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34th IEEE International Conference on Data Engineering Workshops (ICDEW)

作者： Fan, Wenfei Liu, Xueli Cao, Yingjie Univ Edinburgh Edinburgh Midlothian Scotland Harbin Inst Technol Harbin Heilongjiang Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781538655207

This paper develops techniques for reasoning about graph functional dependencies (GFDs). We study the satisfiability problem, to decide whether a given set of GFDs has a model, and the implication problem, to decide whether a set of GFDs entails another GFD. While these fundamental problems are important in practice, they are coNP-complete and NP-complete, respectively. We establish a small model property for satisfiability, showing that if a set Sigma of GFDs is satisfiable, then it has a model of a size bounded by the size vertical bar Sigma vertical bar of Sigma;similarly we prove a small model property for implication. Based on the properties, we develop algorithms for checking the satisfiability and implication of GFDs. Moreover, we provide parallel algorithms that guarantee to reduce running time when more processors are used, despite the intractability of the problems. We experimentally verify the efficiency and scalability of the algorithms.

关键词： parallel algorithms Program processors Cognition Semantics Scalability Static analysis Pattern matching

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Alternating Optimization for Tensor Factorization with Orthogonality Constraints: Algorithm and parallel Implementation 16

Alternating Optimization for Tensor Factorization with Ortho...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Karakasis, Paris A. Liavas, Athanasios P. Tech Univ Crete Sch Elect & Comp Engn Khania Greece

ISBN: (纸本)9781538678794

We consider the problem of tensor factorization in the cases where one of the factors is constrained to have orthonormal columns. We adopt the alternating optimization framework and derive an efficient algorithm that is also suitable for parallel implementation. We describe in detail a distributed memory implementation of the algorithm on a three-dimensional processor grid. The speedup attained by a message-passing implementation of the algorithm is significant, indicating that it is a competitive candidate for the solution of very large tensor factorization problems with orthogonality constraints.

关键词： tensors tensor factorization PARAFAC orthogonality constraints algorithms parallel algorithms

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GPU Based Acceleration of parallel Block Lancoz Solver

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2018年第4期39卷 596-602页

作者： Zamarashkin, N. L. Zheltkov, D. A. RAS Inst Numer Math Gubkina Str 8 Moscow 119333 Russia

The block Lanczos method for huge sparse linear systems over large prime finite fields is accelerated on GPU. Calculations on GPU are used for the operations with the dense matrices and blocks. The achieved acceleration of block operations significantly increases the parallel resource of the entire method, expanding the scalability area close to linear. The numerical experiments were carried out on supercomputers Lomonosov and Lomonosov-2.

关键词： parallel algorithms large finite fields factorization problem

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Novel parallel Anytime A* for Graph and Network Clustering

Novel Parallel Anytime A* for Graph and Network Clustering

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： da Silva Mendes, Rudson Francisco de Santiago, Rafael Lamb, Luis C. Univ Vale Itajai Lab Appl Intelligence Itajai Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9781509060177

Recently, clustering in graphs and networks have been widely investigated in Computer Science, Artificial Intelligence, Social and Biological sciences. Newman's modularity concept is now widely used in an increasing number of applications. However, developments in Modularity Density Maximization have recently shown improved and better than expected clustering results. In this paper, we investigate and report results on the solution of Li's modularity clustering problem by using Anytime A* search, which offers a novel strategy to tackle this type of problem. In order to do so, we propose two versions of the Anytime A* method, a sequential and a parallel one. In the parallel approach, a pair of connected nodes is used as constraints to divide the search space among the threads. Our results indicate that Anytime A* is a promising search method in this context, leading to small gaps to the best-known solution. Further, the proposed methods add flexibility to the use of parallel strategies that effectively speed up searches.

关键词： clustering methods heuristic algorithms parallel algorithms

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A robust, efficient, and balanced parallel algorithm for finding connected components

A robust, efficient, and balanced parallel algorithm for fin...

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IEEE International Conference on Big Data

作者： M. Asokan Syncsort Inc Pearl River NY USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Finding connected components in an undirected graph has many practical applications. For example in a graph representing a social network, a connected component represents a group of related individuals with common interest. Also, finding connected components forms the basis for other clustering algorithms. In this paper, we will present a parallel algorithm which uses the well known sequential algorithm as the basis for finding connected components in an undirected graph. The algorithm can be adopted to run on a single computer with multiple cores or MapReduce. It is robust in the sense that it honors memory limits. This is important in today's containerized environments. It balances the workload even in the presence of data skew. For the best known algorithm running in MapReduce, the number of iterations is the square of the logarithmic function of the number of vertices in the graph. For our algorithm, we will prove that the upper bound on the number of iterations is a logarithmic function of the maximum size of a connected component. In each iteration, the amount of data read from or written to a file system is bounded by four times the number of edges in the graph.

关键词： Computational modeling Program processors parallel algorithms Clustering algorithms Phase change random access memory Containers

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Conditional Hardness Results for Massively parallel Computation from Distributed Lower Bounds

Conditional Hardness Results for Massively Parallel Computat...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Mohsen Ghaffari Fabian Kuhn Jara Uitto Department of Computer Science ETH Zürich Zürich Switzerland Department of Computer Science University of Freiburg Freiburg Germany Department of Computer Science Aalto University Espoo Finland

We present the first conditional hardness results for massively parallel algorithms for some central graph problems including (approximating) maximum matching, vertex cover, maximal independent set, and coloring. In some cases, these hardness results match or get close to the state of the art algorithms. Our hardness results are conditioned on a widely believed conjecture in massively parallel computation about the complexity of the connectivity problem. We also note that it is known that an unconditional variant of such hardness results might be somewhat out of reach for now, as it would lead to considerably improved circuit complexity lower bounds and would concretely imply that NC 1 is a proper subset of P. We obtain our conditional hardness result via a general method that lifts unconditional lower bounds from the well-studied LOCAL model of distributed computing to the massively parallel computation setting.

