Algorithmic differentiation (AD) is a mathematical concept which evolved over the last decades to a very robust and well understood tool for computation of derivatives. It can be applied to mathematical algorithms, co...
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Algorithmic differentiation (AD) is a mathematical concept which evolved over the last decades to a very robust and well understood tool for computation of derivatives. It can be applied to mathematical algorithms, codes for numerical simulation, and whenever derivatives are needed. In this paper we report on the algorithmic differentiation of the discontinuous Galerkin solver padge , a large and complex code written in C++ with underlying external libraries. The reports on successful application of AD to large scale codes are rare in literature and up to now this is not state of the art. Most of the codes, which are differentiated nowadays, are written in C or Fortran. The padge code was differentiated with the operator overloading tool dco / c ++ in forward as well as reverse mode. The differentiated code is validated and runs in the expected time margins of AD.
We present a generic approach to transforming one-sided MPI communication in the context of adjoint code. The adjoint allows us to compute gradients of multivariate function implementations at a computational cost tha...
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OpenMP and MPI are typically used in combination to implement distributed shared memory parallelization schemes for numerical simulations on hybrid systems. The performance of said numerical simulations is frequently ...
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This case study in Algorithmic Differentiation (AD) discusses the semi-automatic generation of an adjoint simulation code in the context of an inverse atmospheric remote sensing problem. In-depth structural and perfor...
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Algorithmic differentiation is a growing area of research that is concerned with the accuracy and efficient evaluation of derivatives for numerical functions defined by computer programs. In this paper, algorithmic di...
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Algorithmic differentiation is a growing area of research that is concerned with the accuracy and efficient evaluation of derivatives for numerical functions defined by computer programs. In this paper, algorithmic differentiation by overloading is introduced and applied to the linear solver F07AEF of the NAG library. As a case study and for the verification purpose, its result is used to compute the sensitivity of the Newton method in solving a nonlinear system of equation generated by a minimum problem. The function chosen for the case study is Extended Rosenbrock function.
An essential performance and correctness factor in numerical simulation and optimization is access to exact derivative information. Adjoint derivative models are particularly useful if a function’s number of inputs f...
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An essential performance and correctness factor in numerical simulation and optimization is access to exact derivative information. Adjoint derivative models are particularly useful if a function’s number of inputs far exceeds the number of outputs. The propagation of adjoints requires the data flow to be reversed, implying the reversal of all communication in programs that use message-passing. This paper presents recent advances made in developing the adjoint MPI library AMPI. The described proof of concept aims to serve as the basis for coupling other overloading AD tools with AMPI. We illustrate its use in the context of a specific overloading tool for algorithmic differentiation (AD) for C++ programs. A simplified but representative application problem is discussed as a case study.
Goal oriented dual weight error estimation has been used in context of computational fluid dynamics for several years. The adaptation of this method to geophysical models is the subject of this paper. A differentiatio...
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Goal oriented dual weight error estimation has been used in context of computational fluid dynamics for several years. The adaptation of this method to geophysical models is the subject of this paper. A differentiation-enabled prototype of the NAG Fortran compiler is used to generate a discrete adjoint version of such a geophysical model and allows to compute the required goal sensitivities. Numerical results are presented for a shallow water configuration of the Icosahedral Non-hydrostatic General Circulation Model (ICON). A special treatment of the underlying linear solver is discussed yielding improved scalability of this approach and a significant reduction in memory consumption and runtime.
Managing human factors in nondestructive evaluation is critical for maintaining inspection reliability. Reliability of structural health monitoring systems is particularly sensitive to sensor degradation over time. To...
Managing human factors in nondestructive evaluation is critical for maintaining inspection reliability. Reliability of structural health monitoring systems is particularly sensitive to sensor degradation over time. To investigate the impact of these issues, probabilistic models for risk assessment and cost‐benefit analysis tools have been developed. Quantitative studies are presented evaluating the effects of variations in probability of detection associated with human factors, plus in‐situ sensor degradation effects on life cycle measures such as cost and probability of failure.
We present a new experimental version of the differentiation-enabled NAGWare Fortran 95 compiler (from now on referred to as "the AD compiler") that provides support for the computation of adjoints in the re...
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ISBN:
(纸本)9783540284031
We present a new experimental version of the differentiation-enabled NAGWare Fortran 95 compiler (from now on referred to as "the AD compiler") that provides support for the computation of adjoints in the reverse mode of automatic differentiation (AD) [42, 136, 227]. Our implementation uses split program reversal [225, Chapter 10] in conjunction with a stack of gradients of all assignments executed inside the active section. Two papers describe the modifications of the compiler infrastructure that were required to provide forward-mode AD capabilities [126, 401]. The reverse mode presented in this paper makes extensive use of these features. Special emphasis is put on the presentation of the new user interface that provides a very easy and intuitive way for initiating derivative computations as well as for addressing the results. Various language extensions are introduced for this purpose. The compiler front-end is modified to accept these new constructs syntactically and semantically. The use of the language extensions triggers the automatic generation of derivative codes of various kinds by the compiler.
Prior work presented the development of a software platform for integrating NDI design and product life management tools to perform design tradeoffs in terms of cost and reliability. This work explores the development...
Prior work presented the development of a software platform for integrating NDI design and product life management tools to perform design tradeoffs in terms of cost and reliability. This work explores the development of probabilistic model components representing structural health monitoring systems, addressing the use of secondary NDE inspections and SHM system degradation. A discussion is presented concerning opportunities and pitfalls of SHM applications through both a qualitative survey and quantitative studies.
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