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6th World Congress on Global Optimization, WCGO 2019

作者： Deussen, Jens Naumann, Uwe Software and Tools for Computational Engineering RWTH Aachen University Aachen Germany

ISBN: (纸本)9783030218027

We describe how to deploy interval derivatives up to second order in the context of unconstrained global optimization with a branch and bound method. For computing these derivatives we combine the Boost interval library and the algorithmic differentiation tool dco/c++. The differentiation tool also computes the required floating-point derivatives for a local search algorithm that is embedded in our branch and bound implementation. First results are promising in terms of utility of interval adjoints in global optimization. © 2020, Springer Nature Switzerland AG.

关键词： Global optimization

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Parallel Adjoint Taping Using MPI 23rd

Parallel Adjoint Taping Using MPI

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23rd International Conference on computational Science, ICCS 2023

作者： Towara, Markus Kremer, Jannick Naumann, Uwe Software and Tools for Computational Engineering RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031359941

The adjoint reversal of long evolutionary calculations (e.g. loops), where each iteration depends on the output of the previous iteration, is a common occurrence in computational engineering (e.g. computational fluid dynamics (CFD) simulation), physics (e.g. molecular dynamics) and computational finance (e.g. long Monte Carlo paths). For the edge case of a scalar state, the execution, as well as adjoint control flow reversal, are inherently serial operations, as there is no spatial dimension to parallelize. Our proposed method exploits the run time difference between passive function evaluation and augmented forward evaluation, which is inherent to most adjoint AD techniques. For high dimensional states, additional parallelization of the primal computation can and should be exploited at the spatial level. Still, for problem sizes where the parallelization of the primal has reached the barrier of scalability, the proposed method can be used to better utilize available computing resources and improve the efficiency of adjoint reversal. We expect this method to be especially useful for operator-overloading AD tools. However, the concepts are also applicable to source-to-source transformation and handwritten adjoints, or a hybrid of all approaches. For illustration, C++ reference implementations of a low dimensional evolution (lorenz attractor) and a high dimensional evolution (computational fluid dynamics problem in OpenFOAM) are discussed. Both theoretical bounds on the speedup and run time measurements are presented. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Molecular dynamics

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A wish list for efficient adjoints of one-sided MPI communication

A wish list for efficient adjoints of one-sided MPI communic...

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19th European MPI Users' Group Meeting on Recent Advances in the Message Passing Interface, EuroMPI 2012

作者： Schanen, Michel Naumann, Uwe LuFG Informatik 12: Software and Tools for Computational Engineering RWTH Aachen University Germany

ISBN: (纸本)9783642335174

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 that is independent of the size of these gradients. In this paper, we apply the adjoint model to codes containing one-side MPI communication. The Partitioned Global Address Space notation is used to derive the corresponding adjoint code. Our adjoint solutions rely on certain workarounds due to limited adjoint support in the current MPI standard. To avoid these, we provide a wish list for future MPI standards that potentially lead to a more efficient adjoint communication. © 2012 Springer-Verlag.

关键词： Codes (symbols)

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Development of a discrete adjoint CFD code using algorithmic differentiation by operator overloading 1

Development of a discrete adjoint CFD code using algorithmic...

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1st International Conference on engineering and Applied Sciences Optimization, OPT-i 2014

作者： Dastouri, Z. Lotz, J. Naumann, U. RWTH Aachen University Software and Tools for Computational Engineering AachenD-52062 Germany

