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Hugh Leather
Hugh Leather
Verified email at inf.ed.ac.uk
Title
Cited by
Cited by
Year
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
1992014
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
1632017
MILEPOST GCC: machine learning based research compiler
G Fursin, C Miranda, O Temam, M Namolaru, A Zaks, B Mendelson, ...
GCC summit, 2008
1502008
Emergency evacuation using wireless sensor networks
M Barnes, H Leather, DK Arvind
32nd IEEE Conference on Local Computer Networks (LCN 2007), 851-857, 2007
1392007
Compiler fuzzing through deep learning
C Cummins, P Petoumenos, A Murray, H Leather
Proceedings of the 27th ACM SIGSOFT International Symposium on Software …, 2018
902018
Synthesizing benchmarks for predictive modeling
C Cummins, P Petoumenos, Z Wang, H Leather
2017 IEEE/ACM International Symposium on Code Generation and Optimization …, 2017
882017
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
542017
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
392020
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
392014
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
342015
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
342009
Autotuning OpenCL workgroup size for stencil patterns
C Cummins, P Petoumenos, M Steuwer, H Leather
arXiv preprint arXiv:1511.02490, 2015
302015
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
302013
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
262019
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
192016
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
192014
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
182020
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
152021
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
142021
Machine learning in compilers: Past, present and future
H Leather, C Cummins
2020 Forum for Specification and Design Languages (FDL), 1-8, 2020
142020
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