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Chris Cummins
Chris Cummins
FAIR, Meta
Подтвержден адрес электронной почты в домене meta.com - Главная страница
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Процитировано
Процитировано
Год
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
2632017
Compiler fuzzing through deep learning
C Cummins, P Petoumenos, A Murray, H Leather
Proceedings of the 27th ACM SIGSOFT international symposium on software …, 2018
1812018
Synthesizing Benchmarks for Predictive Modeling
C Cummins, P Petoumenos, Z Wang, H Leather
International Symposium on Code Generationand Optimization (CGO), 2017
1282017
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
1232021
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
802022
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
792020
Machine learning in compilers: Past, present and future
H Leather, C Cummins
2020 Forum for Specification and Design Languages (FDL), 1-8, 2020
622020
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
582021
Large language models for compiler optimization
C Cummins, V Seeker, D Grubisic, M Elhoushi, Y Liang, B Roziere, ...
arXiv preprint arXiv:2309.07062, 2023
472023
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
362016
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
332015
Meta large language model compiler: Foundation models of compiler optimization
C Cummins, V Seeker, D Grubisic, B Roziere, J Gehring, G Synnaeve, ...
arXiv preprint arXiv:2407.02524, 2024
182024
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
172024
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
162019
Deep Learning for Compilers
C Cummins
University of Edinburgh, 2020
152020
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
112021
Learning compiler pass orders using coreset and normalized value prediction
Y Liang, K Stone, A Shameli, C Cummins, M Elhoushi, J Guo, B Steiner, ...
International Conference on Machine Learning, 20746-20762, 2023
102023
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
92022
Profile guided optimization without profiles: A machine learning approach
N Rotem, C Cummins
arXiv preprint arXiv:2112.14679, 2021
92021
Caviar: an e-graph based TRS for automatic code optimization
S Kourta, AA Namani, F Benbouzid-Si Tayeb, K Hazelwood, C Cummins, ...
Proceedings of the 31st ACM SIGPLAN International Conference on Compiler …, 2022
72022
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Статьи 1–20