We present a family of periodic comparator networks that transform the input so that it consists of a few sorted subsequences. The depths of the networks range from 4 to 2 log n while the number of sorted subsequences...
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ISBN:
(纸本)3540405437
We present a family of periodic comparator networks that transform the input so that it consists of a few sorted subsequences. The depths of the networks range from 4 to 2 log n while the number of sorted subsequences ranges from 2 log n to 2. They work in time c log(2) n + O(log n) with 4 less than or equal to c less than or equal to 12, and the remaining constants are also suitable for practical applications. So far, known periodic sorting networks of a constant depth that run in time O(log(2) n) (a periodic version of AKS network [7]) are impractical because of complex structure and very large constant factor hidden by big "Oh".
The studied large-scale linear problems arise from Crouzeix-Raviart non-conforming FEM approximation of second order elliptic boundary value problems. A two-level preconditioner for the case of coefficient anisotropy ...
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ISBN:
(纸本)3540210903
The studied large-scale linear problems arise from Crouzeix-Raviart non-conforming FEM approximation of second order elliptic boundary value problems. A two-level preconditioner for the case of coefficient anisotropy is analyzed. A special attention is given to the potential of the method for a parallel implementation.
The accuracies of three equations to determine the size of populations for serial and parallel genetic algorithms are evaluated when applied to a parallel genetic algorithm that schedules tasks on a cluster of compute...
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The accuracies of three equations to determine the size of populations for serial and parallel genetic algorithms are evaluated when applied to a parallel genetic algorithm that schedules tasks on a cluster of computers connected via shared bus. This NP-complete problem is representative of a variety of optimisation problems for which genetic algorithms (GAs) have been shown to effectively approximate the optimal solution. However, empirical determination of parameters needed by both serial and parallel GAs is time-consuming, often impractically so in production environments. The ability to predetermine parameter values mathematically eliminates this difficulty. The parameter that exerts the most influence over the solution quality of a parallel genetic algorithm is the population size of the demes. Comparisons here show that the most accurate equation for the scheduling application is Cantú-Paz serial population sizing calculation based on the gambler's ruin model [1]. The study presented below is part of an ongoing analysis of the effectiveness of parallel genetic algorithm parameter value computations based on schema theory. The study demonstrates that the correct deme size can be predetermined quantitatively for the scheduling problem presented here, and suggests that this may also be true for similar optimisation problems.
We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solving some algorithmic problems in graphs, including split decompositi...
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We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solving some algorithmic problems in graphs, including split decomposition. We show that efficient parallel split decomposition induces an efficient parallel parity graph recognition algorithm. This is a consequence of the result of S. Cicerone and D. Di Stefano [7] that parity graphs are exactly those graphs that can be split decomposed into cliques and bipartite graphs, (C) 2000 Academic Press.
New parallel "ring" algorithm for solution of a spatially one dimensional initial-boundary-value problem (IBVP) for a parabolic equation using an explicit difference method in this article is suggested. The ...
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New parallel "ring" algorithm for solution of a spatially one dimensional initial-boundary-value problem (IBVP) for a parabolic equation using an explicit difference method in this article is suggested. The parallel algorithm has been verified by implementation on a workstation-cluster running under parallel virtual machine (PVM). A speed-up function is determined as a ratio of time needed for realization of the algorithm in sequential case to time in parallel. Theoretical estimates of the speed-up function show the significant speed-up of the parallel algorithm in comparison with the serial one.
In this paper we present a parallel algorithm, implemented using MPICH, for isosurface extraction from volumetric data sets. The main contribution of this paper is in the analysis and performance improvements of the d...
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In this paper four parallel algorithms for the evaluation of finite series of orthogonal polynomials are introduced. The algorithms are based on the Forsythe and Clenshaw sequential algorithms. Several tests carried o...
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In this paper four parallel algorithms for the evaluation of finite series of orthogonal polynomials are introduced. The algorithms are based on the Forsythe and Clenshaw sequential algorithms. Several tests carried out on a Cray T3D are presented.
This paper addresses the problem of developing efficient parallel algorithms for the training procedure of a neural network-based Fingerprint Image Comparison (FIC) system. The target architecture is assumed to be a c...
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This paper addresses the problem of developing efficient parallel algorithms for the training procedure of a neural network-based Fingerprint Image Comparison (FIC) system. The target architecture is assumed to be a coarse-grain distributed-memory parallel architecture. Two types of parallelism-node parallelism and training set parallelism (TSP)-are investigated. Theoretical analysis and experimental results show that node parallelism has low speedup and poor scalability, while TSP proves to have the best speedup performance. TSP, however, is amenable to a slow convergence rate. To reduce this effect, a modified training set parallel algorithm using weighted contributions of synaptic connections is proposed. Experimental results show that this algorithm provides a fast convergence rate while keeping the best speedup performance obtained. The combination of TSP with node parallelism is also investigated. A good performance is achieved by this approach. This provides better scalability with the trade-off of a slight decrease in speedup. The above algorithms are implemented on a 32-node CM-5.
Coupled-cluster (CC) methods are now widely used in quantum chemistry to calculate the electron correlation energy and many other properties of atoms and molecules. In this paper we outline the basics of the theory, d...
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Coupled-cluster (CC) methods are now widely used in quantum chemistry to calculate the electron correlation energy and many other properties of atoms and molecules. In this paper we outline the basics of the theory, discuss some computational aspects, and review work that has been done toward developing and implementing algorithms for CC methods on parallel computers. (C) 2000 Elsevier Science B.V. All rights reserved.
One of the recent thrust areas in research on hyperelliptic curve cryptography has been to obtain explicit formulae for performing arithmetic in the Jacobian of such curves. We continue this line of research by obtain...
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