# awesome-machine-learning-in-compilers **Repository Path**: chisuhua/awesome-machine-learning-in-compilers ## Basic Information - **Project Name**: awesome-machine-learning-in-compilers - **Description**: No description available - **Primary Language**: Unknown - **License**: CC0-1.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2020-06-28 - **Last Updated**: 2024-06-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Awesome machine learning for compilers and program optimisation [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) A curated list of awesome research papers, datasets, and tools for applying machine learning techniques to compilers and program optimisation. ## Contents - [Papers](#papers) - [Survey](#survey) - [Iterative Compilation and Compiler Option Tuning](#iterative-compilation-and-compiler-option-tuning) - [Instruction-level Optimisation](#instruction-level-optimisation) - [Parallelism Mapping and Task Scheduling](#parallelism-mapping-and-task-scheduling) - [Languages and Compilation](#languages-and-compilation) - [Auto-tuning and Design Space Exploration](#auto-tuning-and-design-space-exploration) - [Code Size Reduction](#code-size-reduction) - [Cost and Performance Models](#cost-and-performance-models) - [Domain-specific Optimisation](#domain-specific-optimisation) - [Learning Program Representation](#learning-program-representation) - [Enabling ML in Compilers](#enabling-ml-in-compilers) - [Talks and Tutorials](#talks-and-tutorials) - [Software](#software) - [Benchmarks and Datasets](#benchmarks-and-datasets) - [Conferences](#conferences) - [How to Contribute](#how-to-contribute) ## Papers #### Survey -

[Machine Learning in Compiler Optimisation](https://zwang4.github.io/publications/pieee18.pdf) - Zheng Wang and Michael O'Boyle, Proceedings of the IEEE, 2018 -

[A survey on compiler autotuning using machine learning](https://dl.acm.org/doi/abs/10.1145/3197978) - Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, and Cristina Silvano, ACM Computing Surveys (CSUR), 2018 -

[A survey of machine learning for big code and naturalness](https://daniel.perez.sh/talks/2018/ml-bigcode-survey.pdf) - Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton, ACM Computing Surveys (CSUR), 2018 #### Iterative Compilation and Compiler Option Tuning -

[Improved basic block reordering](https://arxiv.org/pdf/1809.04676.pdf) - Andy Newell and Sergey Pupyrev. IEEE Transactions on Computers, 2020. -

[A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations](https://dl.acm.org/doi/abs/10.1145/3372799.3394361) - Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, and Stefano Doni, LCTES 2020. -

[Autophase: Compiler phase-ordering for hls with deep reinforcement learning](https://ieeexplore.ieee.org/abstract/document/8735549). Qijing Huang, Ameer Haj-Ali, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek. FCCM 2019. -

[Micomp: Mitigating the compiler phase-ordering problem using optimization sub-sequences and machine learning](https://core.ac.uk/download/pdf/93751619.pdf) - Amir H. Ashouri, Andrea Bignoli, Gianluca Palermo, Cristina Silvano, Sameer Kulkarni, and John Cavazos. ACM Transactions on Architecture and Code Optimization (TACO) 2017. -

[Learning to superoptimize programs](https://www.infosun.fim.uni-passau.de/publications/docs/GGS+17.pdf) - Stefan Ganser, Armin Grösslinger, Norbert Siegmund, Sven Apel, and Christian Lengauer. ACM Transactions on Architecture and Code Optimization (TACO), 2017. -

[Learning to superoptimize programs](https://arxiv.org/abs/1611.01787v3) - Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S. Torr, Pushmeet Kohlim. ICLR 2017 -

[Continuous learning of compiler heuristics](https://dl.acm.org/doi/abs/10.1145/2400682.2400705) - Michele Tartara and Stefano Crespi Reghizzi. ACM Transactions on Architecture and Code Optimization (TACO), 2013. -

[Mitigating the compiler optimization phase-ordering problem using machine learning](https://www.eecis.udel.edu/~cavazos/oopsla-2012.pdf) - Sameer Kulkarni and John Cavazos. OOPSLA 2012. -

[An evaluation of different modeling techniques for iterative compilation](https://www.eecis.udel.edu/~cavazos/cases-2011.pdf) - Eunjung Park, Sameer Kulkarni, and John Cavazos. CASES 2011. -

[Evaluating iterative optimization across 1000 datasets](https://users.elis.ugent.be/~leeckhou/papers/pldi10.pdf) - Yang Chen, Yuanjie Huang, Lieven Eeckhout, Grigori Fursin, Liang Peng, Olivier Temam, and Chengyong Wu. PLDI 2010 -

