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The MapReduce-based approach to improve the shortest path computation in large-scale road networks: the case of A* algorithm

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JOURNAL OF BIG DATA 2018年第1期5卷 1页

作者： Adoni, Wilfried Yves Hamilton Nahhal, Tarik Aghezzaf, Brahim Elbyed, Abdeltif Hassan II Univ Casablanca Fac Sci Lab Informat Modelisat Syst & Aide Decis Km 8 Route El JadidaBP 5366 Casablanca Morocco

This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardware to compute the shortest path on large-scale networks. Thus, it is necessary to reduce the time complexity while exploiting a low cost commodity hardwares. To cope with this situation, we propose a novel approach that reduces the A* algorithm into a set of Map and Reduce tasks for running the path computation on Hadoop MapReduce framework. An application on real road networks illustrates the feasibility and reliability of the proposed framework. The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time.

关键词： Path-finding Large-scale network A(*) algorithm Big Data Hadoop MapReduce HDFS parallel and distributed computing

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Dependable parallel Canonical Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

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IFAC-PapersOnLine 2018年第28期51卷 167-172页

作者： Yoshida, Hotaka Azuma, Daich Fukuyama, Yoshikazu Meiji University Tokyo Japan

This paper proposes dependable parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt/Var Control: VVC). Since the problem can be formulated as a mixed integer nonlinear optimization problem (MINLP), various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Considering large penetration of renewable energies and deregulated environments of power systems, VVC requires fast computation for larger-scale VVC problems. One of the solutions to speed-up the computation is to utilize parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VVC. The simulation results with with IEEE 14, 30, 57, and 118 bus systems indicate that parallel C-DEEPSO is superior to the conventional parallel DEEPSO from the dependability point of view. © 2018

关键词： Particle swarm optimization (PSO) Computational efficiency distributed computer systems Integer programming Nonlinear programming Power control Reactive power Voltage control Differential evolutionary Evolutionary computation techniques Heuristic search Mixed integer nonlinear optimization problems Optimization problems parallel and distributed computing parallel Computation Voltage and reactive power control

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What Are the Limits of Evolutionary Induction of Decision Trees? 15th

What Are the Limits of Evolutionary Induction of Decision Tr...

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15th International Conference on parallel Problem Solving from Nature (PPSN)

作者： Jurczuk, Krzysztof Reska, Daniel Kretowski, Marek Bialystok Tech Univ Fac Comp Sci Wiejska 45a PL-15351 Bialystok Poland

ISBN: (纸本)9783319992594;9783319992587

For typical assessment of applying machine learning or data mining techniques, accuracy and interpretability are usually the most important elements. However, when the analyst is faced with real contemporary big data problems, scalability and efficiency become crucial factors. parallel and distributed processing support is often an indispensable component of operational solutions. In the paper, we investigate the applicability of evolutionary induction of decision trees to large-scale data. We focus on the existing Global Decision Tree system, which searches the tree structure and tests in one run of an evolutionary algorithm. Evolved individuals are not encoded, so the specialized genetic operators and their application schemes are used. As in most evolutionary data mining systems, every fitness evaluation needs processing the whole training dataset. For high-dimensional datasets, this operation is very time consuming and to overcome this deficiency, two acceleration solutions, based on the most promising, latest approaches (NVIDIA CUDA and Apache Spark) are presented. The fitness calculations are delegated, while the core evolution is unchanged. In the experimental part, among others, we identify what are dataset dimensions which can be efficiently processed in the fixed time interval.

关键词： Evolutionary data mining Decision trees parallel and distributed computing Spark GPU CUDA

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A Pragmatic Study and Analysis of Load Balancing Techniques in parallel computing 6th

A Pragmatic Study and Analysis of Load Balancing Techniques ...

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6th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA)

作者： Thakur, Varsha Kumar, Sanjay PRSU SoS CS & IT Raipur Madhya Pradesh India

ISBN: (纸本)9789811075636;9789811075629

Availability of economical systems and advancement in communication technicalities has galvanized appreciable attentiveness in a parallel architecture. parallel architecture is materialized as a research area with the perspective of furnishing faster results. In a parallel architecture, balancing of load over engaged cores or nodes is a critical issue. Imbalance in cores or processors degrades the performance of systems. This paper presents a study and comparative anatomy of some load balancing approaches on parallel architecture with its effect on parallelism. The performances are analyzed on the basis of execution time, speed up, throughput, and efficiency. To implement load balancing approaches we have used OpenMP in Linux Environment.

关键词： Load balancing Dynamic Static parallel and distributed computing OMP

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Integrating parallel computing in Introductory Programming Classes: An Experience and Lessons Learned 1

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23rd International Conference on parallel and distributed computing (Euro-Par)

作者： Ghafoor, Sheikh Brown, David W. Rogers, Mike Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (数字)9783319751788

ISBN: (纸本)9783319751788;9783319751771

parallel and distributed computing (PDC) has become ubiquitous to the extent that even common users depend on parallel programming. This points to the need for every programmer to understand how parallelism and distributed programming affect problem solving, teaching only traditional sequential programming is no longer sufficient. To address the rapidly widening gap between emerging highly-parallel computer architectures and the sequential programming approach taught in traditional CS/CE courses, the Computer Science Department at Tennessee Technological University has integrated PDC into their introductory programming course sequence. This paper presents our implementation efforts, experience and lessons learned, as well as preliminary evaluation results.

