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The polyhedral model allows a structured way of defining semantics-preserving transformations to improve the performance of a large class of loops.
P. Feautrier, “Some efficient solutions to the affine scheduling problem. i. one-dimensional time,” International journal of parallel programming , vol. 21, no. 5, pp. 313–347, 1992
1992
Earlier work this paper cites.
——, “Some efficient solutions to the affine scheduling problem. part ii. multidimensional time,” International journal of parallel programming , vol. 21, no. 6, pp. 389–420, 1992
1992
Earlier work this paper cites.
H. Le Verge, “A Note on Chernikova’s algorithm,” INRIA, Research Report RR-1662, 1992. [Online]. Available: https://hal.inria.fr/inria-00074895
1992
Earlier work this paper cites.
A. Schrijver, Theory of linear and integer programming . John Wiley & Sons, 1998
1998
Earlier work this paper cites.
S. Pop, A. Cohen, C. Bastoul, S. Girbal, G.-A. Silber, and N. Vasilache, “Graphite: Polyhedral analyses and optimizations for gcc,” in Proceedings of the 2006 GCC Developers Summit , 2006, p. 2006
2006
Earlier work this paper cites.
U. Bondhugula and J. Ramanujam, “Pluto: A practical and fully automatic polyhedral parallelizer and locality optimizer,” 2007
2007
Earlier work this paper cites.
L.-N. Pouchet, C. Bastoul, A. Cohen, and N. Vasilache, “Iterative optimization in the polyhedral model: Part i, one-dimensional time,” in International Symposium on Code Generation and Optimization (CGO’07) . IEEE, 2007, pp. 144–156
2007
Earlier work this paper cites.
L.-N. Pouchet, C. Bastoul, A. Cohen, and J. Cavazos, “Iterative optimization in the polyhedral model: Part ii, multidimensional time,” ACM SIGPLAN Notices , vol. 43, no. 6, pp. 90–100, 2008
2008
Earlier work this paper cites.
S. Verdoolaege, “isl: An integer set library for the polyhedral model,” in International Congress on Mathematical Software . Springer, 2010, pp. 299–302
2010
Earlier work this paper cites.
T. Grosser, H. Zheng, R. Aloor, A. Simbürger, A. Größlinger, and L.-N. Pouchet, “Polly-polyhedral optimization in llvm,” in Proceedings of the First International Workshop on Polyhedral Compilation Techniques (IMPACT) , vol. 2011, 2011, p. 1
2011
Earlier work this paper cites.
L.-N. Pouchet et al. (2012) Polybench: The polyhedral benchmark suite. [Online]. Available: http://web.cs.ucla.edu/~pouchet/software/polybench/
2012
Earlier work this paper cites.
J. Ragan-Kelley, C. Barnes, A. Adams, S. Paris, F. Durand, and S. Amarasinghe, “Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines,” Acm Sigplan Notices , vol. 48, no. 6, pp. 519–530, 2013
2013
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Cited alongside, same era.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Ganser, A. Grösslinger, N. Siegmund, S. Apel, and C. Lengauer, “Iterative schedule optimization for parallelization in the polyhedron model,” ACM Transactions on Architecture and Code Optimization (TACO) , vol. 14, no. 3, pp. 1–26, 2017
K. Vadivel, L. Chelini, A. BanaGozar, G. Singh, S. Corda, R. Jordans, and H. Corporaal, “Tdo-cim: Transparent detection and offloading for computation in-memory,” in 2020 Design, Automation Test in Europe Conference Exhibition (DATE) , 2020, pp. 1602–1605
2020
Later among the works it cites.
A. A. Khan, H. Mewes, T. Grosser, T. Hoefler, and J. Castrillon, “Polyhedral compilation for racetrack memories,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Special issue on Compilers, Architecture, and Synthesis of Embedded Systems (CASES’20) , vol. 39, no. 11, pp. 3968–3980, Oct. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9216560
2020
Later among the works it cites.
