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59
ADIC: An Extensible Automatic Differentiation Tool for ANSIC
, 1997
"... . In scientific computing, we often require the derivatives @f=@x of a function f expressed as a program with respect to some input parameter(s) x, say. Automatic differentiation (AD) techniques augment the program with derivative computation by applying the chain rule of calculus to elementary oper ..."
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Cited by 76 (14 self)
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. In scientific computing, we often require the derivatives @f=@x of a function f expressed as a program with respect to some input parameter(s) x, say. Automatic differentiation (AD) techniques augment the program with derivative computation by applying the chain rule of calculus to elementary operations in an automated fashion. This article introduces ADIC (Automatic Differentiation of C), a new AD tool for ANSIC programs. ADIC is currently the only tool for ANSIC that employs a sourcetosource program transformation approach; that is, it takes a C code and produces a new C code that computes the original results as well as the derivatives. We first present ADIC "by example" to illustrate the functionality and ease of use of ADIC and then describe in detail the architecture of ADIC. ADIC incorporates a modular design that provides a foundation for both rapid prototyping of better AD algorithms and their sharing across AD tools for different languages. A component architecture call...
What color is your Jacobian? Graph coloring for computing derivatives
 SIAM REV
, 2005
"... Graph coloring has been employed since the 1980s to efficiently compute sparse Jacobian and Hessian matrices using either finite differences or automatic differentiation. Several coloring problems occur in this context, depending on whether the matrix is a Jacobian or a Hessian, and on the specific ..."
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Cited by 41 (7 self)
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Graph coloring has been employed since the 1980s to efficiently compute sparse Jacobian and Hessian matrices using either finite differences or automatic differentiation. Several coloring problems occur in this context, depending on whether the matrix is a Jacobian or a Hessian, and on the specifics of the computational techniques employed. We consider eight variant vertexcoloring problems here. This article begins with a gentle introduction to the problem of computing a sparse Jacobian, followed by an overview of the historical development of the research area. Then we present a unifying framework for the graph models of the variant matrixestimation problems. The framework is based upon the viewpoint that a partition of a matrixinto structurally orthogonal groups of columns corresponds to distance2 coloring an appropriate graph representation. The unified framework helps integrate earlier work and leads to fresh insights; enables the design of more efficient algorithms for many problems; leads to new algorithms for others; and eases the task of building graph models for new problems. We report computational results on two of the coloring problems to support our claims. Most of the methods for these problems treat a column or a row of a matrixas an atomic entity, and partition the columns or rows (or both). A brief review of methods that do not fit these criteria is provided. We also discuss results in discrete mathematics and theoretical computer science that intersect with the topics considered here.
Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems
, 2002
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Multidisciplinary Design Optimization Techniques: Implications and Opportunities for Fluid Dynamics Research
 JAROSLAW SOBIESZCZANSKISOBIESKI AND RAPHAEL T. HAFTKA ā€¯MULTIDISCIPLINARY AEROSPACE DESIGN OPTIMIZATION: SURVEY OF RECENT DEVELOPMENTS,ā€¯ 34TH AIAA AEROSPACE SCIENCES MEETING AND EXHIBIT
, 1999
"... A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the de ..."
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Cited by 20 (0 self)
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A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.
On the Future of Problem Solving Environments

, 2000
"... In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sit ..."
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Cited by 16 (2 self)
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In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The stateoftheart is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research
Preliminary results from the application of automated code generation to CFL3D
 Proceedings, 12th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and
, 1998
"... This report describes preliminary results obtained using an automated adjoint code generator for Fortran to augment a widelyused computational fluid dynamics flow solver to compute derivatives. These preliminary results with this augmented code suggest that, even in its infancy, the automated adjoi ..."
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Cited by 16 (2 self)
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This report describes preliminary results obtained using an automated adjoint code generator for Fortran to augment a widelyused computational fluid dynamics flow solver to compute derivatives. These preliminary results with this augmented code suggest that, even in its infancy, the automated adjoint code generator can accurately and efficiently deliver derivatives for use in transonic Eulerbased aerodynamic shape optimization problems with hundreds to thousands of independent design variables.
ADIFOR 2.0 user's guide (Revision D)
 TECHNICAL MEMORANDUM ANL/MCSTM192, MATHEMATICS AND COMPUTER SCIENCE DIVISION, ARGONNE NATIONAL LABORATORY
, 1998
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On the implementation of automatic differentiation tools
 HigherOrder and Symbolic Computation
, 2004
"... Abstract. Automatic differentiation is a semantic transformation that applies the rules of differential calculus to source code. It thus transforms a computer program that computes a mathematical function into a program that computes the function and its derivatives. Derivatives play an important ro ..."
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Cited by 13 (2 self)
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Abstract. Automatic differentiation is a semantic transformation that applies the rules of differential calculus to source code. It thus transforms a computer program that computes a mathematical function into a program that computes the function and its derivatives. Derivatives play an important role in a wide variety of scientific computing applications, including numerical optimization, solution of nonlinear equations, sensitivity analysis, and nonlinear inverse problems. We describe the forward and reverse modes of automatic differentiation and provide a survey of implementation strategies. We describe some of the challenges in the implementation of automatic differentiation tools, with a focus on tools based on source transformation. We conclude with an overview of current research and future opportunities.
Dataflow analysis for MPI programs
 in Proceedings of the International Conference on Parallel Processing
"... Message passing via MPI is widely used in singleprogram, multipledata (SPMD) parallel programs. Dataflow analysis frameworks that respect the semantics of messagepassing SPMD programs are needed to obtain more accurate and in some cases correct analysis results for such programs. We qualitativel ..."
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Cited by 13 (0 self)
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Message passing via MPI is widely used in singleprogram, multipledata (SPMD) parallel programs. Dataflow analysis frameworks that respect the semantics of messagepassing SPMD programs are needed to obtain more accurate and in some cases correct analysis results for such programs. We qualitatively evaluate various approaches for performing dataflow analysis on SPMD MPI programs and present a method for performing interprocedural dataflow analysis on the MPIICFG representation. The MPIICFG is an interprocedural controlflow graph (ICFG) augmented with communication edges between possible send and receive pairs. We discuss in detail two analyses that potentially benefit from propagating information over communication edges: reaching constants and activity analysis. Constants can be shared in SPMD programs without communicating them; therefore, performing reaching constants over the MPIICFG is useful mainly for illustrative purposes. Activity analysis is a domainspecific analysis used to reduce the computation and storage requirements for automatically differentiated MPI programs. Our experimental results show that activity analysis performed over the MPIICFG has a convergence rate comparable to a more conservative version of the analysis performed on an ICFG. Also, using the MPIICFG dataflow analysis framework improves the precision of activity analysis and significantly reduces memory requirements for the automatically differentiated versions of some parallel benchmarks, including some of the NAS Parallel
Algorithms and Design for a SecondOrder Automatic Differentation Module
, 1997
"... This paper describes approaches to computing secondorder derivatives with automatic differentiation (AD) based on the forward mode and the propagation of univariate Taylor series. Performance results are given which show the speedup possible with these techniques. We also describe a new source tran ..."
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Cited by 12 (5 self)
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This paper describes approaches to computing secondorder derivatives with automatic differentiation (AD) based on the forward mode and the propagation of univariate Taylor series. Performance results are given which show the speedup possible with these techniques. We also describe a new source transformation AD module for computing secondorder derivatives of C and Fortran codes and the underlying infrastructure used to create a languageindependent translation tool.