[Iterative optimization in the polyhedral model: Part II, multidimensional time](https://www.eecis.udel.edu/~cavazos/pldi-2008.pdf) - Louis-Noël Pouchet, Cédric Bastoul, Albert Cohen, and John Cavazos. PLDI 2008. -

[Cole: compiler optimization level exploration](https://users.elis.ugent.be/~leeckhou/papers/cgo08.pdf) - Kenneth Hoste and Lieven Eeckhout. CGO 2008. -

[MILEPOST GCC: machine learning based research compiler](http://www.fursin.net/papers/fmtp2008.pdf) - Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson et al., 2008 -

[Evaluating heuristic optimization phase order search algorithms](http://www.cs.fsu.edu/~whalley/papers/cgo07.pdf) - J. W. Davidson, Gary S. Tyson, D. B. Whalley, and P. A. Kulkarni. CGO 2007. -

[Rapidly selecting good compiler optimizations using performance counters](http://ebonilla.github.io/papers/cavazos-et-al-cgo-2007.pdf) - John Cavazos, Grigori Fursin, Felix Agakov, Edwin Bonilla, Michael FP O'Boyle, and Olivier Temam. CGO 2007. -

[Using machine learning to focus iterative optimization](http://homepages.inf.ed.ac.uk/bfranke/Publications/cgo-2006.pdf) - Felix Agakov, Edwin Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael FP O'Boyle, John Thomson, Marc Toussaint, and Christopher KI Williams. CGO 2006. -

[Method-specific dynamic compilation using logistic regression](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.132.4370&rep=rep1&type=pdf) - John Cavazos and Michael FP O'boyle. OOPSLA 2005. -

[Predicting unroll factors using supervised classification](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.2788&rep=rep1&type=pdf) - Mark Stephenson and Saman Amarasinghe. CGO 2005. -

[Fast searches for effective optimization phase sequences](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.2788&rep=rep1&type=pdf) - Prasad Kulkarni, Stephen Hines, Jason Hiser, David Whalley, Jack Davidson, and Douglas Jones. PLDI 2004. #### Instruction-level Optimisation -

[NeuroVectorizer: end-to-end vectorization with deep reinforcement learning](https://people.eecs.berkeley.edu/~krste/papers/neurovectorizer-cgo2020.pdf) - Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Yakun Sophia Shao, Krste Asanovic, and Ion Stoica. CGO 2020. -

[Compiler Auto-Vectorization with Imitation Learning](https://papers.nips.cc/paper/9604-compiler-auto-vectorization-with-imitation-learning.pdf) - Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin. NeurIPS 2019. -

[Multi-objective Exploration for Practical Optimization Decisions in Binary Translation](https://dl.acm.org/doi/abs/10.1145/3358185) - Sunghyun Park, Youfeng Wu, Janghaeng Lee, Amir Aupov, and Scott Mahlke. ACM Transactions on Embedded Computing Systems (TECS), 2019. -

[Automatic construction of inlining heuristics using machine learning.](https://dl.acm.org/doi/abs/10.1109/CGO.2013.6495004) - Sameer Kulkarni, John Cavazos, Christian Wimmer, and Douglas Simon. CGO 2013. -

[Automatic tuning of inlining heuristics](http://sc05.supercomputing.org/schedule/pdf/pap274.pdf) - John Cavazos and Michael O'Boyle. SC 2005. -

[Inducing heuristics to decide whether to schedule](https://www.eecis.udel.edu/~cavazos/pldi-2004.pdf) - John Cavazos and J. Eliot B. Moss. PLDI 2003. -

[Meta optimization: Improving compiler heuristics with machine learning](http://groups.csail.mit.edu/cag/metaopt/papers/metaopt-pldi03.pdf) - Mark Stephenson, Saman Amarasinghe, Martin Martin, and Una-May O'Reilly. PLDI 2003. -

[Learning to schedule straight-line code](http://papers.nips.cc/paper/1349-learning-to-schedule-straight-line-code.pdf) - J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Doina Precup, Darko Stefanovic, Carla E. Brodley, and David Scheeff. NeurIPS 1998. #### Auto-tuning and Design Space Exploration -

[TVM: An automated end-to-end optimizing compiler for deep learning](https://www.usenix.org/system/files/osdi18-chen.pdf) - Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan et al., OSDI 2018 -

