| 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 | 199 | 2014 |
| 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 | 163 | 2017 |
| MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... GCC summit, 2008 | 149 | 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 | 139 | 2007 |
| Compiler fuzzing through deep learning C Cummins, P Petoumenos, A Murray, H Leather Proceedings of the 27th ACM SIGSOFT International Symposium on Software …, 2018 | 90 | 2018 |
| Synthesizing benchmarks for predictive modeling C Cummins, P Petoumenos, Z Wang, H Leather 2017 IEEE/ACM International Symposium on Code Generation and Optimization …, 2017 | 88 | 2017 |
| 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 | 54 | 2017 |
| 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 | 39 | 2020 |
| Fast automatic heuristic construction using active learning WF Ogilvie, P Petoumenos, Z Wang, H Leather International Workshop on Languages and Compilers for Parallel Computing …, 2014 | 39 | 2014 |
| 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 | 34 | 2015 |
| Raced profiles: efficient selection of competing compiler optimizations H Leather, M O'Boyle, B Worton Proceedings of the 2009 ACM SIGPLAN/SIGBED conference on Languages …, 2009 | 34 | 2009 |
| Autotuning OpenCL workgroup size for stencil patterns C Cummins, P Petoumenos, M Steuwer, H Leather arXiv preprint arXiv:1511.02490, 2015 | 30 | 2015 |
| MaSiF: Machine learning guided auto-tuning of parallel skeletons A Collins, C Fensch, H Leather, M Cole 20th Annual International Conference on High Performance Computing, 186-195, 2013 | 30 | 2013 |
| 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 | 26 | 2019 |
| On the inference of user paths from anonymized mobility data G Tsoukaneri, G Theodorakopoulos, H Leather, MK Marina 2016 IEEE European Symposium on Security and Privacy (EuroS&P), 199-213, 2016 | 19 | 2016 |
| Measuring qoe of interactive workloads and characterising frequency governors on mobile devices V Seeker, P Petoumenos, H Leather, B Franke 2014 IEEE International Symposium on Workload Characterization (IISWC), 61-70, 2014 | 19 | 2014 |
| Effective function merging in the ssa form RCO Rocha, P Petoumenos, Z Wang, M Cole, H Leather Proceedings of the 41st ACM SIGPLAN Conference on Programming Language …, 2020 | 18 | 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 | 15 | 2021 |
| 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 | 14 | 2021 |
| Machine learning in compilers: Past, present and future H Leather, C Cummins 2020 Forum for Specification and Design Languages (FDL), 1-8, 2020 | 14 | 2020 |
Pavlos PetoumenosLecturer, Department of Computer Science, University of ManchesterVerified email at manchester.ac.uk
