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Chris Cummins
Chris Cummins
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Title
Cited by
Cited by
Year
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
2412017
Compiler fuzzing through deep learning
C Cummins, P Petoumenos, A Murray, H Leather
Proceedings of the 27th ACM SIGSOFT international symposium on software …, 2018
1672018
Synthesizing Benchmarks for Predictive Modeling
C Cummins, P Petoumenos, Z Wang, H Leather
International Symposium on Code Generationand Optimization (CGO), 2017
1192017
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
1002021
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
742020
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
632022
Machine learning in compilers: Past, present and future
H Leather, C Cummins
2020 Forum for Specification and Design Languages (FDL), 1-8, 2020
512020
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
462021
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
372016
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
312015
Large language models for compiler optimization
C Cummins, V Seeker, D Grubisic, M Elhoushi, Y Liang, B Roziere, ...
arXiv preprint arXiv:2309.07062, 2023
212023
Deep Learning for Compilers
C Cummins
University of Edinburgh, 2020
142020
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
142019
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
92021
Profile guided optimization without profiles: A machine learning approach
N Rotem, C Cummins
arXiv preprint arXiv:2112.14679, 2021
82021
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
72024
Deep data flow analysis
C Cummins, H Leather, Z Fisches, T Ben-Nun, T Hoefler, M O'Boyle
arXiv preprint arXiv:2012.01470, 2020
62020
Towards Collaborative Performance Tuning of Algorithmic Skeletons
C Cummins, P Petoumenos, M Steuwer, H Leather
High-Level Programming for Heterogeneous and Hierarchical Parallel Systems …, 2016
62016
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
52022
Q-gym: An equality saturation framework for dnn inference exploiting weight repetition
C Fu, H Huang, B Wasti, C Cummins, R Baghdadi, K Hazelwood, Y Tian, ...
Proceedings of the International Conference on Parallel Architectures and …, 2022
52022
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