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AD in Fortran Part 1: Design
Radul, Alexey Andreyevich; Pearlmutter, Barak A.; Siskind, Jeffrey Mark
We propose extensions to FORTRAN which integrate forward and reverse Automatic Differentiation (AD) directly into the programming model. Irrespective of implementation technology, embedding AD constructs directly into the language extends the reach and convenience of AD while allowing abstraction of concepts of interest to scientific-computing practice, such as root finding, optimization, and finding equilibria of continuous games. Multiple different subprograms for these tasks can share common interfaces, regardless of whether and how they use AD internally. A programmer can maximize a function F by calling a library maximizer, XSTAR=ARGMAX(F, X0), which internally constructs derivatives of F by AD, without having to learn how to use any particular AD tool. We illustrate the utility of these extensions by example: programs become much more concise and closer to traditional mathematical notation. A companion paper describes how these extensions can be implemented by a program that generates input to existing FORTRANbased AD tools.
Keyword(s): Nesting; multiple transformation; forward mode; reverse mode; TAPENADE; ADIFOR; programming-language design
Publication Date:
Type: Report
Peer-Reviewed: No
Institution: Maynooth University
Citation(s): Radul, Alexey Andreyevich and Pearlmutter, Barak A. and Siskind, Jeffrey Mark (2012) AD in Fortran Part 1: Design. Working Paper. arXiv.
Publisher(s): arXiv
File Format(s): other
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First Indexed: 2020-04-02 06:43:18 Last Updated: 2020-04-02 06:43:18