Built on

Dagum, L., Menon, R.: Openmp: an industry standard api for shared-memory programming. IEEE Computational Science and Engineering 5
1998
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Black, S., Gao, L., Wang, P., Leahy, C., Biderman, S.: Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow. If you use this software, please cite it using these metadata 58
2021
Earlier work this paper cites.
Chen, L., Mahmud, Q.I., Jannesari, A.: Multi-view learning for parallelism discovery of sequential programs. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 295–303. IEEE (2022)
2022
Earlier work this paper cites.
Kocetkov, D., et al.: The stack: 3 tb of permissively licensed source code. Preprint (2022)
2022
Earlier work this paper cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35
2022
Earlier work this paper cites.
Xu, F.F., Alon, U., Neubig, G., Hellendoorn, V.J.: A systematic evaluation of large language models of code. In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. pp. 1–10 (2022)
2022
Earlier work this paper cites.
Bhattacharjee, A., Daley, C.S., Jannesari, A.: Openmp offload features and strategies for high performance across architectures and compilers. In: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 564–573 (2023)
2023
Earlier work this paper cites.

Similar

Chen, L., Bhattacharjee, A., Ahmed, N.K., Hasabnis, N., Oren, G., Lei, B., Jannesari, A.: CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset. arXiv preprint arXiv:2311.06505 (2023)
Original
2023
Cited alongside, same era.
Chen, L., Lin, P.H., Vanderbruggen, T., Liao, C., Emani, M., de Supinski, B.: Lm4hpc: Towards effective language model application in high-performance computing. In: International Workshop on OpenMP. pp. 18–33. Springer (2023)
2023
Cited alongside, same era.
Chen, L., Mahmud, Q.I., Phan, H., Ahmed, N., Jannesari, A.: Learning to parallelize with openmp by augmented heterogeneous ast representation. Proceedings of Machine Learning and Systems 5
2023
Cited alongside, same era.
Ding, X., Chen, L., Emani, M., Liao, C., Lin, P.H., Vanderbruggen, T., Xie, Z., Cerpa, A., Du, W.: 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. pp. 951–960 (2023)

Then

Liu, C., Bao, X., Zhang, H., Zhang, N., Hu, H., Zhang, X., Yan, M.: Improving chatgpt prompt for code generation. arXiv preprint arXiv:2305.08360 (2023)
Original
2023
Later among the works it cites.
Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., Jiang, D.: Wizardcoder: Empowering code large language models with evol-instruct. arXiv preprint arXiv:2306.08568 (2023)
Original
2023
Later among the works it cites.
Nichols, D., Marathe, A., Menon, H., Gamblin, T., Bhatele, A.: Modeling parallel programs using large language models. arXiv preprint arXiv:2306.17281 (2023)
Original
2023
Later among the works it cites.
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X.E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al.: Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)
Original
2023
Later among the works it cites.

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2024

OMPGPT: A Generative Pre-trained Transformer Model for OpenMP

Chen, Le, Bhattacharjee, Arijit, Ahmed, Nesreen et al.

Understand

Large language models (LLMs)such as ChatGPT have significantly advanced the field of Natural Language Processing (NLP).

This trend led to the development of code-based large language models such as StarCoder, WizardCoder, and CodeLlama, which are trained extensively on vast repositories of code and programming languages.
While the generic abilities of these code LLMs are useful for many programmers in tasks like code generation, the area of high-performance computing (HPC) has a narrower set of requirements that make a smaller and more domain-specific model a smarter choice.
This paper presents OMPGPT, a novel domain-specific model meticulously designed to harness the inherent strengths of language models for OpenMP pragma generation.

Built on

Dagum, L., Menon, R.: Openmp: an industry standard api for shared-memory programming. IEEE Computational Science and Engineering 5
1998
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Black, S., Gao, L., Wang, P., Leahy, C., Biderman, S.: Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow. If you use this software, please cite it using these metadata 58
2021
Earlier work this paper cites.
Chen, L., Mahmud, Q.I., Jannesari, A.: Multi-view learning for parallelism discovery of sequential programs. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 295–303. IEEE (2022)
2022
Earlier work this paper cites.
Kocetkov, D., et al.: The stack: 3 tb of permissively licensed source code. Preprint (2022)
2022
Earlier work this paper cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35
2022
Earlier work this paper cites.
Xu, F.F., Alon, U., Neubig, G., Hellendoorn, V.J.: A systematic evaluation of large language models of code. In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. pp. 1–10 (2022)
2022
Earlier work this paper cites.
Bhattacharjee, A., Daley, C.S., Jannesari, A.: Openmp offload features and strategies for high performance across architectures and compilers. In: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 564–573 (2023)
2023
Earlier work this paper cites.

