| Automatic feature generation for machine learning based optimizing compilation H Leather, E Bonilla, M O'Boyle 2009 International Symposium on Code Generation and Optimization, 81-91, 2009 | 156 | 2009 |
| MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... GCC summit, 2008 | 141 | 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 | 127 | 2007 |
| 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 | 101 | 2017 |
| Synthesizing benchmarks for predictive modeling C Cummins, P Petoumenos, Z Wang, H Leather 2017 IEEE/ACM International Symposium on Code Generation and Optimization …, 2017 | 59 | 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 | 53 | 2018 |
| 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 | 46 | 2017 |
| 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 | 34 | 2014 |
| 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 |
| 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 | 30 | 2014 |
| 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 | 25 | 2013 |
| 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 | 24 | 2015 |
| Autotuning OpenCL workgroup size for stencil patterns C Cummins, P Petoumenos, M Steuwer, H Leather arXiv preprint arXiv:1511.02490, 2015 | 24 | 2015 |
| 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 | 20 | 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 | 15 | 2014 |
| Auto-tuning parallel skeletons A Collins, C Fensch, H Leather Parallel Processing Letters 22 (02), 1240005, 2012 | 14 | 2012 |
| 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 | 13 | 2020 |
| Application of domain-aware binary fuzzing to aid Android virtual machine testing S Kyle, H Leather, B Franke, D Butcher, S Monteith ACM SIGPLAN Notices 50 (7), 121-132, 2015 | 12 | 2015 |
| Efficiently parallelizing instruction set simulation of embedded multi-core processors using region-based just-in-time dynamic binary translation S Kyle, I Böhm, B Franke, H Leather, N Topham ACM SIGPLAN Notices 47 (5), 21-30, 2012 | 12 | 2012 |
| ALEA: A fine-grained energy profiling tool L Mukhanov, P Petoumenos, Z Wang, N Parasyris, DS Nikolopoulos, ... ACM Transactions on Architecture and Code Optimization (TACO) 14 (1), 1-25, 2017 | 11 | 2017 |
Pavlos PetoumenosLecturer, Department of Computer Science, University of ManchesterVerified email at manchester.ac.uk