关键词： Complexity theory Approximation algorithms Computational modeling Computer science Program processors Distributed computing parallel algorithms

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GPU and Multiprocessing parallel Implementations for the Elimination of Finite State Machine Mutants 18

GPU and Multiprocessing Parallel Implementations for the Eli...

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18th IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Badawi, Emad El-Fakih, Khaled Barlas, Gerassimos Amer Uivers Sharjah Dept Comp Sci & Engn Sharjah U Arab Emirates

ISBN: (纸本)9781538678398

Given a test suite TS and a set of mutants that can be derived from the specification FSM S with respect to an assumed type of faults. Mutants' elimination deals with killing each mutant of the fault domain that has an output behavior different than that of S in respect to some test case of TS. This process is time consuming, especially when the number of mutants is in the order of millions. Thus, we present and assess two parallel implementations for the considered problem based on the OpenMP and GPU with CUDA technologies. Experiments are conducted to assess the speedup (and execution time) of proposed implementations using both random and real machines. CUDA implementation is shown to be scalable. Experiments also conducted to determine the experimental setup attributes such as TS length, number of - test cases, threads in OpenMP, and inputs of a test case that will be applied to the mutants in each GPU invocation.

关键词： mutant elimination mutation testing finite state machines parallel algorithms GPU CUDA OpenMP

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Effects of Parameters of an Island Model parallel Genetic Algorithm for the Quadratic Assignment Problem

Effects of Parameters of an Island Model Parallel Genetic Al...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Alper Kizil Korhan Karabulut Computer Engineering Department Yasar University Izmir Software Engineering Department Yasar University Izmir

ISBN: (纸本)9781728126289

Quadratic Assignment Problem (QAP) is one of the most difficult combinatorial problems in the literature and has a diverse field of applications. This paper presents the results of experiments on the impact of parallelization of a sequential GA using island model. Both of the genetic algorithms are applied to the QAP. For the island model parallel GA, we systematically change the number of islands and investigate the effects of dividing the same global population into a number of subpopulations. The number of islands is gradually increased to observe the effects on solution quality and speedup in total execution time using different problem instances. The results clearly indicate that, while parallelized version outperforms sequential counterpart in both solution quality and total execution time, an increasing number of subpopulations also positively effects the results until a critical point where every subpopulation has a certain number of individuals to be able to evolve independently. Beyond that point, the performance of the algorithm begins to decrease.

关键词： combinatorial mathematics genetic algorithms parallel algorithms search problems Genetic Algorithm (GA) combinatorial mathematics tabu search parallel algorithms islands execution time channel assignment problem Quadratic

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A Novel Approach for Scheduling and Mapping of Real-Time parallel Matrices Multiplication (SMPMM)

A Novel Approach for Scheduling and Mapping of Real-Time Par...

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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： Hussam Abu Azab Habib Hamam Department of Engineering University de Moncton Moncton Canada

ISBN: (数字)9781728137780

ISBN: (纸本)9781728137797

This paper introduces a novel parallel matrices multiplication algorithm (SMPMM) implies dividing the problem of matrices multiplication into smaller independent tasks where each processor in the parallel environment executes one single task a time, once done, the processor receives another task to process it. As opposed to previous algorithms, like Cannon, Fox, PUMMA, SUMMA, DIMMA and HSUMMA algorithms, where the decomposition is carried out on the data, i.e. the multiplied matrices are decomposed into small blocks, where each processor multiplies some blocks and sends the result to neighbor processors; SMPMM does include any data decomposing. In addition, SMPMM contradicts with previous algorithms where there is no data exchange and no communication among processors on in the parallel environment. One more important advantage is SMPMM multiplies non-square matrices in parallel, which is not available by any previous parallel matrices multiplication algorithms.

关键词： Task analysis Matrix decomposition Layout Multicore processing Heuristic algorithms parallel algorithms Cache memory

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A Gaussian Recursive Filter parallel Implementation with Overlapping

A Gaussian Recursive Filter Parallel Implementation with Ove...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Pasquale De Luca Ardelio Galletti Livia Marcellino Department of Computer Science University of Salerno Italy Department of Science and Technology University of Naples Parthenope Naples Italy

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Gaussian convolutions computation is required in several scientific fields and, to this aim, efficient approximation methods, based on Recursive Filters (RFs), have been developed recently. Among them, Gaussian Recursive Filters (RFs) are designed to approximate the Gaussian convolution in a very efficient way. The accuracy of these methods, as is well known, can be improved by means of the use of the so-called K-iterated Gaussian recursive filters, that is in the repeated application of the basic RF. To improve the provided accuracy, K-iterated versions of these methods are also considered. Since it is often necessary to handle large size one-dimensional input signals, a parallel approach becomes mandatory. Recently, we proposed a parallel algorithm for the implementation of the K-iterated first-order Gaussian RF on multicore architectures. Here, using a similar parallelization strategy, based on a domain decomposition with overlapping, we propose a new implementation that would exploit, in terms of both accuracy and performance, the GPU (Graphics Processing Unit) capabilities on CUDA environment. Tests and experiments confirm the reliability and the efficiency of the proposed implementation.

关键词： Radio frequency Convolution Graphics processing units parallel algorithms Tools Boundary conditions Instruction sets

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