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We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly.
A New Algorithm for Data Compression
Gage, P · 1994
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Iterative compilation in a non-linear optimisation space
Bodin, F., Kisuki, T., Knijnenburg, P., O’Boyle, M., and Rohou, E · 1998
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Combined selection of tile sizes and unroll factors using iterative compilation
Kisuki, T., Knijnenburg, P., and O’Boyle, M · 2000
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BLEU: A Method for Automatic Evaluation of Machine Translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Evaluating Iterative Compilation
Fursin, G. G., O’Boyle, M. F. P., and Knijnenburg, P. M. W · 2005
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Using machine learning to focus iterative optimization
Agakov, F., Bonilla, E., Cavazos, J., Franke, B., Fursin, G., O’Boyle, M., Thomson, J., Toussaint, M., and Williams, C · 2006
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End-to-End Deep Learning of Optimization Heuristics
Cummins, C., Petoumenos, P., Wang, Z., and Leather, H · 2017
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Decoupled Weight Decay Regularization
Loshchilov, I. and Hutter, F · 2017
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Minimizing the cost of iterative compilation with active learning
Ogilvie, W. F., Petoumenos, P., Wang, Z., and Leather, H · 2017
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Machine Learning in Compiler Optimisation
Wang, Z. and O’Boyle, M · 2018
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Unsupervised Translation of Programming Languages
Lachaux, M.-A., Roziere, B., Chanussot, L., and Lample, G · 2020
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Machine Learning in Compilers: Past, Present and Future
Leather, H. and Cummins, C · 2020
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Learning C to x86 Translation: An Experiment in Neural Compilation
Armengol-Estapé, J. and O’Boyle, M. F · 2021
Cited alongside, same era.
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Cited alongside, same era.
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
Cummins, C., Fisches, Z., Ben-Nun, T., Hoefler, T., O’Boyle, M., and Leather, H · 2021
Cited alongside, same era.
Anghabench: A suite with one million compilable c benchmarks for code-size reduction
Da Silva, A. F., Kind, B. C., de Souza Magalhães, J. W., Rocha, J. N., Guimaraes, B. C. F., and Pereira, F. M. Q · 2021
SantaCoder: don’t reach for the stars!
Allal, L. B., Li, R., Kocetkov, D., Mou, C., Akiki, C., Ferrandis, C. M., Muennighoff, N., Mishra, M., Gu, A., Dey, M., Umapathi, L. K., Anderson, C. J., Zi, Y., Poirier, J. L., Schoelkopf, H., Troshin, S., Abulkhanov, D., Romero, M., Lappert, M., Toni, F. D., del Río, B. G., Liu, Q., Bose, S., Bhattacharyya, U., Zhuo, T. Y., Yu, I., Villegas, P., Zocca, M., Mangrulkar, S., Lansky, D., Nguyen, H., Contractor, D., Villa, L., Li, J., Bahdanau, D., Jernite, Y., Hughes, S., Fried, D., Guha, A., de Vries, H., and von Werra, L · 2023
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Large language models for compiler optimization
Cummins, C., Seeker, V., Grubisic, D., Elhoushi, M., Liang, Y., Roziere, B., Gehring, J., Gloeckle, F., Hazelwood, K., Synnaeve, G., et al · 2023
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Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Deng, Y., Xia, C. S., Peng, H., Yang, C., and Zhang, L · 2023
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InCoder: A Generative Model for Code Infilling and Synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-t., Zettlemoyer, L., and Lewis, M · 2023
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Cited alongside, same era.
A Flexible Approach to Autotuning Multi-pass Machine Learning Compilers
Phothilimthana, P. M., Sabne, A., Sarda, N., Murthy, K. S., Zhou, Y., Angermueller, C., Burrows, M., Roy, S., Mandke, K., Farahani, R., et al · 2021
Cited alongside, same era.
MLGO: a Machine Learning Guided Compiler Optimizations Framework
Trofin, M., Qian, Y., Brevdo, E., Lin, Z., Choromanski, K., and Li, D · 2021
Cited alongside, same era.
Automated conformance testing for JavaScript engines via deep compiler fuzzing
Ye, G., Tang, Z., Tan, S. H., Huang, S., Fang, D., Sun, X., Bian, L., Wang, H., and Wang, Z · 2021
Cited alongside, same era.
Exebench: an ml-scale dataset of executable c functions
Armengol-Estapé, J., Woodruff, J., Brauckmann, A., Magalhães, J. W. d. S., and O’Boyle, M. F · 2022
Cited alongside, same era.
MLGOPerf: An ML Guided Inliner to Optimize Performance
Ashouri, A. H., Elhoushi, M., Hua, Y., Wang, X., Manzoor, M. A., Chan, B., and Gao, Y · 2022
Cited alongside, same era.
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
Cited alongside, same era.
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., Hubert, T., Choy, P., d’Autume, C. d. M., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Cherepanov, A., Molloy, J., Mankowitz, D. J., Robson, E. S., Kohli, P., de Freitas, N., Kavukcuoglu, K., and Vinyals, O · 2022
Cited alongside, same era.
Code Translation with Compiler Representations
Szafraniec, M., Roziere, B., Charton, F., Leather, H., Labatut, P., and Synnaeve, G · 2022
Cited alongside, same era.
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Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Giorno, A. D., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., Salim, A., Shah, S., Behl, H. S., Wang, X., Bubeck, S., Eldan, R., Kalai, A. T., Lee, Y. T., and Li, Y · 2023
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StarCoder: may the source be with you!
Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., Liu, Q., Zheltonozhskii, E., Zhuo, T. Y., Wang, T., Dehaene, O., Davaadorj, M., Lamy-Poirier, J., Monteiro, J., Shliazhko, O., Gontier, N., Meade, N., Zebaze, A., Yee, M.-H., Umapathi, L. K., Zhu, J., Lipkin, B., Oblokulov, M., Wang, Z., Murthy, R., Stillerman, J., Patel, S. S., Abulkhanov, D., Zocca, M., Dey, M., Zhang, Z., Fahmy, N., Bhattacharyya, U., Yu, W., Singh, S., Luccioni, S., Villegas, P., Kunakov, M., Zhdanov, F., Romero, M., Lee, T., Timor, N., Ding, J., Schlesinger, C., Schoelkopf, H., Ebert, J., Dao, T., Mishra, M., Gu, A., Robinson, J., Anderson, C. J., Dolan-Gavitt, B., Contractor, D., Reddy, S., Fried, D., Bahdanau, D., Jernite, Y., Ferrandis, C. M., Hughes, S., Wolf, T., Guha, A., von Werra, L., and de Vries, H · 2023
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Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Liang, Y., Stone, K., Shameli, A., Cummins, C., Elhoushi, M., Guo, J., Steiner, B., Yang, X., Xie, P., Leather, H., and Tian, Y · 2023
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OpenAI · 2023
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Code Llama: Open Foundation Models for Code
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M., Ferrer, C. C., Grattafiori, A., Xiong, W., Défossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2023
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Adaptive Test Generation Using a Large Language Model
Schäfer, M., Nadi, S., Eghbali, A., and Tip, F · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Automated program repair in the era of large pre-trained language models
Xia, C. S., Wei, Y., and Zhang, L · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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