Chris Cummins
Chris Cummins
Facebook AI Research
Verified email at - Homepage
Cited by
Cited by
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
Compiler fuzzing through deep learning
C Cummins, P Petoumenos, A Murray, H Leather
Proceedings of the 27th ACM SIGSOFT International Symposium on Software …, 2018
Synthesizing Benchmarks for Predictive Modeling
C Cummins, P Petoumenos, Z Wang, H Leather
International Symposium on Code Generationand Optimization (CGO), 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
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
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
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
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
Machine learning in compilers: Past, present and future
H Leather, C Cummins
2020 Forum for Specification and Design Languages (FDL), 1-8, 2020
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, ...
2022 IEEE/ACM International Symposium on Code Generation and Optimization …, 2022
Deep Learning for Compilers
C Cummins
University of Edinburgh, 2020
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
Deep data flow analysis
C Cummins, H Leather, Z Fisches, T Ben-Nun, T Hoefler, M O'Boyle
arXiv preprint arXiv:2012.01470, 2020
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
Value function based performance optimization of deep learning workloads
B Steiner, C Cummins, H He, H Leather
arXiv preprint arXiv:2011.14486, 2020
DeepSmith: Compiler Fuzzing through Deep Learning
C Cummins, P Petoumenos, A Murray, H Leather
ACACES, 2018
Program Graphs for Machine Learning
C Cummins, Z Fisches, T Ben-Nun, T Hoefler, H Leather, M O’Boyle
NeurIPS, 2020
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
Profile Guided Optimization without Profiles: A Machine Learning Approach
N Rotem, C Cummins
arXiv preprint arXiv:2112.14679, 2021
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
The system can't perform the operation now. Try again later.
Articles 1–20