A convex hull is one of the most fundamental and interesting geometric constructs ill computational geometry. Considerable research effort has focused on developing algorithms, both in serial and in parallel, for comp...
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A convex hull is one of the most fundamental and interesting geometric constructs ill computational geometry. Considerable research effort has focused on developing algorithms, both in serial and in parallel, for computing convex hulls. In particular, there are few I problems whose parallel algorithms are so thoroughly studied as convex hull problems. In this paper, we review the convex hull parallel algorithms and their paradigm. We provide a summary of results and introduce several interesting topics including typical techniques, output-size sensitive methods, randomized approaches, and robust algorithms for convex hull problems, with which we may see the highlights of the whole research for parallel algorithms, Most of our discussion uses the PRAM (parallel Random Access Machine) computational model. but still we give a glance at the results of the other parallel computational models such as mesh, mesh-of-trees, hypercube, recofigurable array, and models of coarse grained multicomputers like BSP and LogP.
Software configuration management (SCM) systems require the ability to maintain collections of a large number of specific versions of files. One technique for managing these configurations is to maintain a single time...
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ISBN:
(纸本)076950759X
Software configuration management (SCM) systems require the ability to maintain collections of a large number of specific versions of files. One technique for managing these configurations is to maintain a single time-stamp identifying which versions of the files belong to the configuration along with an exception list to that time-stamp. Given the individual time-stamps of n files represented by half-open intervals, we develop an O (n log n) time sequential algorithm that finds a time-stamp leading to the smallest possible exception list. The technique is used ill a commercial SCM system and works well in practice. We also develop a parallel version of the algorithm that films in O (log n) rime using n processors on all EREW-PRAM. IS the intervals are sorted, the sequential algorithm runs optimally in O(n) time and the parallel algorithm runs optimally in O (log n) time using n/ log n processors on all EREW-PRAM.
In this paper, we examine the advantages of using a distributed version of the Block Preconditioned Conjugate Gradient as solving algorithm for FEM models arising from electrothermal problems. The implementation is do...
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ISBN:
(纸本)0780359577
In this paper, we examine the advantages of using a distributed version of the Block Preconditioned Conjugate Gradient as solving algorithm for FEM models arising from electrothermal problems. The implementation is done using WMPI as programming environment in a cluster configuration. The targeted application being to model heat transfer in a dry type transformer, we intend to include fluid flow analysis in addition to the conduction analysis.
Data Mining applications have to deaf with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few ini...
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ISBN:
(纸本)185312821X
Data Mining applications have to deaf with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few initial scans of the data compute predictive models for data mining and do not require further access to the data. In addition, we describe various ways of dealing with the complexity (high dimensionality) of the data. Three methods are presented for three different ranges of attribute numbers. They use ideas from the finite element method and are based on penalised least squares fits using sparse grids and additive models for intermediate and very high dimensional data. Computational experiments confirm scalability both with respect to data size and number of processors.
The PROUD module placement algorithm mainly uses a hierarchical decomposition technique and the solution of sparse linear systems based on a resistive network analogy. It has been shown that the PROUD algorithm can ac...
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ISBN:
(纸本)0769517307;0769517315
The PROUD module placement algorithm mainly uses a hierarchical decomposition technique and the solution of sparse linear systems based on a resistive network analogy. It has been shown that the PROUD algorithm can achieve a comparable design of the placement problems for very large circuits with the best placement algorithm based on simulated annealing, but with several order of magnitude faster. The modified PROUD, namely MPROUD algorithm by perturbing the coefficient matrices performs much faster that the original PROUD algorithm. Due to the instability and unguaranteed convergence of MPROUD algorithm, we have proposed a new convergent and numerically stable PROUD, namely Improved PROUD algorithm, denoted as IPROUD with attractive computational costs to solve the module placement problems by making use of the SYMMLQ and MINRES methods based on Lanczos process in [11]. In this paper, we subsequently propose parallel versions of the improved PROUD algorithms. The parallel algorithm is derived such that all inner products and matrix-vector multiplications of a single iteration step am independent. Therefore, the cost of global communication which represents the bottleneck of the parallel performance on parallel distributed memory computers can be significantly reduced, therefore, to obtain another order of magnitude improvement in the runtime without loss of the quality of the layout.
In this study, a solution to the school timetabling problem using parallel genetic algorithm with simulated annealing is presented. The hybridization of simulated annealing and parallel genetic algorithm is explained....
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ISBN:
(纸本)0769515797
In this study, a solution to the school timetabling problem using parallel genetic algorithm with simulated annealing is presented. The hybridization of simulated annealing and parallel genetic algorithm is explained. Also, how these algorithms are run in parallel on a local network of workstations are discussed. Some comparative results among the different parallel models are exhibited. The implementation of the parallel algorithms are used to construct conflict-free and satisfying timetables for the Department of Mathematics of the University of the Philippines Diliman. The program output of this study can be easily modified to be used as a helpful and efficient guide to the decision-making process of the scheduler.
This paper adopts a transformational programming approach for deriving massively parallel algorithms from functional specific ations. It gives a brief description of a framework for relating key higher order functions...
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In this paper, we present parallel algorithms for the coarse grained multicomputer (CGM) and the bulk synchronous parallel computer (BSP) for solving two well known graph problems: (1) determining whether a graph G is...
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We present a class of new parallel algorithms for solving large sparse linear systems with special structure on distributed memory multiprocessor systems such as PC clusters. The objective of these algorithms is to re...
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Exploiting programs' locality is one of the most important problems in parallel compiling optimization and the program transformations are one of the most important approaches in exploiting programs' temporal ...
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Exploiting programs' locality is one of the most important problems in parallel compiling optimization and the program transformations are one of the most important approaches in exploiting programs' temporal locality and spatial locality. The paper presents a new locality optimization approach using non-singular loop transformations to optimize programs' locality, namely linear expressing based loop transformations. This approach uses a group of the least linearly independent vectors to express array accesses' subscripts, and then constructs a non-singular loop transformation matrix to optimize array accesses' temporal locality and spatial locality. The approach can fully exploit array accesses' temporal locality, and easily determine whether array accesses' temporal locality or spatial locality can be exploited, it can also simultaneously optimize the given loop nest's temporal locality and spatial locality. The experimental results show that the linear expressing based approach for optimizing locality using non-singular loop transformations presented in this paper is effective.
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