Abstract
Recomputation and storing are typically seen as tradeoffs for checkpointing schemes in the context of adjoint computations. At finer granularity during the adjoint sweep, in practice, only the store-all or recompute-all approaches are fully automated. This paper considers a heuristic approach for exploiting finer granularity recomputations to reduce the storage requirements and thereby improve the overall adjoint efficiency without the need for manual intervention.
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References
Bischof, C.H., Bücker, H.M., Hovland, P.D., Naumann, U., Utke, J. (eds.): Advances in Automatic Differentiation, Lecture Notes in Computational Science and Engineering, vol. 64. Springer, Berlin (2008). DOI 10.1007/978-3-540-68942-3
Bücker, H.M., Corliss, G.F., Hovland, P.D., Naumann, U., Norris, B. (eds.): Automatic Differentiation: Applications, Theory, and Implementations, Lecture Notes in Computational Science and Engineering, vol. 50. Springer, New York, NY (2005). DOI 10.1007/ 3-540-28438-9
Giering, R., Kaminski, T.: Recomputations in reverse mode AD. In: G. Corliss, C. Faure, A. Griewank, L. Hascoët, U. Naumann (eds.) Automatic Differentiation: From Simulation to Optimization, Computer and Information Science, chap. 33, pp. 283–291. Springer, New York (2002). URL http://www.springer.de/cgi-bin/search_book.pl?isbn=0-387-95305-1
Giering, R., Kaminski, T.: Applying TAF to generate efficient derivative code of Fortran 77-95 programs. Proceedings in Applied Mathematics and Mechanics 2(1), 54–57 (2003). URL http://www3.interscience.wiley.com/cgi-bin/issuetoc?ID=104084257
Griewank, A., Walther, A.: Algorithm 799: Revolve: An implementation of checkpoint for the reverse or adjoint mode of computational differentiation. ACM Transactions on Mathematical Software 26(1), 19–45 (2000). URL http://doi.acm.org/10.1145/347837.347846. Also appeared as Technical University of Dresden, Technical Report IOKOMO-04-1997.
Griewank, A., Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, 2nd edn. No. 105 in Other Titles in Applied Mathematics. SIAM, Philadelphia, PA (2008). URL http://www.ec-securehost.com/SIAM/OT105.html
Hascoët, L., Araya-Polo, M.: The adjoint data-flow analyses: Formalization, properties, and applications. In: Bücker et al. [2], pp. 135–146. DOI 10.1007/3-540-28438-9{ _}12
Hascoët, L., Naumann, U., Pascual, V.: “To be recorded” analysis in reverse-mode automatic differentiation. Future Generation Computer Systems 21(8), 1401–1417 (2005). DOI 10.1016/j.future.2004.11.009
Lyons, A., Utke, J.: On the practical exploitation of scarsity. In: Bischof et al. [1], pp. 103–114. DOI 10.1007/978-3-540-68942-3_10
Utke, J.: Flattening basic blocks. In: Bücker et al. [2], pp. 121–133. DOI 10.1007/3-540-28438-9{ _}11
Utke, J., Naumann, U., Fagan, M., Tallent, N., Strout, M., Heimbach, P., Hill, C., Wunsch, C.: OpenAD/F: A modular, open-source tool for automatic differentiation of Fortran codes. ACM Transactions on Mathematical Software 34(4), 18:1–18:36 (2008). DOI 10.1145/1377596. 1377598
Acknowledgements
This work was supported by the U.S. Department of Energy, under contract DE-AC02-06CH11357.
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Cole-Mullen, H., Lyons, A., Utke, J. (2012). Storing Versus Recomputation on Multiple DAGs. In: Forth, S., Hovland, P., Phipps, E., Utke, J., Walther, A. (eds) Recent Advances in Algorithmic Differentiation. Lecture Notes in Computational Science and Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30023-3_18
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DOI: https://doi.org/10.1007/978-3-642-30023-3_18
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