Similar

Chen, L., Bhattacharjee, A., Ahmed, N.K., Hasabnis, N., Oren, G., Lei, B., Jannesari, A.: CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset. arXiv preprint arXiv:2311.06505 (2023)
Original
2023
Cited alongside, same era.
Chen, L., Lin, P.H., Vanderbruggen, T., Liao, C., Emani, M., de Supinski, B.: Lm4hpc: Towards effective language model application in high-performance computing. In: International Workshop on OpenMP. pp. 18–33. Springer (2023)
2023
Cited alongside, same era.
Chen, L., Mahmud, Q.I., Phan, H., Ahmed, N., Jannesari, A.: Learning to parallelize with openmp by augmented heterogeneous ast representation. Proceedings of Machine Learning and Systems 5
2023
Cited alongside, same era.
Ding, X., Chen, L., Emani, M., Liao, C., Lin, P.H., Vanderbruggen, T., Xie, Z., Cerpa, A., Du, W.: 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. pp. 951–960 (2023)

Then

Liu, C., Bao, X., Zhang, H., Zhang, N., Hu, H., Zhang, X., Yan, M.: Improving chatgpt prompt for code generation. arXiv preprint arXiv:2305.08360 (2023)
Original
2023
Later among the works it cites.
Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., Jiang, D.: Wizardcoder: Empowering code large language models with evol-instruct. arXiv preprint arXiv:2306.08568 (2023)
Original
2023
Later among the works it cites.
Nichols, D., Marathe, A., Menon, H., Gamblin, T., Bhatele, A.: Modeling parallel programs using large language models. arXiv preprint arXiv:2306.17281 (2023)
Original
2023
Later among the works it cites.
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X.E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al.: Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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2023

Cited alongside, same era.

Kadosh, T., Hasabnis, N., Mattson, T., Pinter, Y., Oren, G.: Quantifying openmp: Statistical insights into usage and adoption. In: 2023 IEEE High Performance Extreme Computing Conference (HPEC). pp. 1–7. IEEE (2023)

2023

Cited alongside, same era.

Kadosh, T., Hasabnis, N., Vo, V.A., Schneider, N., Krien, N., Capota, M., Wasay, A., Ahmed, N., Willke, T., Tamir, G., et al.: Domain-specific code language models: Unraveling the potential for hpc codes and tasks. arXiv preprint arXiv:2312.13322 (2023)

Original

2023

Cited alongside, same era.

Kadosh, T., Schneider, N., Hasabnis, N., Mattson, T., Pinter, Y., Oren, G.: Advising OpenMP Parallelization via A Graph-Based Approach with Transformers. In: International Workshop on OpenMP. pp. 3–17. Springer (2023)

2023

Cited alongside, same era.

Li, J., Li, G., Li, Y., Jin, Z.: Structured chain-of-thought prompting for code generation. arXiv preprint arXiv:2305.06599 (2023)

Original

2023

Cited alongside, same era.

Li, R., Allal, L.B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., et al.: Starcoder: may the source be with you! arXiv preprint arXiv:2305.06161 (2023)

Original

2023

Cited alongside, same era.

Rukmono, S.A., Ochoa, L., Chaudron, M.R.: Achieving high-level software component summarization via hierarchical chain-of-thought prompting and static code analysis. In: 2023 IEEE International Conference on Data and Software Engineering (ICoDSE). pp. 7–12. IEEE (2023)

2023

Later among the works it cites.

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)

Original

2023

Later among the works it cites.

Valero-Lara, P., Huante, A., Lail, M.A., Godoy, W.F., Teranishi, K., Balaprakash, P., Vetter, J.S.: Comparing llama-2 and gpt-3 llms for hpc kernels generation. arXiv preprint arXiv:2309.07103 (2023)

Original

2023

Later among the works it cites.

Feng, S., Chen, C.: Prompting Is All You Need: Automated Android Bug Replay with Large Language Models. In: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering. pp. 1–13 (2024)

2024

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