We present a parallel data structure for the discretization of partial differential equations which is based on distributed point objects and which enables the flexible, transparent, and efficient realization of confo...
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We present a parallel data structure for the discretization of partial differential equations which is based on distributed point objects and which enables the flexible, transparent, and efficient realization of conforming, nonconforming, and mixed finite elements. This concept is realized for elliptic, parabolic and hyperbolic model problems, and sample applications are provided by a tutorial complementing a lecture on scientific computing. The corresponding open-source software is based on this parallel data structure, and it supports multilevel methods on nested meshes and 2D and 3D as well as in space-time. Here, we present generic results on porous media applications including multilevel preconditioning and multilevel Monte Carlo methods for uncertainty quantification. (C) 2020 Elsevier Ltd. All rights reserved.
We describe a new parallel method for solving global optimization problems. The formulation of the decision rules of this method is presented. We examine convergence conditions of the proposed algorithm and establish ...
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We describe a new parallel method for solving global optimization problems. The formulation of the decision rules of this method is presented. We examine convergence conditions of the proposed algorithm and establish conditions which guarantee a considerable speedup with respect to the sequential version of the algorithm. We also present some numerical experiments executed on Alliant FX/80 for one class of multiextremal functions.
A parallel algorithm for solving multiextremal multidimensional global optimization problems is proposed. The algorithm is based on reducing multidimensional problems to the one-dimensional ones by applying Peano-type...
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A parallel algorithm for solving multiextremal multidimensional global optimization problems is proposed. The algorithm is based on reducing multidimensional problems to the one-dimensional ones by applying Peano-type space-filling curves. A new parallel scheme to construct such curves is presented. For reduced optimization problems a parallel global optimization method is constructed. Sufficient conditions of global convergence are investigated. Conditions, which guarantee considerable speedup with respect to the sequential version of the algorithm, are established. numerical experiments executed on ALLIANT FX/80 are also presented.
This paper presents a straightforward approach to determining how best to utilize an MIMD multiprocessor in the solution of one-dimensional optimization problems involving continuous unimodal functions and nongradient...
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This paper presents a straightforward approach to determining how best to utilize an MIMD multiprocessor in the solution of one-dimensional optimization problems involving continuous unimodal functions and nongradient search techniques. A methodology is presented which allows one to consider a variety of speedup functions which may occur in parallel function and systems evaluation. It is shown how the best of two parallel optimization strategies can be determined for a given accuracy, number of processors, and speedup function.
This paper presents a performance comparison of three parallel algorithms for the solution of sets of coupled first-order differential equations. A general format for comparison of the algorithms is given, and perform...
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This paper presents a performance comparison of three parallel algorithms for the solution of sets of coupled first-order differential equations. A general format for comparison of the algorithms is given, and performance equations for the two processor cases are developed. The equations take into account both the computational and intercommunications requirements of the processors. These equations are applied to a benchmark of six problems and a simple, single bus multiprocessor architecture. The more than two processor case is considered for one of the parallel algorithms.
A performance model is constructed for parallel iterative numericalmethods under the assumption of a message-passing computing system. Arguments are given for the fact that the speedup of parallel iterative methods i...
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A performance model is constructed for parallel iterative numericalmethods under the assumption of a message-passing computing system. Arguments are given for the fact that the speedup of parallel iterative methods is mainly influenced by the speedup at one iterative step. Using the theoretical model, it is proved why explicit iterative methods for ordinary differential equations are inefficient in implementation on distributed memory multiprocessor systems. numerical tests on parallel and distributed computing environments confirm the correctness of the theoretical model at least in the case of iterative methods for ordinary differential equations and time-dependent partial differential equations. (c) 2005 Elsevier Ltd. All rights reserved.
An interior point method for block angular optimization is developed and the convergence properties of the method are described. A major motivation for such a method is that most of the computation is easily paralleli...
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An interior point method for block angular optimization is developed and the convergence properties of the method are described. A major motivation for such a method is that most of the computation is easily parallelized. Computational results are presented for a class of large-scale linear programming models. These models are multicommodity flow problems that arise from an Air Force (Military Airlift Command) application and generate problems as large as 100,000 rows and 300,000 columns.
Dynamical technical networks and dynamic network objects (DNO) as a special case of them are considered in many subject areas as objects of research, design, quality assurance, automatization, monitoring and control. ...
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ISBN:
(纸本)9781538606971
Dynamical technical networks and dynamic network objects (DNO) as a special case of them are considered in many subject areas as objects of research, design, quality assurance, automatization, monitoring and control. In this paper we focus on the simulation of the mine air networks (MAN) as an example of dynamic network objects with distributed parameters. To improve the simulation process, we propose to use new type of numericalmethods-block difference methods (BDM). Because of the especial numerical structure these methods promise a possibility to optimize the simulation process to the parallel computing architectures. We introduce the first experimental results with a sequential version of the BDM-solvers and make a conclusion about the advisability of further steps in the direction of parallelization.
This paper presents the systolic implementation of Huang's direct method for the general solution of a system of linear equations. The method is extended to the solution of general matrix equations and generalised...
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This paper deals with a new parallel method for solving one-dimensional global optimization problems. We present the formulation of the decision rules of this method, the conditions for its nonredundant parallelizatio...
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