ISBN: (纸本)9789609999465

Design optimization for fluid flow recently has drawn a great deal of attention in the industrial design area including aeronautic[1, 2], turbo-machinery[3] and automotive design[4, 5]. The optimization problem is subject to a large number of design variables that makes calculating the sensitivity derivatives by finite differences a costly procedure. Algorith- mic differentiation(AD) is a well known method to differentiate the computer program with the aim of obtaining the sensitivity of the objective with respect to design variables. There are two different AD approaches, operator overloading and source transformation. In this paper we describe a design framework for application of the algorithmic differentiation tool by operator overloading in Fortran dco/fortran1 to CFD analysis solver called GPDE2. GPDE is an un- structured pressure-based steady Navier-Stokes code with finite volume spatial discretization, which is based on the SIMPLE pressure-correction scheme for the incompressible viscous flow computation. This approach yields a discrete tangent-linear and adjoint version of the CFD code. Moreover, we address typical implementation issues and complexities in the differentia- tion procedure by AD tool in the CFD code. The numerical results of a relevant test case by our overloading tool in forward and reverse mode are compared with their corresponding results of the previously differentiated code by source transformation that is generated by TAPENADE. We show that in terms of ease of implementation and ability to handle arbitrary functions, dco/- fortran provides the differentiated code with a greater flexibility and robustness in comparison with AD by source transformation. This research is aimed toward the application of AD tools by operator overloading on a legacy industrial incompressible flow solver that is in our context ESI? ACE+.

关键词： Sensitivity analysis

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Adjoining hybrid parallel code

Adjoining hybrid parallel code

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8th International Conference on engineering computational Technology, ECT 2012

作者： Schanen, M. Foerster, M. Lotz, J. Leppkes, K. Naumann, U. Software and Tools for Computational Engineering Dept. LuFG Informatik 12 RWTH Aachen University Germany

ISBN: (纸本)9781905088553

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 linked to the efficient and accurate computation of derivatives. One method of acquiring this derivative information for a given code is algorithmic differentiation (AD). AD implementations are subdivided into either source transformation or overloading tools. We investigate a generic approach of applying AD tools on hybrid parallel codes. A dense matrixmatrix multiplication serves as a case study. © Civil-Comp Press, 2012.

关键词： Hybrid systems

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Higher-order discrete adjoint ODE Solver in C++ for dynamic optimization

Higher-order discrete adjoint ODE Solver in C++ for dynamic ...

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15th Annual International Conference on computational Science, ICCS 2015

作者： Lotz, Johannes Naumann, Uwe Hannemann-Tamás, Ralf Ploch, Tobias Mitsos, Alexander RWTH Aachen Software and Tools for Computational Engineering Aachen Germany RWTH Aachen Process Systems Engineering Aachen Germany

Parametric ordinary differential equations (ODE) arise in many engineering applications. We consider ODE solutions to be embedded in an overall objective function which is to be minimized, e.g. for parameter estimation. For derivative-based optimization algorithms adjoint methods should be used. In this article, we present a discrete adjoint ODE integration framework written in C++ (NIXE 2.0) combined with Algorithmic Differentiation by overloading (dco/c++). All required derivatives, i.e. Jacobians for the integration as well as gradients and Hessians for the optimization, are generated automatically. With this framework, derivatives of arbitrary order can be implemented with minimal programming effort. The practicability of this approach is demonstrated in a dynamic parameter estimation case study for a batch fermentation process using sequential method of dynamic optimization. Ipopt is used as the optimizer which requires second derivatives. © The Authors. Published by Elsevier B.V.

关键词： Ordinary differential equations

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Towards tangent-linear GPU programs using OpenACC

Towards tangent-linear GPU programs using OpenACC

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4th Symposium on Information and Communication Technology, SoICT 2013

作者： Minh, Bui Tat Förster, Michael Naumann, Uwe Bangkok Thailand LuFG Informatik 12: Software and Tools for Computational Engineering RWTH Aachen University Aachen Germany

ISBN: (纸本)9781450324540

Recently, Graphics Processing Units(GPUs) have emerged as a very promisingly powerful resource in scientific computing. Algorithmic Differentiation is a technique to numerically evaluate first and higher derivatives of a function specified by a computer program efficiently up to machine precision. Derivative programs which are used to compute derivatives of functions are so-called tangent-linear program and adjoint program. This paper aims to offload any particular independent loop in tangent-linear program to GPUs. The proposed technique is OpenACC APIs for annotating an independent loop to be executed in parallel on GPUs. Our case study for OpenACC tangent-linear code shows an enormous speedup. OpenACC shows its simplicity of accelerating tangent-linear code by hiding the data movement between CPU and GPU memory. Copyright © 2013 ACM.