H. Leather and C. Cummins, “Machine learning in compilers: Past, present and future,” in 2020 Forum for Specification and Design Languages (FDL) . IEEE, 2020, pp. 1–8
2020
Later among the works it cites.
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2017
Cited alongside, same era.
C. Cummins, P. Petoumenos, Z. Wang, and H. Leather, “End-to-end deep learning of optimization heuristics,” in 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT) . IEEE, 2017, pp. 219–232
2017
Cited alongside, same era.
2018
Cited alongside, same era.
R. Gareev, T. Grosser, and M. Kruse, “High-performance generalized tensor operations: A compiler-oriented approach,” ACM Transactions on Architecture and Code Optimization (TACO) , vol. 15, no. 3, pp. 1–27, 2018
2018
Cited alongside, same era.
Z. Wang and M. O’Boyle, “Machine learning in compiler optimization,” Proceedings of the IEEE , vol. 106, no. 11, pp. 1879–1901, 2018
2018
Cited alongside, same era.
S. Ganser, A. Größlinger, N. Siegmund, S. Apel, and C. Lengauer, “Speeding up iterative polyhedral schedule optimization with surrogate performance models,” ACM Transactions on Architecture and Code Optimization (TACO) , vol. 15, no. 4, pp. 1–27, 2018
2018
Cited alongside, same era.
R. Baghdadi, J. Ray, M. B. Romdhane, E. D. Sozzo, A. Akkas, Y. Zhang, P. Suriana, S. Kamil, and S. Amarasinghe, “Tiramisu: A polyhedral compiler for expressing fast and portable code,” in 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) , 2019, pp. 193–205
2019
Cited alongside, same era.
A. Adams, K. Ma, L. Anderson, R. Baghdadi, T.-M. Li, M. Gharbi, B. Steiner, S. Johnson, K. Fatahalian, F. Durand et al. , “Learning to optimize halide with tree search and random programs,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 1–12, 2019
2019
Cited alongside, same era.
S. Kobeissi, A. Ketterlin, and P. Clauss, “Rec2poly: Converting recursions to polyhedral optimized loops using an inspector-executor strategy,” in International Conference on Embedded Computer Systems . Springer, 2020, pp. 96–109
2020
Cited alongside, same era.
A. Haj-Ali, N. K. Ahmed, T. Willke, Y. S. Shao, K. Asanovic, and I. Stoica, “Neurovectorizer: End-to-end vectorization with deep reinforcement learning,” in Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization , 2020, pp. 242–255
2020
Later among the works it cites.
C. Cummins, H. Leather, B. Steiner, H. He, and S. Chintala, “CompilerGym: A reinforcement learning toolkit for compilers,” https://github.com/facebookresearch/CompilerGym/ , 2020
2020
Later among the works it cites.
R. Mammadli, A. Jannesari, and F. Wolf, “Static neural compiler optimization via deep reinforcement learning,” in 2020 IEEE/ACM 6th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) and Workshop on Hierarchical Parallelism for Exascale Computing (HiPar) . IEEE, 2020, pp. 1–11
2020
Later among the works it cites.
A. Brauckmann, A. Goens, S. Ertel, and J. Castrillon, “Compiler-based graph representations for deep learning models of code,” in Proceedings of the 29th ACM SIGPLAN International Conference on Compiler Construction (CC 2020) , ser. CC 2020. New York, NY, USA: Association for Computing Machinery, Feb. 2020, p. 201–211. [Online]. Available: https://doi.org/10.1145/3377555.3377894
2020
Later among the works it cites.
G. Ye, Z. Tang, H. Wang, D. Fang, J. Fang, S. Huang, and Z. Wang, “Deep program structure modeling through multi-relational graph-based learning,” in Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques , 2020, pp. 111–123
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
Closest in time.
R. Baghdadi, M. Merouani, M.-H. Leghettas, K. Abdous, T. Arbaoui, K. Benatchba et al. , “A deep learning based cost model for automatic code optimization,” Proceedings of Machine Learning and Systems , vol. 3, 2021
2021
Closest in time.