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Toward differentiation-enabled Fortran 95 compiler technology (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Naumann, Uwe Aachen, Rwth |
| Description | The availability of first derivatives of vector functions is crucial for the robustness and efficiency of a large number of numerical algorithms. An upcoming new version of the differentiation-enabled NAGWare Fortran 95 compiler is described that uses programming language extensions and a semantic code transformation known as automatic differentiation to provide Jacobians of numerical programs with machine accuracy. We describe a new user interface as well as the relevant algorithmic details. In particular, we focus on the source transformation approach that generates locally optimal gradient code for single assignments by vertex elimination in the linearized computational graph. Extensive tests show the superiority of this method over the current overloading-based approach. The robustness and convenience of the new compiler-feature is illustrated by various case studies. In Proceedings of the 2003 ACM symposium on Applied computing |
| File Format | |
| Language | English |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Vertex Elimination Linearized Computational Graph Language Extension Upcoming New Version First Derivative Relevant Algorithmic Detail Numerical Program Compiler Technology Machine Accuracy Various Case Study Current Overloading-based Approach Toward Differentiation-enabled Fortran New Compiler-feature Automatic Differentiation Vector Function Source Transformation Approach Numerical Algorithm Semantic Code Transformation New User Interface Large Number Extensive Test Optimal Gradient Code Differentiation-enabled Nagware Fortran Single Assignment |
| Content Type | Text |
| Resource Type | Article |