关键词： Program processors

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Discrete adjoints of PETSc through dco/c++ and adjoint MPI

Discrete adjoints of PETSc through dco/c++ and adjoint MPI

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19th International Conference on Parallel Processing, Euro-Par 2013

作者： Lotz, Johannes Naumann, Uwe Sagebaum, Max Schanen, Michel LuFG Informatik 12: Software and Tools for Computational Engineering RWTH Aachen University Germany Department of Mathematics Center for Computational Engineering Science RWTH Aachen University Germany

ISBN: (纸本)9783642400469

PETSc's [1] robustness, scalability and portability makes it the foundation of various parallel implementations of numerical simulation codes. We formulate a least squares problem using a PETSc implementation as the model function and rely on adjoint mode Algorithmic Differentiation (AD) [2] for the accumulation of the derivative information. Various AD tools exist that apply the adjoint model to a given C/C++ code, while none is able to differentiate MPI [3] enabled code. We solved this by combining dco/c++ and the Adjoint MPI library, leading to a fully discrete adjoint implementation of PETSc. We want to underline that this work differs from accumulating derivative information through AD for PETSc algorithms (see e.g. [4]). We compute derivative information of PETSc itself opening up the possibility of an enclosing optimization problem (as needed, e.g., by [5]). © 2013 Springer-Verlag.

关键词： Codes (symbols)

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MPI-Parallel Discrete Adjoint OpenFOAM

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Procedia Computer Science 2015年 51卷 19-28页

作者： Markus Towara Michel Schanen Uwe Naumann Software and Tools for Computational Engineering RWTH Aachen University Aachen Germany

OpenFOAM is a powerful Open-Source (GPLv3) computational Fluid Dynamics tool box with a rising adoption in both academia and industry due to its continuously growing set of features and the lack of license costs. Our previously developed discrete adjoint version of OpenFOAM allows us to calculate derivatives of arbitrary objectives with respect to a potentially very large number of input parameters at a relative (to a single primal flow simulation) computational cost which is independent of that number. Discrete adjoint OpenFOAM enables us to run gradient-based methods such as topology optimization efficiently. Up until recently only a serial version was available limiting both the computing performance and the amount of memory available for the solution of the problem. In this paper we describe a first parallel version of discrete adjoint OpenFOAM based on our adjoint MPI library.

关键词： OpenFOAM Adjoints MPI Algorithmic Differentiation

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Evaluating Energy Efficiency of Buildings using Artificial Neural Networks and K-means Clustering Techniques 3

Evaluating Energy Efficiency of Buildings using Artificial N...

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3rd International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2020

作者： Nazir, Ahsan Wajahat, Ahsan Akhtar, Faheem Ullah, Faheem Qureshi, Sirajuddin Malik, Sher Afghan Shakeel, Anum Beijing University of Technology Beijing School of Software Engineering China RWTH Aachen University Software Tools for Computational Engineering Germany Sukkur IBA University Department of Computer Science Sukkur65200 Pakistan

ISBN: (纸本)9781728149707

The consumption of energy in buildings has risen abruptly over the last decades. Due to less energy-efficient buildings, most of the energy is being thrown in our surroundings thus making an adverse effect on our environment. In this paper, heating and cooling loads of private or non-commercial buildings are covered. By implementing the proposed technique, which is a blend of cluster analysis and Artificial Neural Network (ANN), evaluation and prediction are performed. The estimation of heating and cooling loads of private or non-commercial buildings are performed using eight input variables in the ANN-based model. The details of variables are as follows, a relative surface area, total height, compactness, roof area, glazing area distribution, orientation, glazing area, and wall area. K-means clustering methodology is then used to cluster buildings on the basis of output variables. Stand on simulated literature data, evaluation of 768 different private or non-commercial buildings is done using the above-suggested method. Research results depicted that depending upon input variables, the above-suggested approach can efficiently evaluate heating and cooling load that is very much close to real test results. © 2020 IEEE.

关键词： Cluster analysis

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