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Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware.
Metamorphic Testing: A New Approach for Generating Next Test Cases
T. Y. Chen, S. C. Cheung, and S. M. Yiu. 2020a · 2002
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Finding and Understanding Bugs in C Compilers. In Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation (San Jose, California, USA) (PLDI ’11) . Association for Computing Machinery, New York, NY, USA, 283–294
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Compiler Validation via Equivalence modulo Inputs. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (Edinburgh, United Kingdom) (PLDI ’14) . Association for Computing Machinery, New York, NY, USA, 216–226
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Martín 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 Mané, 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 Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015 · 2015
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DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. In 2015 IEEE International Conference on Computer Vision (ICCV) . 2722–2730
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Finding Deep Compiler Bugs via Guided Stochastic Program Mutation. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (Pittsburgh, PA, USA) (OOPSLA 2015) . Association for Computing Machinery, New York, NY, USA, 386–399
Vu Le, Chengnian Sun, and Zhendong Su. 2015 · 2015
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Many-Core Compiler Fuzzing
Christopher Lidbury, Andrei Lascu, Nathan Chong, and Alastair F. Donaldson. 2015 · 2015
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Policy compression for aircraft collision avoidance systems. In 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC) . 1–10
Kyle D. Julian, Jessica Lopez, Jeffrey S. Brush, Michael P. Owen, and Mykel J. Kochenderfer. 2016 · 2016
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Finding Compiler Bugs via Live Code Mutation. In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (Amsterdam, Netherlands) (OOPSLA 2016) . Association for Computing Machinery, New York, NY, USA, 849–863
Chengnian Sun, Vu Le, and Zhendong Su. 2016a · 2016
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Toward Understanding Compiler Bugs in GCC and LLVM. In Proceedings of the 25th International Symposium on Software Testing and Analysis (Saarbrücken, Germany) (ISSTA 2016) . Association for Computing Machinery, New York, NY, USA, 294–305
Chengnian Sun, Vu Le, Qirun Zhang, and Zhendong Su. 2016b · 2016
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Automated Testing of Graphics Shader Compilers
Alastair F. Donaldson, Hugues Evrard, Andrei Lascu, and Paul Thomson. 2017 · 2017
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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation (Carlsbad, CA, USA) (OSDI’18) . USENIX Association, USA, 579–594
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 · 2018
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Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, and Tristan J. Webb. 2018 · 2018
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Evaluating Fuzz Testing. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (Toronto, Canada) (CCS ’18) . Association for Computing Machinery, New York, NY, USA, 2123–2138
George Klees, Andrew Ruef, Benji Cooper, Shiyi Wei, and Michael Hicks. 2018 · 2018
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Relay: A New IR for Machine Learning Frameworks. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (Philadelphia, PA, USA) (MAPL 2018) . Association for Computing Machinery, New York, NY, USA, 58–68
Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, and Zachary Tatlock. 2018 · 2018
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation
Jiawei Liu, Yuxiang Wei, Sen Yang, Yinlin Deng, and Lingming Zhang. 2022 · 2022
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An Empirical Study on Bugs in Python Interpreters
Ziyuan Wang, Dexin Bu, Aiyue Sun, Shanyi Gou, Yong Wang, and Lin Chen. 2022 · 2022
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Metamorphic Testing of Deep Learning Compilers
Dongwei Xiao, Zhibo LIU, Yuanyuan Yuan, Qi Pang, and Shuai Wang. 2022 · 2022
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DocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, South Korea) (ISSTA 2022) . Association for Computing Machinery, New York, NY, USA, 176–188
Danning Xie, Yitong Li, Mijung Kim, Hung Viet Pham, Lin Tan, Xiangyu Zhang, and Michael W. Godfrey. 2022 · 2022
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XLA: Optimizing Compiler for Machine Learning
2016 · 2023
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Glow: Graph Lowering Compiler Techniques for Neural Networks
Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Garret Catron, Summer Deng, Roman Dzhabarov, Nick Gibson, James Hegeman, Meghan Lele, Roman Levenstein, Jack Montgomery, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Misha Smelyanskiy, and Man Wang. 2019 · 2019
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A Survey of Compiler Testing
Junjie Chen, Jibesh Patra, Michael Pradel, Yingfei Xiong, Hongyu Zhang, Dan Hao, and Lu Zhang. 2020b · 2020
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The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, and Depei Qian. 2021 · 2020
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Deep Learning Library Testing via Effective Model Generation. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 788–799
Zan Wang, Ming Yan, Junjie Chen, Shuang Liu, and Dongdi Zhang. 2020 · 2020
Cited alongside, same era.
An empirical study of optimization bugs in GCC and LLVM
Zhide Zhou, Zhilei Ren, Guojun Gao, and He Jiang. 2021 · 2020
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Well-Typed Programs Can Go Wrong: A Study of Typing-Related Bugs in JVM Compilers
Stefanos Chaliasos, Thodoris Sotiropoulos, Georgios-Petros Drosos, Charalambos Mitropoulos, Dimitris Mitropoulos, and Diomidis Spinellis. 2021 · 2021
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Graph-Based Fuzz Testing for Deep Learning Inference Engines. In Proceedings of the 43rd International Conference on Software Engineering (Madrid, Spain) (ICSE ’21) . IEEE Press, 288–299
Weisi Luo, Dong Chai, Xiaoyue Run, Jiang Wang, Chunrong Fang, and Zhenyu Chen. 2021 · 2021
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A Comprehensive Study of Deep Learning Compiler Bugs. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 968–980
Qingchao Shen, Haoyang Ma, Junjie Chen, Yongqiang Tian, Shing-Chi Cheung, and Xiang Chen. 2021 · 2021
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HirGen’s Optimizations
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Operator Schemas of ONNX
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NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (Vancouver, BC, Canada) (ASPLOS 2023) . Association for Computing Machinery, New York, NY, USA, 530–543
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