Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36024-4_37
Reference11 articles.
1. Geoga, C.J., Marin, O., Schanen, M., Stein, M.L.: Fitting matérn smoothness parameters using automatic differentiation. Stat. Comput. 33(2), 48 (2023). https://doi.org/10.1007/s11222-022-10127-w
2. Griewank, A., Walther, A.: Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation. ACM Trans. Math. Softw. 26(1), 19–45 (2000). https://doi.org/10.1145/347837.347846
3. Griewank, A., Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. 2nd edn. No. 105 in Other Titles in Applied Mathematics. SIAM, Philadelphia (2008). http://bookstore.siam.org/ot105/
4. Innes, M.: Don’t unroll adjoint: differentiating SSA-form programs (2018). https://doi.org/10.48550/ARXIV.1810.07951
5. Moses, W.S., et al.: Scalable automatic differentiation of multiple parallel paradigms through compiler augmentation. In: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–18. IEEE Computer Society, Los Alamitos (2022). https://doi.org/10.1109/SC41404.2022.00065
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