Reducing the memory requirement in reverse mode automatic differentiation by solving TBR flow equations. Metadata
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- Main Title Reducing the memory requirement in reverse mode automatic differentiation by solving TBR flow equations.
Author: Naumann, U.Creator Type: Personal
Sponsor: United States. Department of Energy.Contributor Type: OrganizationContributor Info: US Department of Energy (United States)
Name: Argonne National LaboratoryPlace of Publication: IllinoisAdditional Info: Argonne National Lab., IL (United States)
- Creation: 2002-01-11
- Content Description: The fast computation of gradients in reverse mode Automatic Differentiation (AD) requires the generation of adjoint versions of every statement in the original code. Due to the resulting reversal of the control flow certain intermediate values have to be made available in reverse order to compute the local partial derivatives. This can be achieved by storing these values or by recomputing them when they become required. In any case one is interested in minimizing the size of this set. Following an extensive introduction of the ''To-Be-Recorded'' (TBR) problem the authors present flow equations for propagating the TBR status of variables in the context of reverse mode AD of structured programs.
- Physical Description: 10 pages
- Keyword: Partial Differential Equations
- Keyword: Flow Models
- Keyword: Computer Calculations
- STI Subject Categories: 99 General And Miscellaneous//Mathematics, Computing, And Information Science
- Conference: 2002 International Conference on Computational Science (ICCS 2002), Amsterdam (NL), 04/21/2002--04/24/2002
Name: Office of Scientific & Technical Information Technical ReportsCode: OSTI
Name: UNT Libraries Government Documents DepartmentCode: UNTGD
- Report No.: ANL/MCS/CP-106609
- Grant Number: W-31-109-ENG-38
- Office of Scientific & Technical Information Report Number: 793901
- Archival Resource Key: ark:/67531/metadc741615