[20] D. Grewe, Z. Wang, and M. F. O'Boyle. Portable mapping of data parallel programs to openc for heterogeneous systems. In Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 1-10. IEEE, 2013.
[21] X. Gu, H. Zhang, D. Zhang, and S. Kim. Deep api learning. In Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering, pages 631-642, 2016.
[22] D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan, A. Svyatkovskiy, S. Fu, et al. Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366, 2020.
[23] D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, and J. Yin. Unixcoder: Unified cross-modal pre-training for code representation. arXiv preprint arXiv:2203.03850, 2022.
[24] H.-Q. Jin, M. Frumkin, and J. Yan. The openmp implementation of nas parallel benchmarks and its performance. 1999.
[25] T. Kadosh, N. Hasabnis, T. Mattson, Y. Pinter, G. Oren, et al. Pragformer: Data-driven parallel source code classification with transformers. 2023.
[26] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[27] D. Levy and L. Wolf. Learning to align the source code to the compiled object code. In International Conference on Machine Learning, pages 2043-2051. PMLR, 2017.
[28] Y. Li, C. Gu, T. Dullien, O. Vinyals, and P. Kohli. Graph matching networks for learning the similarity of graph structured objects. In International conference on machine learning, pages 3835-3845. PMLR, 2019.
[29] Z. Li, R. Atre, Z. U. Huda, A. Jannesari, and F. Wolf. Unveiling parallelization opportunities in sequential programs. Journal of Systems and Software, 117:282-295, July 2016. doi: 10.1016/j.jss.2016.03.045.
[30] L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin. Convolutional neural networks over tree structures for programming language processing. In Proceedings of the AAAI conference on artificial intelligence, volume 30, 2016.
[31] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. URL http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
[32] L.-N. Pouchet and T. Yuki. Polybench: The polyhedral benchmark suite (version 4.2), 2017.
[33] D. Quinlan and C. Liao. The ROSE source-to-source compiler infrastructure. In Cetus users and compiler infrastructure workshop, in conjunction with PACT, volume 2011, page 1. Citeseer, 2011.
[34] J. Reinders, B. Ashbaugh, J. Brodman, M. Kinsner, J. Pennycook, X. Tian, J. Reinders, B. Ashbaugh, J. Brodman, M. Kinsner, et al. Common parallel patterns. Data Parallel  $C++$  Mastering DPC++ for Programming of Heterogeneous Systems using  $C++$  and SYCL, pages 323-352, 2021.
[35] A. Ronacher. Jinja2 documentation. Welcome to Jinja2—Jinja2 Documentation (2.8-dev), 2008.
[36] I. Sánchez Barrera, D. Black-Schaffer, M. Casas, M. Moretó, A. Stupnikova, and M. Popov. Modeling and optimizing numa effects and prefetching with machine learning. In Proceedings of the 34th ACM International Conference on Supercomputing, pages 1-13, 2020.
[37] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings 15, pages 593-607. Springer, 2018.
[38] A. TehraniJamsaz, M. Popov, A. Dutta, E. Saillard, and A. Jannesari. Learning intermediate representations using graph neural networks for numa and prefetchers optimization. In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 1206-1216. IEEE, 2022.
[39] S. VenkataKeerthy, R. Aggarwal, S. Jain, M. S. Desarkar, R. Upadrasta, and Y. Srikant. Ir2vec: Llvmir based scalable program embeddings. ACM Transactions on Architecture and Code Optimization (TACO), 17(4):1-27, 2020.
[40] M. Y. Wang. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR workshop on representation learning on graphs and manifolds, 2019.