Justin R Smith · 1993
Abstract We will consider how several different networks handle many common algorithms. In order to do this, we follow Preparata and Vuillemin in [125] in defining a pair of generic parallel algorithms that can be easily implemented on the common network-Naturally, some networks are better than others for developing parallel algorithms. Essentially, they have structural properties that make it easier to describe the data movement operations necessary for parallel computations. We will consider several such architectures in this chapter. In every case, physical computers have been con structed that implement the given architecture.
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Christiane Frougny, Pavel Heller · 2015
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Hrivnac, J. · HAL (Le Centre pour la Communication Scientifique Directe) · 2012
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Nachum Dershowitz, Evgenia Falkovich · Lecture notes in computer science · 2014
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Seyed H. Roosta · 2000
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Jiayang Jiang, Michael Mitzenmacher, Justin Thaler · ACM Transactions on Parallel Computing · 2016
The analysis of several algorithms and data structures can be framed as a peeling process on a random hypergraph: vertices with degree less than k are removed until there are no vertices of degree less than k left. The remaining hypergraph is known as the k -core. In this article, we analyze parallel peeling processes, in which in each round, all vertices of degree less than k are removed. It is known that, below a specific edge-density threshold, the k -core is empty with high probability. We show that, with high probability, below this threshold, only 1/log(( k -1)( r -1)) log log n + O (1) rounds of peeling are needed to obtain the empty k -core for r -uniform hypergraphs. This bound is tight up to an additive constant. Interestingly, we show that, above this threshold, Ω(log n ) rounds of peeling are required to find the nonempty k -core. Since most algorithms and data structures aim to peel to an empty k -core, this asymmetry appears fortunate. We verify the theoretical results both with simulation and with a parallel implementation using graphics processing units (GPUs). Our implementation provides insights into how to structure parallel peeling algorithms for efficiency in practice.
Cited by 17Open access
Choice Reviews Online · 1989
Parallel algorithms designed for simulation and performance evaluation of single-server tandem queueing systems with both infinite and finite buffers are presented.The algorithms exploit a simple computational procedure based on recursive equations as a representation of system dynamics.A brief analysis of the performance of the algorithms are given to show that they involve low time and memory requirements.
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Siege1, Howard Jay, Wang, Lee, So, John John E. et al. · Purdue e-Pubs (Purdue University System) · 1994
Data parallelism is a model of parallel computing in which the same set of instructions is applied to all the elements in a data set. A sampling of data parallel algorithms is presented. The examples are certainly not exhaustive, but address many issues involved in designing data parallel algorithms. Case studies are used to illustrate some algorithm design techniques; and to highlight some implementation decisions that influence the overall performance of a parallel algorithm. It is shown that the characteristics of a particular parallel machine to be used need to be considered in transforming a given task into a parallel algorithm that executes effectively.
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Adam Freeman · Apress eBooks · 2010
In this chapter are 11 common parallel algorithms implemented using the TPL. You can download all of the source code for all of the algorithms from www.Apress.com.
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Peter Hochschild, Ernst Mayr, Alan Siegel · 1984
This paper presents new paradigms to solve efficiently a variety of graph problems on parallel machines. These paradigms make it possible to discover and exploit the parallelism inherent in many classical graph problems. We abandon attempts to force sequential algorithms into parallel environments for such attempts usually result in transforming a good uniprocessor algorithm into a hopelessly greedy parallel algorithm. We show that by employing more local computation and mild redundance, a variety of problems can be solved in a resource- and time-efficient manner on a variety of architectures.
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Zbigniew J. Czech · Cambridge University Press eBooks · 2016
As we stated in Section 3.1, a parallel algorithm is composed of a number of algorithm components. These components are intended to solve the subproblems into which the original problem has been divided. Normally, the components cooperate with each other using intermediate results of computation as well as synchronize their action. The desired output of the parallel algorithm is a composition of results computed by the algorithm components. Since the subproblems are solved by the components of a parallel program called tasks, we will use the terms subproblem and task interchangeably.
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Gabriel Luque, Enrique Alba, Bernabé Dorronsoro · 2005
This chapter contains sections titled: Introduction Panmictic Genetic Algorithms Structured Genetic Algorithms Parallel Genetic Algorithms Experimental Results Summary References
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Kyongsoo Jung · 1995
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El‐Ghazali Talbi · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
International audience
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S. K. Dallakyan, N. S. Ananikian, R. G. Ghulghazaryan · 2008
The mechanisms leading to thermodynamic chaos in the Baxter-Wu model is considered. We compare the Baxter-Wu model with triangular antiferromagnets and discuss the difficulties related to the modeling of thermodynamic chaos by disordered models. We also discuss how to overcome the problem of infinitely many order parameters. Then we consider the Baxter-Wu model in a complex magnetic field and show the existence of infinitely many critical exponents in this model.
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Sergei M. Ermakov, Nikolai K. Krivulin · 2016
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Weill, J.C., Baudot, M.F., Seret, J.L. et al. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1996
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Fernandez, L.A.G., Kronsjo, L.I, Birmingham Univ. (United Kingdom). School of Computer Science · OpenGrey (Institut de l'Information Scientifique et Technique) · 1994
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S. W. Song · 2007
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Janson, S., Merkle, D., Middendorf, M. · Publikation Server (Leipzig University)
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