Source transformation

Abstract: The numerical methods employed in the solution of many scientific computing problems require the computation of derivatives of a function f $R^N$→$R^m$ Both the accuracy and the computational requirements of the derivative computation are usually of critical importance for the robustness and speed of the numerical solution Automatic Differentiation of FORtran (ADIFOR) is a source transformation tool that accepts Fortran 77 code for the computation of a function and writes portable Fortran 77 code for the computation of the derivatives In contrast to previous approaches, ADIFOR views automatic differentiation as a source transformation problem ADIFOR employs the data analysis capabilities of the ParaScope Parallel Programming Environment, which enable us to handle arbitrary Fortran 77 codes and to exploit the computational context in the computation of derivatives Experimental results show that ADIFOR can handle real-life codes and that ADIFOR-generated codes are competitive with divided-difference approximations of derivatives In addition, studies suggest that the source transformation approach to automatic differentiation may improve the time to compute derivatives by orders of magnitude

Abstract: The use of source-to-source program transformations has proved valuable in improving program performance. The concept of program manipulation is elucidated by describing its role in both conventional optimization and high level modification of conditional, looping, and procedure structures. An example program fragment written in an Algol-like language is greatly improved by transformations enabled by a user-provided assertion about a data array. A compilation model based on the use of source-to-source program transformations is used to provide a framework for discussing issues of code generation, compilation of high level languages such as APL, and eliminating overhead commonly associated with modular structured programming. Application of the compilation model to several different languages is discussed.

Abstract: The Open/ADF tool allows the evaluation of derivatives of functions defined by a Fortran program. The derivative evaluation is performed by a Fortran code resulting from the analysis and transformation of the original program that defines the function of interest. Open/ADF has been designed with a particular emphasis on modularity, flexibility, and the use of open source components. While the code transformation follows the basic principles of automatic differentiation, the tool implements new algorithmic approaches at various levels, for example, for basic block preaccumulation and call graph reversal. Unlike most other automatic differentiation tools, Open/ADF uses components provided by the Open/AD framework, which supports a comparatively easy extension of the code transformations in a language-independent fashion. It uses code analysis results implemented in the OpenAnalysis component. The interface to the language-independent transformation engine is an XML-based format, specified through an XML schema. The implemented transformation algorithms allow efficient derivative computations using locally optimized cross-country sequences of vertex, edge, and face elimination steps. Specifically, for the generation of adjoint codes, Open/ADF supports various code reversal schemes with hierarchical checkpointing at the subroutine level. As an example from geophysical fluid dynamics, a nonlinear time-dependent scalable, yet simple, barotropic ocean model is considered. OpenAD/F's reverse mode is applied to compute sensitivities of some of the model's transport properties with respect to gridded fields such as bottom topography as independent (control) variables.

Abstract: Many tasks in software engineering can be characterized as source to source transformations. Design recovery, software restructuring, forward engineering, language translation, platform migration, and code reuse can all be understood as transformations from one source text to another. The tree transformation language, TXL, is a programming language and rapid prototyping system specifically designed to support rule-based source to source transformation. Originally conceived as a tool for exploring programming language dialects, TXL has evolved into a general purpose source transformation system that has proven well suited to a wide range of software maintenance and reengineering tasks, including the design recovery, analysis and automated reprogramming of billions of lines of commercial Cobol, PL/I, and RPG code for the Year 2000. In this paper, we introduce the basic features of modern TXL and its use in a range of software engineering applications, with an emphasis on how each task can be achieved by source transformation.

Abstract: Derivatives of mathematical functions play a key role in various areas of numerical and technical computing. Many of these computations are done in MATLAB, a popular environment for technical computing providing engineers and scientists with capabilities for mathematical computing, analysis, visualization, and algorithmic development. For functions written in the MATLAB language, a novel software tool is proposed to automatically transform a given MATLAB program into another MATLAB program capable of computing not only the original function but also user-specified derivatives of that function. That is, a program transformation known as automatic differentiation is performed to change the semantics of the program in a fashion based on the chain rule of differential calculus. The crucial ingredient of the tool is a combination of source-to-source transformation and operator overloading. The overall design of the tool is described and numerical experiments are reported demonstrating the efficiency of the resulting code for a sample problem.