This paper studies a parallel algorithm for real Toeplitz systems, which is proposed based on the block Jacobi iteration and GMRES method. The algorithm has the advantage of less float operations, fast convergence spe...
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This paper studies a parallel algorithm for real Toeplitz systems, which is proposed based on the block Jacobi iteration and GMRES method. The algorithm has the advantage of less float operations, fast convergence speed and especially suitable for parallel computating. In this paper, we first use the block Jacobi iterative method to obtain the iterative process, and then the GMRES method is nested to obtain the iterative sequences {x(k)}. Therefore, the parallel algorithm for solving symmetric positive definite Toeplitz systems is constructed. The convergence of the algorithm is also discussed simply in the paper. At the end, we give some numerical examples to illustrate the effectiveness of the parallel algorithm.
Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution- based strate...
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Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution- based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here.
A modified version of the fast parallel thinning algorithm proposed by Zhang and Suen is presented in this paper. It preserves the original merits such as the contour noise immunity and good effect in thinning crossin...
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A modified version of the fast parallel thinning algorithm proposed by Zhang and Suen is presented in this paper. It preserves the original merits such as the contour noise immunity and good effect in thinning crossing lines; and overcomes the original demerits such as the serious shrinking and line connectivity problems.
In order to overcome the heavy task of big data storage structure evolution computation, this paper proposes a parallel algorithm based network learning behaviour big data storage structure evolution model. This metho...
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In order to overcome the heavy task of big data storage structure evolution computation, this paper proposes a parallel algorithm based network learning behaviour big data storage structure evolution model. This method introduces parallel algorithm, divides the whole dataset into several non overlapping data subsets randomly, mines the local frequent itemsets in the network learning behaviour big data in parallel and hierarchically, and connects the local frequent itemsets. Frequent itemsets can get all candidate sets. The actual support degree of different candidate sets is calculated by scanning datasets, and the evolution model of big data storage structure of network learning behaviour is established. The experimental results show that the operation efficiency of the proposed evolutionary model is as high as 99%, the cost is significantly lower than the other three evolutionary models, and the storage space consumption is the lowest.
The Cosmic Microwave Background (CMB) is a diffuse radiation which is contaminated by the radiation emitted by point sources. The precise knowledge of CMB fluctuations can lead to a better knowledge of the chemistry a...
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The Cosmic Microwave Background (CMB) is a diffuse radiation which is contaminated by the radiation emitted by point sources. The precise knowledge of CMB fluctuations can lead to a better knowledge of the chemistry at the early stages of the Universe. In this work, we present an efficient algorithm, with a high degree of parallelism, which can improve, from the computational point of view, the classical approaches for detecting point sources in Cosmic Microwave Background maps. High performance computing libraries and parallel computing techniques have allowed to construct a portable, fast and numerically stable algorithm. To check the performance of the new method, we have carried out several simulations resembling the observational data collected by the Low Frequency Instrument of the Planck satellite. The sources are detected in their real positions.
This paper describes a new parallel algorithm for solving the m-machines, n-jobs flow-shop scheduling problem as well as its implementation on a distributed memory multiprocessor. The algorithm is basically a parallel...
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This paper describes a new parallel algorithm for solving the m-machines, n-jobs flow-shop scheduling problem as well as its implementation on a distributed memory multiprocessor. The algorithm is basically a parallelization of the usual branch-and-bound method. It also takes advantage of the all-search method to keep the efficiency of parallel processing reasonably high when subproblems become smaller than a certain size. The performance evaluation is done by comparing the parallel execution of this algorithm on the nCUBE2 multiprocessor and the sequential execution of the branch-and-bound with depth-first search algorithm. The result shows that the mean speedup ratio for some conditions of the problem is more than the number of processors, and the mean speedup ratio for the conditions on which the sequential executions complete quickly is not smaller than 1.
作者：
Wei, RanMurray, Alan T.Univ Utah
Dept Geog Salt Lake City UT 84112 USA Drexel Univ
Sch Publ Hlth Coll Comp & Informat Ctr Spatial Analyt & Geocomputat Philadelphia PA 19104 USA
Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of dema...
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Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.
The fractional reaction-diffusion equations play an important role in dynamical systems. Indeed, it is time consuming to numerically solve differential fractional diffusion equations. In this paper, we present a paral...
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The fractional reaction-diffusion equations play an important role in dynamical systems. Indeed, it is time consuming to numerically solve differential fractional diffusion equations. In this paper, we present a parallel algorithm for the Riesz space fractional diffusion equation. The parallel algorithm, which is implemented with MPI parallel programming model, consists of three procedures: preprocessing, parallel solver and postprocessing. The parallel solver involves the parallel matrix vector multiplication and vector vector addition. As to the authors' knowledge, this is the first parallel algorithm for the Riesz space fractional reaction-diffusion equation. The experimental results show that the parallel algorithm is as accurate as the serial algorithm. The parallel algorithm on single Intel Xeon X5540 CPU runs 3.3-3.4 times faster than the serial algorithm on single CPU core. The parallel efficiency of 64 processes is up to 79.39% compared with 8 processes on a distributed memory cluster system.
In this paper, we concentrate on the generalized multiple-set split feasibility problems in Hilbert spaces and propose a new iterative method for this problem. One of the most important of this method is using dynamic...
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In this paper, we concentrate on the generalized multiple-set split feasibility problems in Hilbert spaces and propose a new iterative method for this problem. One of the most important of this method is using dynamic step-sizes, in which the information of the previous step is the only requirement to compute the next approximation. The strong convergence result of the suggested algorithm is proven theoretically under some feasible assumptions. When considering the main results in some special cases, we also obtain some applications regarding the solution of the multiple-set split feasibility problem, the split feasibility problem with multiple output sets, and the split feasibility problem as well as the linear optimal control problem. Some numerical experiments on infinite-dimensional spaces and applications in optimal control problems are conducted to demonstrate the advantages and computational efficiency of the proposed algorithms over some existing results.
The simulation of biologically realistic neural networks requires the numerical solution of very large systems of differential equations. Variables within the system can be changing at rates that vary by orders of mag...
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The simulation of biologically realistic neural networks requires the numerical solution of very large systems of differential equations. Variables within the system can be changing at rates that vary by orders of magnitude, not only at different times of the solution, but at the same time in different parts of the network. Therefore, an efficient implementation must be able to vary the solution step size, and do so independently in different subsystems. A single processor algorithm is presented in which each neuron can be solved with its own step size by using a priority queue to integrate them in the correct order. But this leaves the problem of how communication and synchronisation between neurons should be managed when executing in parallel. The proposed solution uses an algorithm based on waveform relaxation, which allows groups of neurons on different processors to be solved independently and hence in parallel, for substantial parts of the computation. Realistic test problems were run on a distributed memory parallel computer and results show that speedups of 10 using 16 processors are achievable, and indicate that further speedups may be possible. (C) 2000 Elsevier Science B.V. All rights reserved.
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