[BOAT: Building auto-tuners with structured Bayesian optimization](https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2018_2019/papers/dalibard_WWW_2017.pdf) - Valentin Dalibard, Michael Schaarschmidt, and Eiko Yoneki, WWW 2017. -

[Cobayn: Compiler autotuning framework using bayesian networks](https://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf) - Amir H. Ashouri, Giovanni Mariani, Gianluca Palermo, Eunjung Park, John Cavazos, and Cristina Silvano, ACM Transactions on Architecture and Code Optimization (TACO), 2016. -

[Autotuning algorithmic choice for input sensitivity](http://groups.csail.mit.edu/commit/papers/2015/yding-pldi15-pbinput.pdf) - Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O'Reilly, and Saman Amarasinghe. PLDI 2015 -

[Fast: A fast stencil autotuning framework based on an optimal-solution space model](http://www.elbagarza.com/pdfs/jia_2015_gpu.pdf) - Yulong Luo, Guangming Tan, Zeyao Mo, and Ninghui Sun. ACM Transactions on Architecture and Code Optimization (TACO), 2015. -

[GPU performance and power tuning using regression trees](https://dl.acm.org/doi/abs/10.1145/2751205.2751214) - Wenhao Jia, Elba Garza, Kelly A. Shaw, and Margaret Martonosi. SC 2015. -

[Opentuner: An extensible framework for program autotuning](https://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf) - Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, and Saman Amarasinghe. PACT 2014 -

[Taming parallel I/O complexity with auto-tuning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.714.1995&rep=rep1&type=pdf) - Babak Behzad, Huong Vu Thanh Luu, Joseph Huchette, Surendra Byna, Ruth Aydt, Quincey Koziol, and Marc Snir. SC 2013. -

[A multi-objective auto-tuning framework for parallel codes](https://www.researchgate.net/profile/Philipp_Gschwandtner/publication/235436717_A_multi-objective_auto-tuning_framework_for_parallel_codes/links/55b5d86b08aed621de02f1d9/A-multi-objective-auto-tuning-framework-for-parallel-codes.pdf) - Herbert Jordan, Peter Thoman, Juan J. Durillo, Simone Pellegrini, Philipp Gschwandtner, Thomas Fahringer, and Hans Moritsch. SC 2012. -

[Combining models and guided empirical search to optimize for multiple levels of the memory hierarchy](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.532.9511&rep=rep1&type=pdf) - Chun Chen, Jacqueline Chame, and Mary Hall. CGO 2005 #### Parallelism Mapping and Task Scheduling -

[Poise: Balancing thread-level parallelism and memory system performance in GPUs using machine learning](https://homepages.inf.ed.ac.uk/vnagaraj/papers/hpca19.pdf) - Saumay Dublish, Vijay Nagarajan, and Nigel Tophama. HPCA 2019. -

[Data and thread placement in NUMA architectures: A statistical learning approach](https://www.mcs.anl.gov/research/projects/argo/publications/2019-icpp-denoyelle.pdf) - Nicolas Denoyelle, Brice Goglin, Emmanuel Jeannot, and Thomas Ropars. ICPP 2019. -

[Code Mapping in Heterogeneous Platforms Using Deep Learning and LLVM-IR](https://iris.polito.it/retrieve/handle/11583/2726074/327896/document_post_print.pdf) - Francesco Barchi, Gianvito Urgese, Enrico Macii, and Andrea Acquaviva. DAC 2019. -

[Adaptive optimization for OpenCL programs on embedded heterogeneous systems](https://core.ac.uk/download/pdf/83920402.pdf) - Ben Taylor, Vicent Sanz Marco, and Zheng Wang. LCTES 2017. -

[Improving spark application throughput via memory aware task co-location: A mixture of experts approach](https://zwang4.github.io/publications/middleware17.pdf) - Vicent Sanz Marco, Ben Taylor, Barry Porter, and Zheng Wang. Middleware 2017. -

[Quasar: resource-efficient and QoS-aware cluster management](http://csl.stanford.edu/~christos/publications/2014.quasar.asplos.pdf) - Christina Delimitrou, and Christos Kozyrakis. ASPLOS 2014. -

[Automatic and portable mapping of data parallel programs to opencl for gpu-based heterogeneous systems](https://zwang4.github.io/publications/zheng_taco_2015.pdf) - Zheng Wang, Dominik Grewe, and Michael O'boyle. ACM Transactions on Architecture and Code Optimization (TACO), 2014. -

