| End-to-end deep learning of optimization heuristics C Cummins, P Petoumenos, Z Wang, H Leather 2017 26th International Conference on Parallel Architectures and Compilation …, 2017 | 255 | 2017 |
| Automatic feature generation for machine learning--based optimising compilation H Leather, E Bonilla, M O'boyle ACM Transactions on Architecture and Code Optimization (TACO) 11 (1), 1-32, 2014 | 221 | 2014 |
| Compiler fuzzing through deep learning C Cummins, P Petoumenos, A Murray, H Leather Proceedings of the 27th ACM SIGSOFT international symposium on software …, 2018 | 174 | 2018 |
| MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... Proceedings of the GCC Developers' Summit, 2008 | 171 | 2008 |
| Emergency evacuation using wireless sensor networks M Barnes, H Leather, DK Arvind 32nd IEEE Conference on Local Computer Networks (LCN 2007), 851-857, 2007 | 147 | 2007 |
| Synthesizing benchmarks for predictive modeling C Cummins, P Petoumenos, Z Wang, H Leather 2017 IEEE/ACM International Symposium on Code Generation and Optimization …, 2017 | 121 | 2017 |
| 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 | 113 | 2021 |
| 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 | 78 | 2020 |
| 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, ... 2022 IEEE/ACM International Symposium on Code Generation and Optimization …, 2022 | 72 | 2022 |
| Minimizing the cost of iterative compilation with active learning WF Ogilvie, P Petoumenos, Z Wang, H Leather 2017 IEEE/ACM international symposium on code generation and optimization …, 2017 | 69 | 2017 |
| Code translation with compiler representations M Szafraniec, B Roziere, H Leather, F Charton, P Labatut, G Synnaeve arXiv preprint arXiv:2207.03578, 2022 | 63 | 2022 |
| Machine learning in compilers: Past, present and future H Leather, C Cummins 2020 Forum for Specification and Design Languages (FDL), 1-8, 2020 | 55 | 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, 323-334, 2021 | 50 | 2021 |
| Power capping: What works, what does not P Petoumenos, L Mukhanov, Z Wang, H Leather, DS Nikolopoulos 2015 IEEE 21st International Conference on Parallel and Distributed Systems …, 2015 | 45 | 2015 |
| Fast automatic heuristic construction using active learning WF Ogilvie, P Petoumenos, Z Wang, H Leather Languages and Compilers for Parallel Computing: 27th International Workshop …, 2015 | 44 | 2015 |
| Cruxeval: A benchmark for code reasoning, understanding and execution A Gu, B Rozičre, H Leather, A Solar-Lezama, G Synnaeve, SI Wang arXiv preprint arXiv:2401.03065, 2024 | 40 | 2024 |
| Function merging by sequence alignment RCO Rocha, P Petoumenos, Z Wang, M Cole, H Leather 2019 IEEE/ACM International Symposium on Code Generation and Optimization …, 2019 | 39 | 2019 |
| Autotuning OpenCL workgroup size for stencil patterns C Cummins, P Petoumenos, M Steuwer, H Leather arXiv preprint arXiv:1511.02490, 2015 | 37 | 2015 |
| Large language models for compiler optimization C Cummins, V Seeker, D Grubisic, M Elhoushi, Y Liang, B Roziere, ... arXiv preprint arXiv:2309.07062, 2023 | 35 | 2023 |
| Masif: Machine learning guided auto-tuning of parallel skeletons A Collins, C Fensch, H Leather Proceedings of the 21st international conference on Parallel architectures …, 2012 | 35 | 2012 |
Chris CumminsFAIR, MetaVerified email at meta.com
