda","year":"2000","journal-title":"NeurIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/FDL50818.2020.9232934"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3197978"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WWC.2001.990739"},{"key":"ref22","article-title":"Convolutional Neural Networks Over Tree Structures for Programming Language Processing","author":"mou ge li","year":"2016","journal-title":"AAAI"},{"key":"ref21","author":"bailey","year":"1995","journal-title":"The NAS Parallel Benchmarks 2 0"},{"key":"ref24","article-title":"Ansor: Generating High-Performance Tensor Programs for Deep Learning","author":"zheng","year":"2020","journal-title":"OSDI"},{"key":"ref23","article-title":"TensorFlow: A System for Large-Scale Machine Learning","author":"abadi","year":"2016","journal-title":"OSDI"},{"key":"ref26","year":"2021"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/2491956.2462176"},{"key":"ref50","article-title":"Collective Tuning Initiative: Automating and Accelerating Development and Optimization of Computing Systems","author":"fursin","year":"2009","journal-title":"GCC Developer's summit"},{"key":"ref51","article-title":"TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers","author":"zheng","year":"2021","journal-title":"NeurIPS"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/PACT52795.2021.00009"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/LLVMHPCHiPar51896.2020.00006"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3207719.3207727"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/2928270"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2907950.2907959"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3372799.3394361"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/FDL50818.2020.9232946"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICAPAI49758.2021.9462050"},{"key":"ref10","article-title":"Differential Testing for Software","volume":"10","author":"mckeeman","year":"1998","journal-title":"Digital Technical Journal"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2004.1281665"},{"key":"ref40","article-title":"Asynchronous Methods for Deep Reinforcement Learning","author":"mnih","year":"2016","journal-title":"ICML"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1806596.1806647"},{"key":"ref13","article-title":"Neural Code Comprehension: A Learnable Representation of Code Semantics","author":"ben-nun","year":"2018","journal-title":"NeurIPS"},{"key":"ref14","article-title":"ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations","author":"cummins","year":"2021","journal-title":"ICML"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-69338-3_17"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CGO51591.2021.9370322"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/355841.355847"},{"key":"ref18","article-title":"CHStone: A Benchmark Program Suite for Practical C-based High-Level Synthesis","author":"hara","year":"2008","journal-title":"ISCAS"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2017.7863731"},{"key":"ref4","article-title":"Autophase: Juggling hls phase orderings in random forests with deep reinforcement learning","author":"haj-ali","year":"2020","journal-title":"MLSys"},{"key":"ref3","article-title":"MLGO: a Machine Learning Guided Compiler Optimizations Framework","author":"trofin","year":"2021","journal-title":"arXiv 2101 04808"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3212695"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3368826.3377928"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1993498.1993532"},{"key":"ref7","article-title":"OpenAI Gym","author":"brockman","year":"2016","journal-title":"arXiv 1606 01540 [cs]"},{"key":"ref49","article-title":"YaCoS: a Complete Infrastructure to the Design and Exploration of Code Optimization Sequences","author":"felip\u00e9 zanella","year":"2019","journal-title":"SBLP"},{"key":"ref9","article-title":"RLlib: Abstractions for Distributed Reinforcement Learning","author":"liang","year":"2018","journal-title":"ICML"},{"key":"ref46","article-title":"Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift","author":"ovadia","year":"2019","journal-title":"NeurIPS"},{"key":"ref45","article-title":"Improving Generalization in Reinforcement Learning with Mixture Regularization","author":"wang","year":"2020","journal-title":"NeurIPS"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00011"},{"key":"ref47","article-title":"Learning and Evaluating Contextual Embedding of Source Code","author":"kanade","year":"2020","journal-title":"ICML"},{"key":"ref42","article-title":"IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures","author":"espeholt","year":"2018","journal-title":"ICML"},{"key":"ref41","article-title":"Distributed Prioritized Experience Replay","author":"horgan","year":"2018","journal-title":"ICML"},{"key":"ref44","article-title":"Quantifying Generalization in Reinforcement Learning","author":"cobbe","year":"2019","journal-title":"ICML"},{"key":"ref43","article-title":"Proximal Policy Optimization Algorithms","author":"schulman","year":"2017","journal-title":"arXiv 1707 06347"}],"event":{"name":"2022 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO)","location":"Seoul, Korea, Republic of","start":{"date-parts":[[2022,4,2]]},"end":{"date-parts":[[2022,4,6]]}},"container-title":["2022 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9741235\/9741095\/09741258.pdf?arnumber=9741258","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T20:17:57Z","timestamp":1655324277000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9741258\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,2]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/cgo53902.2022.9741258","relation":{},"subject":[],"published":{"date-parts":[[2022,4,2]]}}}