[Automatic and portable mapping of data parallel programs to opencl for gpu-based heterogeneous systems](https://zwang4.github.io/publications/taco14.pdf) - Zheng Wang, Georgios Tournavitis, Björn Franke, and Michael FP O'boyle. ACM Transactions on Architecture and Code Optimization (TACO), 2014. -

[Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms](http://www.lancaster.ac.uk/staff/wangz3/publications/hipc14.pdf) - Yuan Wen, Zheng Wang, and Michael FP O'Boyle. HiPC 2015. -

[Smart, adaptive mapping of parallelism in the presence of external workload](https://dl.acm.org/doi/abs/10.1109/CGO.2013.6495010) - Murali Krishna Emani, Zheng Wang, and Michael O'Boyle. CGO 2013. -

[Partitioning streaming parallelism for multi-cores: a machine learning based approach](https://zwang4.github.io/publications/pact10.pdf) - Zheng Wang and Michael O'Boyle. PACT 2010. -

[Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping](http://www.sphong.net/MICRO_2009.pdf) - Chi-Keung Luk, Sunpyo Hong, and Hyesoon Kim. MICRO 2009. -

[Mapping parallelism to multi-cores: a machine learning based approach](http://llvm.org/pubs/2009-02-PPoPP-MappingParallelism.pdf) - Zheng Wang and Michael O'Boyle. PPoPP 2009. #### Domain-specific Optimisation -

[Bridging the gap between deep learning and sparse matrix format selection](https://people.engr.ncsu.edu/xshen5/Publications/ppopp18.pdf) - Yue Zhao, Jiajia Li, Chunhua Liao and Xipeng Shen. PPoPP 2018. -

[Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions](http://eprints.whiterose.ac.uk/155720/1/paper.pdf) - Jie Ren, Y. Lu, Petteri Nurmi, Xiaoming Wang, Miao Ma, Ling Gao, Zhanyong Tang, Jie Zheng, and Zheng Wang. INFOCOM 2020. -

[Optimizing sorting with genetic algorithms](http://polaris.cs.uiuc.edu/~garzaran/doc/cgo05.pdf) - Xiaoming Li, Maria Jesus Garzaran, and David Padua. CGO 2005. #### Languages and Compilation -

[Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines](https://core.ac.uk/download/pdf/20024748.pdf) - Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe, PLDI 2013. -

[PetaBricks: a language and compiler for algorithmic choice](http://people.csail.mit.edu/cychan/papers/2009pldi-petabricks.pdf) - Jason Ansel, Cy Chan, Yee Lok Wong, Marek Olszewski, Qin Zhao, Alan Edelman, and Saman Amarasinghe. PLDI 2009. #### Code Size Reduction -

[Reinforcement Learning Guided Software Debloating](http://www.csl.sri.com/users/gehani/papers/MLSys-2019.DeepOCCAM.pdf) - Nham Le Van, Ashish Gehani, Arie Gurfinkel, Susmit Jha, and Jorge A. Navas. MLSys 2019. -

[Optimizing for reduced code space using genetic algorithms](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1586&rep=rep1&type=pdf) - Keith D. Cooper, Philip J. Schielke, and Devika Subramanian. LCTES 1999. #### Cost and Performance Models -

[Learning to Optimize Halide with Tree Search and Random Programs](https://dl.acm.org/doi/pdf/10.1145/3306346.3322967) - Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michael Gharbi, Benoit Steiner, Steven Johson, Kayvon Fatahalian, Fredo Durand, Jonathan Ragan-Kelley. ACM Trans Graph, 2019. -

[Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks](http://proceedings.mlr.press/v97/mendis19a/mendis19a.pdf) - Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin. ICML 2019. -

[Absinthe: Learning an Analytical Performance Model to Fuse and Tile Stencil Codes in One Shot](http://unixer.de/publications/img/gysi-absinthe.pdf) - Tobias Gysi, Tobias Grosser, and Torsten Hoefler. PACT 2019. -

[Predicting new workload or CPU performance by analyzing public datasets](https://yuemmawang.github.io/publications/wang-taco2019.pdf) - Yu Wang, Victor Lee, Gu-Yeon Wei, and David Brooks. ACM Transactions on Architecture and Code Optimization (TACO), 2019. -

