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For the past 25 years, we have witnessed an extensive application of Machine Learning to the Compiler space; the selection and the phase-ordering problem.
An analysis of inline substitution for a structured programming language
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A system-level framework for evaluating area/performance/power trade-offs of VLIW-based embedded systems. In Design Automation Conference, 2005. Proceedings of the ASP-DAC 2005. Asia and South Pacific , Vol. 2. 940–943 Vol. 2
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Using machine learning to focus iterative optimization. In Proceedings of the International Symposium on Code Generation and Optimization . IEEE Computer Society, 295–305
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MILEPOST GCC: machine learning based research compiler
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Collective optimization. In International Conference on High-Performance Embedded Architectures and Compilers . Springer, 34–49
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Compiler research: the next 50 years
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A scalable auto-tuning framework for compiler optimization. In Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on . 1–12
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Multicube: Multi-objective design space exploration of multi-core architectures. In VLSI 2010 Annual Symposium . Springer, 47–63
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Milepost gcc: Machine learning enabled self-tuning compiler
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Deconstructing iterative optimization
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Mitigating the compiler optimization phase-ordering problem using machine learning
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Using graph-based program characterization for predictive modeling
E Park, J Cavazos, and MA Alvarez. 2012 · 2012
Cited alongside, same era.
A framework for Compiler Level statistical analysis over customized VLIW architecture. In VLSI-SoC . 124–129
Amir Hossein Ashouri, Vittorio Zaccaria, Sotirios Xydis, Gianluca Palermo, and Cristina Silvano. 2013 · 2013
Cited alongside, same era.
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Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013 · 2013
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Predictive modeling in a polyhedral optimization space
E Park, J Cavazos, and LN Pouchet. 2013 · 2013
Cited alongside, same era.
Opentuner: An extensible framework for program autotuning. In Proceedings of the 23rd international conference on Parallel architectures and compilation . 303–316
Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O’Reilly, and Saman Amarasinghe. 2014 · 2014
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 2018 · 2018
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Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018 · 2018
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code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019 · 2019
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Code Region Based Auto-Tuning Enabled Compilers. In Workshop on the Intersection of High Performance Computing and Machine Learning (HPCaML) . ACM
Michael Kalyan, Xiang Wang, Ahmed Eltantawy, and Yaoqing Gao. 2019 · 2019
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An optimization-driven incremental inline substitution algorithm for just-in-time compilers. In 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) . IEEE, 164–179
Aleksandar Prokopec, Gilles Duboscq, David Leopoldseder, and Thomas Wïrthinger. 2019 · 2019
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COBAYN: Compiler Autotuning Framework Using Bayesian Networks
Amir Hossein Ashouri, Giovanni Mariani, Gianluca Palermo, Eunjung Park, John Cavazos, and Cristina Silvano. 2016 · 2016
Cited alongside, same era.
Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining . 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell. 2016 · 2016
Cited alongside, same era.
MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning
Amir H. Ashouri, Andrea Bignoli, Gianluca Palermo, Cristina Silvano, Sameer Kulkarni, and John Cavazos. 2017 · 2017
Cited alongside, same era.
End-to-End Deep Learning of Optimization Heuristics. In 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT) . 219–232
C. Cummins, P. Petoumenos, Z. Wang, and H. Leather. 2017 · 2017
Cited alongside, same era.
Impact of Compiler Phase Ordering When Targeting GPUs. In Euro-Par 2017: Parallel Processing Workshops , Dora B. Heras and Luc Bougé (Eds.). Springer International Publishing, Cham, 427–438
Ricardo Nobre, Luís Reis, and João M. P. Cardoso. 2018 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Cited alongside, same era.
Deep Learning-based Approximate Graph-Coloring Algorithm for Register Allocation. In 2020 IEEE/ACM 6th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) and Workshop on Hierarchical Parallelism for Exascale Computing (HiPar) . IEEE, 23–32
Dibyendu Das, Shahid Asghar Ahmad, and Venkataramanan Kumar. 2020 · 2020
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Neurovectorizer: End-to-end vectorization with deep reinforcement learning. In Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization . 242–255
Ameer Haj-Ali, Nesreen K Ahmed, Ted Willke, Yakun Sophia Shao, Krste Asanovic, and Ion Stoica. 2020 · 2020
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Semi-supervised reward learning for offline reinforcement learning
Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott Reed, Serkan Cabi, and Nando de Freitas. 2020 · 2020
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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, et al · 2021
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A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers. In 2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT) . IEEE, 1–16
Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, et al · 2021
Later among the works it cites.
Profile Guided Optimization without Profiles: A Machine Learning Approach
Nadav Rotem and Chris Cummins. 2021 · 2021
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Exploring the space of optimization sequences for code-size reduction: insights and tools. In Proceedings of the 30th ACM SIGPLAN International Conference on Compiler Construction . 47–58
Anderson Faustino da Silva, Bernardo NB de Lima, and Fernando Magno Quintão Pereira. 2021 · 2021
Later among the works it cites.
Mlgo: a machine learning guided compiler optimizations framework
Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, and David Li. 2021 · 2021
Later among the works it cites.
Learning to Combine Instructions in LLVM Compiler
Sandya Mannarswamy and Dibyendu Das. 2022 · 2022
Closest in time.
SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations. In 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) . IEEE, 118–130
Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, and Scott Mahlke. 2022 · 2022
Closest in time.
Understanding and exploiting optimal function inlining. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems . 977–989
Theodoros Theodoridis, Tobias Grosser, and Zhendong Su. 2022 · 2022
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Automating reinforcement learning architecture design for code optimization. In Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction . 129–143
Huanting Wang, Zhanyong Tang, Cheng Zhang, Jiaqi Zhao, Chris Cummins, Hugh Leather, and Zheng Wang. 2022 · 2022
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