LM4HPC: Towards Effective Language Model Application in High-Performance Computing

Jin, H.Q., Frumkin, M., Yan, J.: The OpenMP implementation of NAS parallel benchmarks and its performance (1999)

1999

Earlier work this paper cites.

Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Lee, S.H., Skadron, K.: Rodinia: A benchmark suite for heterogeneous computing. In: 2009 IEEE international symposium on workload characterization (IISWC). pp. 44–54. Ieee (2009)

2009

Earlier work this paper cites.

Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the AAAI conference on artificial intelligence. vol. 30 (2016)

2016

Earlier work this paper cites.

Liao, C., Lin, P.H., Asplund, J., Schordan, M., Karlin, I.: Dataracebench: a benchmark suite for systematic evaluation of data race detection tools. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1–14 (2017)

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.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Earlier work this paper cites.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33

2020

Earlier work this paper cites.

Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al.: Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)

Original

2020

Earlier work this paper cites.

Jin, D., Pan, E., Oufattole, N., Weng, W.H., Fang, H., Szolovits, P.: What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams. arXiv preprint arXiv:2009.13081 (2020)

Original

2020

Earlier work this paper cites.

Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., Ma, S.: CodeBLEU: a method for automatic evaluation of code synthesis. arXiv preprint arXiv:2009.10297 (2020)

Original

2020

Earlier work this paper cites.

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)

Original

2021

Cited alongside, same era.

Verma, G., Emani, M., Liao, C., Lin, P.H., Vanderbruggen, T., Shen, X., Chapman, B.: HPCFAIR: Enabling FAIR AI for HPC Applications. In: 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC). pp. 58–68. IEEE (2021)

2021

Cited alongside, same era.

Wang, Y., Wang, W., Joty, S., Hoi, S.C.: Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 (2021)

Original

2021

Cited alongside, same era.

Armengol-Estapé, J., Woodruff, J., Brauckmann, A., Magalhães, J.W.d.S., O’Boyle, M.F.: ExeBench: an ML-scale dataset of executable C functions. In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. pp. 50–59 (2022)

ProGraML: Program Graphs for Machine Learning (2022), https://pypi.org/project/programl/ , accessed: 2023-05-15

2023

Closest in time.

The py-tree-sitter project (2023), https://pypi.org/project/tree-sitter-builds/ , accessed: 2023-05-15

2023

Closest in time.

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., et al.: Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023)

Original

2023

Closest in time.

Chase, H.: LangChain: Next Generation Language Processing (2023), https://langchain.com/ , accessed: 2023-05-15

2023

Closest in time.

Chen, L., Mahmud, Q.I., Phan, H., Ahmed, N.K., Jannesari, A.: Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation. arXiv preprint arXiv:2305.05779 (2023)

Original

2023