[Automatic creation of tile size selection models](http://people.rennes.inria.fr/Tomofumi.Yuki/papers/yuki-cgo2010.pdf) - Tomofumi Yuki, Lakshminarayanan Renganarayanan, Sanjay Rajopadhye, Charles Anderson, Alexandre E. Eichenberger, and Kevin O'Brien. CGO 2010. -

[Microarchitecture sensitive empirical models for compiler optimizations](https://www.csa.iisc.ac.in/~srikant/papers-theses/kapil-CGO-2007.pdf) - Kapil Vaswani, Matthew J. Thazhuthaveetil, Y. N. Srikant, and P. J. Joseph. CGO 2007. -

[Accurate static estimators for program optimization](https://dl.acm.org/doi/abs/10.1145/178243.178251) - Tim A. Wagner, Vance Maverick, Susan L. Graham, and Michael A. Harrison. PLDI 1994. #### Learning Program Representation -

[Compiler-based graph representations for deep learning models of code](https://cfaed.tu-dresden.de/files/Images/people/chair-cc/publications/2002_Brauckmann_CC.pdf) - Alexander Brauckmann, Andrés Goens, Sebastian Ertel, and Jeronimo Castrillon. CC 2020. -

[Generative Code Modeling with Graphs](https://arxiv.org/pdf/1805.08490.pdf) - Marc Brockschmidt, Miltos Allamanis, Alexander L. Gaunt, and Oleksandr Polozov. ICLR 2019. -

[code2seq: Generating sequences from structured representations of code](https://arxiv.org/pdf/1808.01400) - Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. ICLR 2019. -

[code2vec: Learning distributed representations of code](http://www.cs.technion.ac.il/~mbs/publications/code2vec-popl19.pdf) - Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. POPL 2019. -

[Learning to Represent Programs with Graphs](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/11/programGraphs.pdf) - Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. ICLR 2018. -

[Neural Code Comprehension: A Learnable Representation of Code Semantics](http://homepages.inf.ed.ac.uk/hleather/publications/2017-deepopt-pact.pdf) - Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. NeurIPS 2018. -

[End-to-end deep learning of optimization heuristics](http://homepages.inf.ed.ac.uk/hleather/publications/2017-deepopt-pact.pdf) - Chris Cummins, Pavlos Petoumenos, Zheng Wang, and Hugh Leather ([slides](https://speakerdeck.com/chriscummins/end-to-end-deep-learning-of-optimization-heuristics-pact-17)). PACT 2017. -

[Semantic-aware program sampling](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/11/nips_2017.pdf) - Pratiksha Thaker, Daniel Tarlow, and Marc Brockschmidt. NeurIPS 2017. -

[DeepCoder: Learning to write programs](https://www.microsoft.com/en-us/research/uploads/prod/2017/03/main.pdf) - Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. ICLR 2017. -

[Convolutional neural networks over tree structures for programming language processing](http://sei.pku.edu.cn/~zhanglu/Download/AAAI16.pdf) - Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. AAAI 2016. -

[A Convolutional Attention Network for Extreme Summarization of Source Code](http://proceedings.mlr.press/v48/allamanis16.pdf) - Miltos Allamanis, Hao Peng, and Charles Sutton. ICML 2016. -

[Structured Generative Models of Natural Source Code](http://proceedings.mlr.press/v32/maddison14.pdf) - Chris Maddison and Daniel Tarlow. ICML 2014. -

[Using graph-based program characterization for predictive modeling](https://www.eecis.udel.edu/~cavazos/cgo-2012.pdf) - Eunjung Park, John Cavazos, and Marco A. Alvarez. CGO 2011. -

[Automatic feature generation for machine learning based optimizing compilation](http://homepages.inf.ed.ac.uk/hleather/publications/2009_autofeatures_cgo.pdf) - Hugh Leather, Edwin Bonilla, and Michael O'Boyle. CGO 2009. #### Enabling ML in Compilers -

[Synthesizing Benchmarks for Predictive Modeling](https://www.pure.ed.ac.uk/ws/files/29479104/2017_cgo_1.pdf) - Chris Cummins, Pavlos Petoumenos, Zheng Wang, and Hugh Leather ([slides](https://speakerdeck.com/chriscummins/synthesizing-benchmarks-for-predictive-modelling-cgo-17)). CGO 2017. -

