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The performance of the code a compiler generates depends on the order in which it applies the optimization passes.
A view on deep reinforcement learning in system optimization
Haj-Ali, A., K. Ahmed, N., Willke, T., Gonzalez, J., Asanovic, K., and Stoica, I · 1908
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Introduction to reinforcement learning , volume 135
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Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y · 2000
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Random forests
Breiman, L · 2001
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Meta optimization: Improving compiler heuristics with machine learning
Stephenson, M., Amarasinghe, S., Martin, M., and O’Reilly, U.-M · 2003
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Compiler optimization-space exploration
Triantafyllis, S., Vachharajani, M., Vachharajani, N., and August, D. I · 2003
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Finding effective compilation sequences
Almagor, L., Cooper, K. D., Grosul, A., Harvey, T. J., Reeves, S. W., Subramanian, D., Torczon, L., and Waterman, T · 2004
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Llvm: A compilation framework for lifelong program analysis & transformation
Lattner, C. and Adve, V · 2004
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Using machine learning to focus iterative optimization
Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O’Boyle, M. F., Thomson, J., Toussaint, M., and Williams, C. K · 2006
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Genetic algorithms
Goldberg, D. E · 2006
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Fast and effective orchestration of compiler optimizations for automatic performance tuning
Pan, Z. and Eigenmann, R · 2006
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CHstone: A benchmark program suite for practical c-based high-level synthesis
Hara, Y., Tomiyama, H., Honda, S., Takada, H., and Ishii, K · 2008
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Particle swarm optimization
Kennedy, J · 2010
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Milepost gcc: Machine learning enabled self-tuning compiler
Fursin, G., Kashnikov, Y., Memon, A. W., Chamski, Z., Temam, O., Namolaru, M., Yom-Tov, E., Mendelson, B., Zaks, A., Courtois, E., et al · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
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Finding and understanding bugs in c compilers
Yang, X., Chen, Y., Eide, E., and Regehr, J · 2011
Openai gym, 2016
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Rllib: Abstractions for distributed reinforcement learning
Liang, E., Liaw, R., Moritz, P., Nishihara, R., Fox, R., Goldberg, K., Gonzalez, J. E., Jordan, M. I., and Stoica, I · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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DEAP: Evolutionary algorithms made easy
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., and Gagné, C · 2012
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Mitigating the compiler optimization phase-ordering problem using machine learning
Kulkarni, S. and Cavazos, J · 2012
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Legup: An open-source high-level synthesis tool for fpga-based processor/accelerator systems
Canis, A., Choi, J., Aldham, M., Zhang, V., Kammoona, A., Czajkowski, T., Brown, S. D., and Anderson, J. H · 2013
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The effect of compiler optimizations on high-level synthesis for fpgas
Huang, Q., Lian, R., Canis, A., Choi, J., Xi, R., Brown, S., and Anderson, J · 2013
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Opentuner: An extensible framework for program autotuning
Ansel, J., Kamil, S., Veeramachaneni, K., Ragan-Kelley, J., Bosboom, J., O’Reilly, U.-M., and Amarasinghe, S · 2014
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The effect of compiler optimizations on high-level synthesis-generated hardware
Huang, Q., Lian, R., Canis, A., Choi, J., Xi, R., Calagar, N., Brown, S., and Anderson, J · 2015
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Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
Conti, E., Madhavan, V., Such, F. P., Lehman, J., Stanley, K., and Clune, J · 2018
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Ray: A distributed framework for emerging { \{ AI } \} applications
Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., et al · 2018
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Machine learning in compiler optimization
Wang, Z. and OBoyle, M · 2018
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Autophase: Compiler phase-ordering for hls with deep reinforcement learning
Huang, Q., Haj-Ali, A., Moses, W., Xiang, J., Stoica, I., Asanovic, K., and Wawrzynek, J · 2019
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Intel High-Level Synthesis Compiler, 2019
Intel · 2019
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Vivado High-Level Synthesis, 2019
Xilinx · 2019
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Neurovectorizer: End-to-end vectorization with deep reinforcement learning
Haj-Ali, A., Ahmed, N. K., Willke, T., Shao, S., Asanovic, K., and Stoica, I · 2020
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