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Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others.
M. Snir, MPI–the Complete Reference: The MPI core , ser. MPI: The Complete Reference. Mass, 1998. [Online]. Available: https://books.google.com/books?id=x79puJ2YkroC
1998
Earlier work this paper cites.
J. C. S. Grauer-Gray, “Polybench,” https://web.cs.ucla.edu/~pouchet/software/polybench/
2012
Earlier work this paper cites.
“OpenMP Application Program Interface. Version 4.0. July 2013,” 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Jaques, A. Ghandeharioun, J. H. Shen, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, and R. Picard, “Way off-policy batch deep reinforcement learning of implicit human preferences in dialog,” 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=rygGQyrFvH
2020
Earlier work this paper cites.
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving, “Fine-tuning language models from human preferences,” 2020
2020
Earlier work this paper cites.
L. von Werra, Y. Belkada, L. Tunstall, E. Beeching, T. Thrush, N. Lambert, and S. Huang, “Trl: Transformer reinforcement learning,” https://github.com/huggingface/trl
2020
Earlier work this paper cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-Art Natural Language Processing.” Association for Computational Linguistics, Oct. 2020, pp. 38–45. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-demos.6
2020
Earlier work this paper cites.
M. Chen and et al, “Evaluating large language models trained on code,” 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021
2021
Earlier work this paper cites.
M. Yasunaga and P. Liang, “Break-it-fix-it: Unsupervised learning for program repair,” in International conference on machine learning . PMLR, 2021, pp. 11 941–11 952
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
A. Kharkar, R. Z. Moghaddam, M. Jin, X. Liu, X. Shi, C. B. Clement, and N. Sundaresan, “Learning to reduce false positives in analytic bug detectors,” 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pp. 1307–1316, 2022
2022
Earlier work this paper cites.
S. Haque, Z. Eberhart, A. Bansal, and C. McMillan, “Semantic similarity metrics for evaluating source code summarization,” 2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC) , pp. 36–47, 2022
2022
Earlier work this paper cites.
J. Gu, P. Salza, and H. C. Gall, “Assemble foundation models for automatic code summarization,” 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) , pp. 935–946, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
D. Kocetkov, R. Li, L. Ben Allal, J. Li, C. Mou, C. Muñoz Ferrandis, Y. Jernite, M. Mitchell, S. Hughes, T. Wolf, D. Bahdanau, L. von Werra, and H. de Vries, “The stack: 3 tb of permissively licensed source code,” Preprint , 2022
2022
Cited alongside, same era.
Z. Wang, Y. Dong, J. Zeng, V. Adams, M. N. Sreedhar, D. Egert, O. Delalleau, J. P. Scowcroft, N. Kant, A. Swope, and O. Kuchaiev, “Helpsteer: Multi-attribute helpfulness dataset for steerlm,” 2023
2023
Later among the works it cites.
“Big code models leaderboard - a hugging face space by bigcode,” 2023. [Online]. Available: https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
2023
Later among the works it cites.
Y. Zhao, A. Gu, R. Varma, L. Luo, C.-C. Huang, M. Xu, L. Wright, H. Shojanazeri, M. Ott, S. Shleifer, A. Desmaison, C. Balioglu, P. Damania, B. Nguyen, G. Chauhan, Y. Hao, A. Mathews, and S. Li, “Pytorch fsdp: Experiences on scaling fully sharded data parallel,” Proc. VLDB Endow. , vol. 16, no. 12, p. 3848–3860, aug 2023
2023
Later among the works it cites.
H. Touvron et al. , “Llama 2: Open foundation and fine-tuned chat models,” 2023
2023
Later among the works it cites.
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Y. Bai, A. Jones, K. Ndousse, A. Askell, A. Chen, N. DasSarma, D. Drain, S. Fort, D. Ganguli, T. Henighan, N. Joseph, S. Kadavath, J. Kernion, T. Conerly, S. El-Showk, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, T. Hume, S. Johnston, S. Kravec, L. Lovitt, N. Nanda, C. Olsson, D. Amodei, T. Brown, J. Clark, S. McCandlish, C. Olah, B. Mann, and J. Kaplan, “Training a helpful and harmless assistant with reinforcement learning from human feedback,” 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Y. Khan and G. Uddin, “Automatic code documentation generation using gpt-3,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–6
2022
Cited alongside, same era.
2022
Cited alongside, same era.
D. Nichols, A. Marathe, H. Menon, T. Gamblin, and A. Bhatele, “Modeling parallel programs using large language models,” 2023
2023
Cited alongside, same era.
P. Valero-Lara, A. Huante, M. A. Lail, W. F. Godoy, K. Teranishi, P. Balaprakash, and J. S. Vetter, “Comparing llama-2 and gpt-3 llms for hpc kernels generation,” 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
Phind. (2023) Phind-codellama-34b-v2. [Online]. Available: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
2023
Later among the works it cites.
D. Guo, C. Xu, N. Duan, J. Yin, and J. McAuley, “Longcoder: A long-range pre-trained language model for code completion,” in International Conference on Machine Learning . PMLR, 2023, pp. 12 098–12 107
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Sobania, M. Briesch, C. Hanna, and J. Petke, “An analysis of the automatic bug fixing performance of chatgpt,” in 2023 IEEE/ACM International Workshop on Automated Program Repair (APR) . IEEE, 2023, pp. 23–30
2023
Later among the works it cites.
M. Schäfer, S. Nadi, A. Eghbali, and F. Tip, “An empirical evaluation of using large language models for automated unit test generation,” IEEE Transactions on Software Engineering , 2023
2023
Later among the works it cites.
L. Chen, P.-H. Lin, T. Vanderbruggen, C. Liao, M. Emani, and B. de Supinski, “Lm4hpc: Towards effective language model application in high-performance computing,” in OpenMP: Advanced Task-Based, Device and Compiler Programming , S. McIntosh-Smith, M. Klemm, B. R. de Supinski, T. Deakin, and J. Klinkenberg, Eds. Cham: Springer Nature Switzerland, 2023, pp. 18–33
2023
Later among the works it cites.
X. Ding, L. Chen, M. Emani, C. Liao, P.-H. Lin, T. Vanderbruggen, Z. Xie, A. Cerpa, and W. Du, “Hpc-gpt: Integrating large language model for high-performance computing,” in Proceedings of the SC’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis , 2023, pp. 951–960
2023
Later among the works it cites.
L. Chen, X. Ding, M. Emani, T. Vanderbruggen, P. hung Lin, and C. Liao, “Data race detection using large language models,” 2023
2023
Later among the works it cites.
D. J. Mankowitz, A. Michi, A. Zhernov, M. Gelmi, M. Selvi, C. Paduraru, E. Leurent, S. Iqbal, J.-B. Lespiau, A. Ahern et al. , “Faster sorting algorithms discovered using deep reinforcement learning,” Nature , vol. 618, no. 7964, pp. 257–263, 2023
2023
Later among the works it cites.
D. Nichols, J. H. Davis, Z. Xie, A. Rajaram, and A. Bhatele, “Can large language models write parallel code?” 2024
2024
Closest in time.
D. Guo, Q. Zhu, D. Yang, Z. Xie, K. Dong, W. Zhang, G. Chen, X. Bi, Y. Wu, Y. K. Li, F. Luo, Y. Xiong, and W. Liang, “Deepseek-coder: When the large language model meets programming – the rise of code intelligence,” 2024
2024
Closest in time.
L. Ben Allal, A. Lozhkov, G. Penedo, T. Wolf, and L. von Werra, “Cosmopedia,” 2024. [Online]. Available: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia
2024
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H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Akhtar, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” 2024
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