[Minimizing the cost of iterative compilation with active learning](http://homepages.inf.ed.ac.uk/hleather/publications/2017-minimitercomp-cgo.pdf) - William Ogilvie, Pavlos Petoumenos, Zheng Wang, and Hugh Leather. CGO 2017. ## Talks and Tutorials - Saman Amarasinghe, [Compiler 2.0: Using Machine Learning to Modernize Compiler Technology](https://www.youtube.com/watch?v=a1w_NKDVdkI). LCTES 2020. ## Software - [programl](https://github.com/ChrisCummins/ProGraML) - LLVM and XLA IR program representation for machine learning ([paper](https://arxiv.org/pdf/2003.10536.pdf)). - [NeuroVectorizer](https://github.com/intel/neuro-vectorizer) - Using deep reinforcement learning (RL) to predict optimal vectorization compiler pragmas ([paper](https://arxiv.org/pdf/1909.13639)). - [TVM](https://tvm.apache.org/) - Open Deep Learning Compiler Stack for cpu, gpu and specialized accelerators ([paper](https://www.usenix.org/system/files/osdi18-chen.pdf); [slides](https://www.usenix.org/sites/default/files/conference/protected-files/osdi18_slides_chen.pdf)). - [clgen](https://github.com/ChrisCummins/clgen) - Benchmark generator using LSTMs ([paper](https://chriscummins.cc/pub/2017-cgo.pdf); [slides](https://speakerdeck.com/chriscummins/synthesizing-benchmarks-for-predictive-modelling-cgo-17)). - [OpenTuner](https://github.com/jansel/opentuner) - Framework for building domain-specific multi-objective program autotuners ([paper](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf); [slides](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf)) ## Benchmarks and Datasets - [BHive](https://github.com/ithemal/bhive) - A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models ([paper](https://groups.csail.mit.edu/commit/papers/19/ithemal-measurement.pdf)). - [cBench](https://ctuning.org/wiki/index.php/CTools:CBench) - 32 C benchmarks with datasets and driver scripts. - [DeepDataFlow](https://github.com/ChrisCummins/ProGraML/blob/master/programl/Documentation/DataflowDataset.md) - 469k LLVM-IR files and 8.6B data-flow analysis labels for classification ([paper](https://arxiv.org/pdf/2003.10536.pdf)). - [devmap](https://github.com/ChrisCummins/paper-end2end-dl) - 650 OpenCL benchmark features and CPU/GPU classification labels ([paper](https://chriscummins.cc/pub/2017-pact.pdf); [slides](https://speakerdeck.com/chriscummins/end-to-end-deep-learning-of-optimization-heuristics-pact-17)). ## Conferences -

[ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI](https://www.sigplan.org/Conferences/PLDI/) -

[Architectural Support for Programming Languages and Operating Systems, ASPLOS](https://asplos-conference.org/) -

[ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP](https://dl.acm.org/conference/ppopp) -

[International Symposium on Code Generation and Optimization, CGO](https://dl.acm.org/conference/cgo) -

[International Conference on Parallel Architectures and Compilation Techniques, PACT](https://dl.acm.org/conference/cgo) -

[Object-oriented Programming, Systems, Languages, and Applications, OOPSLA](http://www.sigplan.org/Conferences/OOPSLA/) -

[International Conference on Compiler Construction, CC](https://conf.researchr.org/series/CC) -

[International Conference on Supercomputing, ICS](http://dblp.uni-trier.de/db/conf/ics/) -

[International Conference on High Performance and Embedded Architectures and Compilers, HiPEAC](http://dblp.uni-trier.de/db/conf/hipeac/) -

[International Conference on Languages, Compilers and Tools for Embedded Systems, LCTES](http://dblp.uni-trier.de/db/conf/lctrts/) -

[International Conference on Computing Frontiers, CF](http://dblp.uni-trier.de/db/conf/cf) -

[International Parallel and Distributed Processing Symposium, IPDPS](http://www.ipdps.org/) -

[International Conference for High Performance Computing, Networking, Storage, and Analysis, SC](http://supercomputing.org/) -

[Machine Learning and Programming Languages Workshop, MAPL](https://pldi20.sigplan.org/series/mapl) -

[Languages and Compilers for Parallel Computing, LCPC](https://dblp.org/db/conf/lcpc/index) -

[International Conference on Learning Representations, ICLR](https://dblp1.uni-trier.de/db/conf/iclr/) -

[Conference on Machine Learning and Systems, MLSys](https://mlsys.org/) ## How to Contribute See [Contribution Guidelines](Contributions.md). TL;DR: send one of the [maintainers](MAINTAINERS) a [pull request](https://github.com/zwang4/awesome-machine-learning-in-compilers/pulls).