| End-to-end Deep Learning of Optimization Heuristics C Cummins, P Petoumenos, Z Wang, H Leather 26th International Conference on Parallel Architectures and Compilation …, 2017 | 266 | 2017 |
| Compiler fuzzing through deep learning C Cummins, P Petoumenos, A Murray, H Leather Proceedings of the 27th ACM SIGSOFT international symposium on software …, 2018 | 182 | 2018 |
| Programl: A graph-based program representation for data flow analysis and compiler optimizations C Cummins, ZV Fisches, T Ben-Nun, T Hoefler, MFP O’Boyle, H Leather International Conference on Machine Learning, 2244-2253, 2021 | 129 | 2021 |
| Synthesizing Benchmarks for Predictive Modeling C Cummins, P Petoumenos, Z Wang, H Leather International Symposium on Code Generationand Optimization (CGO), 2017 | 128 | 2017 |
| Compilergym: Robust, Performant Compiler Optimization environments for AI Research C Cummins, B Wasti, J Guo, B Cui, J Ansel, S Gomez, S Jain, J Liu, ... CGO, 2022 | 83 | 2022 |
| Programl: Graph-based deep learning for program optimization and analysis C Cummins, ZV Fisches, T Ben-Nun, T Hoefler, H Leather arXiv preprint arXiv:2003.10536, 2020 | 79 | 2020 |
| Machine learning in compilers: Past, present and future H Leather, C Cummins 2020 Forum for Specification and Design Languages (FDL), 1-8, 2020 | 64 | 2020 |
| Value Learning for Throughput Optimization of Deep Learning Workloads B Steiner, C Cummins, H He, H Leather Proceedings of Machine Learning and Systems 3, 2021 | 58 | 2021 |
| Large language models for compiler optimization C Cummins, V Seeker, D Grubisic, M Elhoushi, Y Liang, B Roziere, ... arXiv preprint arXiv:2309.07062, 2023 | 49 | 2023 |
| Autotuning OpenCL Workgroup Size for Stencil Patterns C Cummins, P Petoumenos, M Steuwer, H Leather The 6th International Workshop on Adaptive Self-tuning Computing Systems, HiPEAC, 2016 | 36 | 2016 |
| PIP-DB: the protein isoelectric point database E Bunkute, C Cummins, FJ Crofts, G Bunce, IT Nabney, DR Flower Bioinformatics 31 (2), 295-296, 2015 | 33 | 2015 |
| Meta large language model compiler: Foundation models of compiler optimization C Cummins, V Seeker, D Grubisic, B Roziere, J Gehring, G Synnaeve, ... arXiv preprint arXiv:2407.02524, 2024 | 21 | 2024 |
| Slade: A portable small language model decompiler for optimized assembly J Armengol-Estapé, J Woodruff, C Cummins, MFP O'Boyle 2024 IEEE/ACM International Symposium on Code Generation and Optimization …, 2024 | 20 | 2024 |
| A case study on machine learning for synthesizing benchmarks A Goens, A Brauckmann, S Ertel, C Cummins, H Leather, J Castrillon Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine …, 2019 | 16 | 2019 |
| Deep Learning for Compilers C Cummins University of Edinburgh, 2020 | 15 | 2020 |
| Learning compiler pass orders using coreset and normalized value prediction Y Liang, K Stone, A Shameli, C Cummins, M Elhoushi, J Guo, B Steiner, ... International Conference on Machine Learning, 20746-20762, 2023 | 12 | 2023 |
| Learning space partitions for path planning K Yang, T Zhang, C Cummins, B Cui, B Steiner, L Wang, JE Gonzalez, ... Advances in Neural Information Processing Systems 34, 378-391, 2021 | 11 | 2021 |
| Profile guided optimization without profiles: A machine learning approach N Rotem, C Cummins arXiv preprint arXiv:2112.14679, 2021 | 10 | 2021 |
| Compiler generated feedback for large language models D Grubisic, C Cummins, V Seeker, H Leather arXiv preprint arXiv:2403.14714, 2024 | 9 | 2024 |
| Benchpress: A deep active benchmark generator F Tsimpourlas, P Petoumenos, M Xu, C Cummins, K Hazelwood, A Rajan, ... Proceedings of the International Conference on Parallel Architectures and …, 2022 | 9 | 2022 |
Hugh LeatherUniversity of EdinburghPatvirtintas el. paštas inf.ed.ac.uk