Purpose - The paper aim is the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of Multistage D...
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Purpose - The paper aim is the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of Multistage Depressed Collector.
Design/methodology/approach - The Flock-of-Starlings Optimization (FSO), the Particle Swarm Optimization (PSO) and the Bacterial Chemotaxis Algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search.
Findings - The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort.
Practical implications - The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm.
Originality/value - The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.
This paper presents some practical ways of using polynomial preconditions for solving large sparse linear systems of equations issued from discretizations of partial differential equations. For a symmetric positive de...
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This paper presents some practical ways of using polynomial preconditions for solving large sparse linear systems of equations issued from discretizations of partial differential equations. For a symmetric positive definite matrix A these techniques are based on least squares polynomials on the interval [0,b]
In this paper, we propose and analyze the parallel Robin-Robin domain decomposition method based on the modified characteristic finite element method for the time-dependent dual-porosity-Navier-Stokes model with the B...
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In this paper, we propose and analyze the parallel Robin-Robin domain decomposition method based on the modified characteristic finite element method for the time-dependent dual-porosity-Navier-Stokes model with the Beavers-Joseph interface condition. For the coupling terms, we treat them in an explicit manner which takes advantage of information obtained in previous time steps to construct a non-iteration domain decomposition method. By this means, two single dual-porosity equations and a single Navier-Stokes equation are needed to solve at each time. In particular, we solve the Navier-Stokes equation by the modified characteristic finite element method, which avoids the computational inefficiency caused by the nonlinear convection term. Furthermore, we prove the error convergence of solutions by mathematical induction, whose proof implies the uniform L-infinity-boundedness of the fully discrete velocity solution in conduit flow. Finally, some numerical examples are presented to show the effectiveness and efficiency of the proposed method.
A parallel iterative layered-medium integral-equation solver is presented for fast and scalable network parameter extraction of electronic packages. The solver, which relies on a 2-D fast Fourier transform (FFT)-based...
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A parallel iterative layered-medium integral-equation solver is presented for fast and scalable network parameter extraction of electronic packages. The solver, which relies on a 2-D fast Fourier transform (FFT)-based algorithm and a sparse preconditioner to reduce computational complexity, is parallelized using three workload decomposition strategies, including a pencil decomposition that increases the scalability of the computationally dominant FFT-based multiplication stage. A set of increasingly difficult benchmark problems, which require network parameter computations for N-trace = 1 to 257 package-scale interconnects, are solved on a petaflop scale computer to quantify the solver's accuracy, efficiency, and scalability. The total serialized computation time is observed to scale asymptotically as Ntrace2.6logNtrace. For the largest problem, using similar to 1.14 million unknowns and 1536 processes, the solver requires a wall-clock time of similar to 0.05 s per iteration, similar to 1 minute per excitation, similar to 9 h per frequency, and similar to 424 hours to extract the 514-port network parameters at 40 sample frequencies between 1 to 40 GHz.
In this paper a parallel implementation of an Adaptive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallel...
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In this paper a parallel implementation of an Adaptive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here. Also, since a matrix inversion operation is required in the GPC predictor algorithm, special attention is given to its parallelization. A small DSP network with up to 3 processors is used to investigate, the performance of the parallel implementation. To exploit an heterogeneous architecture the parallel algorithm is mapped over a network builded up of transputers as communication elements, and DSPs as computing elements. Further some heterogeneous topologies are compared. Execution times and efficiency results of the RLS and GPC steps are presented to show the performance of the parallel algorithm, over different topologies.
作者：
BARON, INYU
COURANT INST MATH SCINEW YORKNY 10012
We give a practical parallel algorithm for solving band symmetric positive definite systems of linear equations in O(m* log n) time using nm/log n processors. Here n denotes the system size and m its bandwidth. Hence,...
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We give a practical parallel algorithm for solving band symmetric positive definite systems of linear equations in O(m* log n) time using nm/log n processors. Here n denotes the system size and m its bandwidth. Hence, the algorithm is efficient. For tridiagonal systems, the algorithm runs in O(log n) time using n/log n processors. Furthermore, an improved version runs in O(log m log n) time using nm2/(log m log n) processors.
In most recent substructuring methods, a fundamental role is played by the coarse space. For some of these methods (e.g. BDDC and FETI-DP), its definition relies on a 'minimal' set of coarse nodes (sometimes c...
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In most recent substructuring methods, a fundamental role is played by the coarse space. For some of these methods (e.g. BDDC and FETI-DP), its definition relies on a 'minimal' set of coarse nodes (sometimes called corners) which assures invertibility of local subdomain problems and also of the global coarse problem. This basic set is typically enhanced by enforcing continuity of functions at some generalized degrees of freedom, such as average values on edges or faces of subdomains. We revisit existing algorithms for selection of corners. The main contribution of this paper consists of proposing a new heuristic algorithm for this purpose. Considering faces as the basic building blocks of the interface, inherent parallelism, and better robustness with respect to disconnected subdomains are among features of the new technique. The advantages of the presented algorithm in comparison to some earlier approaches are demonstrated on three engineering problems of structural analysis solved by the BDDC method. (c) 2011 IMACS. Published by Elsevier B.V. All rights reserved.
Coarse grain message passing and shared memory algorithms for solving the quasi-triangular Sylvester equation are discussed. The basic algorithm is of block type, i.e., rich in matrix-matrix operations. The focus is o...
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Coarse grain message passing and shared memory algorithms for solving the quasi-triangular Sylvester equation are discussed. The basic algorithm is of block type, i.e., rich in matrix-matrix operations. The focus is on computing reliable estimates of the sep-1 function (a natural condition number for the Sylvester equation and the invariant subspace problem). Estimators based on the Frobenius norm and the 1-norm, respectively, are presented. Accuracy, efficiency, and reliability results are presented. The applicability of the estimators to both the shared memory and distributed memory paradigms are discussed. Some performance results of the parallel block algorithms with condition estimators are also presented. The reliability of both estimators are very good. The Frobenius norm-based estimator is much more efficient in both sequential and parallel settings (on average between four to five times). Further, it is applicable to both the standard and generalized problems.
There is a group of problems that require big amount of computing power to solve. Computer grids allow building effective computing platforms at relatively low cost. It is expected that algorithms like Genetic Algorit...
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There is a group of problems that require big amount of computing power to solve. Computer grids allow building effective computing platforms at relatively low cost. It is expected that algorithms like Genetic Algorithm will perform well on the grid. In this paper, grid implementation of multiobjective distributed genetic algorithm is proposed. A distributed version of the algorithm is based on a modified island algorithm where genetic data exchange is replaced by introduced new Forgetting Island Elitism. The algorithm is applied to booster station allocation in Chojnice water distribution system.
parallel Jacobi-like algorithms are presented for computing a singular-value decomposition of an $m \times n$ matrix $(m \geqq n)$ and an eigenvalue decomposition of an $n \times n$ symmetric matrix. A linear array of...
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parallel Jacobi-like algorithms are presented for computing a singular-value decomposition of an $m \times n$ matrix $(m \geqq n)$ and an eigenvalue decomposition of an $n \times n$ symmetric matrix. A linear array of $O(n)$ processors is proposed for the singular-value problem; the associated algorithm requires time $O(mnS)$, where S is the number of sweeps (typically $S \leqq 10$). A square array of $O(n^2 )$ processors with nearest-neighbor communication is proposed for the eigenvalue problem; the associated algorithm requires time $O(nS)$.
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