关键词： parallel and distributed computing Introductory programming Undergraduate education

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parallel Canonical Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

Parallel Canonical Differential Evolutionary Particle Swarm ...

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10th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Azuma, Daichi Yoshida, Hotaka Fukuyama, Yoshikazu Meiji Univ Tokyo Japan

This paper proposes parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt Var Control: VVC). The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP) and various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Since VVC is one of the on-line controls, speed-up of computation is required. Moreover, there is still room for improvement on solution quality. This paper proposes parallel C-DEEPSO in order to speed up the calculation and improve solution quality and applies it to a VVC problem. The proposed method is applied to IEEE 118 bus system. The results indicate that the proposed method can realize fast computation and minimize active power losses more than conventional evolutionary computation techniques. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Voltage and reactive power control Power system Evolutionary computation parallel and distributed computing parallel canonical differential evolutionary particle swarm optimization

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Spark-based Spatial Association Mining

Spark-based Spatial Association Mining

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IEEE International Conference on Big Data (Big Data)

作者： Binzani, Kanika Yoo, Jin Soung Purdue Univ Dept Comp Sci Ft Wayne IN 46805 USA

ISBN: (纸本)9781538650356

Spatial association mining, as one of important techniques for spatial data mining, is used to discover interesting relationship patterns among spatial features based on spatial proximity from a large spatial database. Explosive growth in georeferenced data has emphasized the need to develop computationally efficient methods for analyzing big spatial data. parallel and distributed computing is effective and mostly-used strategy for speeding up large scale dataset algorithms. This work presents parallel spatial association mining on the Spark RDD framework -a specially-designed in-memory parallel computing model to support iterative algorithms. The initial experiment result shows that the Spark-based algorithm has significantly improved performance than the method with MapReduce in spatial association pattern mining.

关键词： Spatial association pattern mining parallel and distributed computing Spark

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On the restricted connectivity of the arrangement graph

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JOURNAL OF SUPERcomputing 2017年第8期73卷 3669-3682页

作者： Cheng, Eddie Qiu, Ke Shen, Zhizhang Oakland Univ Dept Math & Stat Rochester MI 48309 USA Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada Plymouth State Univ Dept Comp Sci & Technol MSC 41 Plymouth NH 03264 USA

We derive a general upper bound of the -restricted connectivity for the arrangement graph, namely the minimum cardinality of a vertex set, whose removal disconnects the graph, but every remaining vertex has at least neighbors in the survival graph. We show that this upper bound is exact when and provide an asymptotic lower bound for the cases where .

关键词： Fault tolerance property R-h-restricted connectivity Arrangement graph parallel and distributed computing

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parallel Multi-Block ADMM with o(1 / k) Convergence

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JOURNAL OF SCIENTIFIC computing 2017年第2期71卷 712-736页

作者： Deng, Wei Lai, Ming-Jun Peng, Zhimin Yin, Wotao Rice Univ Dept Computat & Appl Math Houston TX 77005 USA Univ Georgia Dept Math Athens GA 30602 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA

This paper introduces a parallel and distributed algorithm for solving the following minimization problem with linear constraints: minimize f(1)(x(1))+ . . . + f(N) (x(N)) subject to A(1)x(1) + . . . + A(N)x(N) = c, x(1) is an element of chi(1), . . . , x(N) is an element of chi(N), where , are convex functions, are matrices, and are feasible sets for variable . Our algorithm extends the alternating direction method of multipliers (ADMM) and decomposes the original problem into N smaller subproblems and solves them in parallel at each iteration. This paper shows that the classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to the N subproblems (but without any assumption on matrices ). In the latter case, certain proximal terms can let the subproblem be solved in more flexible and efficient ways. We show that converges at a rate of o(1 / k) where M is a symmetric positive semi-definte matrix. Since the parameters used in the convergence analysis are conservative, we introduce a strategy for automatically tuning the parameters to substantially accelerate our algorithm in practice. We implemented our algorithm (for the case ii above) on Amazon EC2 and tested it on basis pursuit problems with > 300 GB of distributed data. This is the first time that successfully solving a compressive sensing problem of such a large scale is reported.

关键词： Alternating direction method of multipliers ADMM parallel and distributed computing Convergence rate

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SeeMore: A kinetic parallel computer sculpture for educating broad audiences on parallel computation

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JOURNAL OF parallel and distributed computing 2017年 105卷 183-199页

作者： Li, Bo Mooring, John Blanchard, Sam Johri, Aditya Leko, Melinda Cameron, Kirk W. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Microsoft Seattle WA USA Virginia Tech Sch Visual Arts Blacksburg VA USA George Mason Univ Dept Informat Sci & Technol Fairfax VA 22030 USA Univ Kansas Dept Special Educ Lawrence KS 66045 USA

We discuss the design, implementation, and evaluation of a 256-node Raspberry-Pi cluster with kinetic properties. Each compute node is attached to a servo mechanism such that movement results from local computation. The result is SeeMore, a kinetic parallel computer sculpture designed to enable visualization of parallel algorithms in an effort to educate broad audiences as to the beauty, complexity, and importance of parallel computation. The algorithms and interfaces were implemented by students from various related courses at VA Tech. We describe these designs in sufficient detail to enable others to build their own kinetic computing sculptures to augment their experiential learning programs. Our evaluations at exhibitions indicate 63% and 84% of visitors enjoyed interacting with SeeMore while 69% and 87% believed SeeMore has educational value. Published by Elsevier Inc.

关键词： parallel and distributed computing Kinetic art Computer science education

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