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The engineering handbook of smart technology for aging, disability, and independence /

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2008年

作者： edited by Abdelsalam (Sumi) Helal Mounir Mokhtari Bessam Abdulrazak.

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Optimization design for parallel coloring of a set of graphs in the High-Performance computing 15

Optimization design for parallel coloring of a set of graphs...

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IEEE 15th International Scientific Conference on Informatics (INFORMATICS)

作者： Dudas, Adam Skrinarova, Jarmila Vesel, Eduard Univ Zilina Fac Management Sci & Informat Zilina Slovakia Matej Bel Univ Fac Nat Sci Banska Bystrica Slovakia

ISBN: (纸本)9781728131795

This paper presents solution to problem of edge coloring of sizable set of cubic graphs and examination of relations between these graphs. We solved this problem on various computing systems and for various sizes of the problem (various number of graphs). For the computations we used High-Performance computing Cluster and Amazon Web Services cloud environment. We measured and analyzed time of computation of edge coloring and other properties. Largest set we worked with contained almost 10 million graphs. We created new methodology, which can be used to finding order of the edges which optimizes time of computation of edge coloring for certain subset of graphs. On the basis of this methodology, we implemented algorithm for parallel edge coloring of set of graphs. For testing of the methodology, we designed 8 experiments. Results showed, that worst time of edge coloring of graph from set of 19 935 graphs before use of the methodology was 1260 ms. After application of our methodology, we found same order of edge coloring for whole group of 19 935 graphs and the highest time of coloring was 10 ms.

关键词： parallel and distributed computing HPC virtual machines edge coloring of cubic graphs

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parallel Domain Decomposition Methods for Ray-Tracing on multi-cores and multi-processors

Parallel Domain Decomposition Methods for Ray-Tracing on mul...

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10th International Symposium on distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Venet, Cedric Magoules, Frederic Ecole Cent Paris Appl Math & Syst Lab Chatenay Malabry France

ISBN: (纸本)9780769544151

In this paper, an original parallel domain decomposition method for ray-tracing is proposed to solve numerical acoustic problems on multi-cores and multi-processors computers. A hybrid method between the ray-tracing and the beam-tracing method is first introduced. Then, a new parallel method based on domain decomposition principles is proposed. This method allows to handle large scale open domains for parallel computing purpose, better than other existing methods. parallel numerical experiments, carried out on a real world problem-namely the acoustic pollution analysis within a large city-illustrate the performance of this new domain decomposition method.

关键词： Domain decomposition methods parallel and distributed computing Acoustics Ray-tracing methods Beam-tracing methods

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Diagnosability problems of the exchanged hypercube and its generalization

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International Journal of Computer Mathematics: Computer Systems Theory 2017年第2期2卷 39-52页

作者： Cheng, Eddie Qiu, Ke Shen, Zhizhang Department of Mathematics and Statistics Oakland University Rochester MI United States Department of Computer Science Brock University St. Catharines ON Canada Department of Computer Science and Technology Plymouth State University Plymouth NH United States

We continue to study various properties of the generalized exchanged hypercube (GEH) structure. We also derive the g-good-neighbour diagnosability of the exchanged hypercube structure, a special case of GEH, i.e. the maximum number of faulty vertices that can be identified therein through self-diagnosis, where every non-faulty vertex has at least g (≥ 1) non-faulty neighbours, in terms of both the PMC and the comparison diagnostic ((MM^*)) models. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

关键词： exchanged hypercube Fault tolerance property g-good-neighbour diagnosability generalized exchanged hypercube parallel and distributed computing

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Operator Placement for Efficient distributed Complex Event Processing in MANETs 11

Operator Placement for Efficient Distributed Complex Event P...

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11th IEEE International Conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Starks, Fabrice Plagemann, Thomas Peter Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9781467377010

Complex Event Processing (CEP) and Mobile Adhoc networks (MANETs) are two technologies that can be used to enable monitoring applications for Emergency and Rescue missions (ER). MANETs are characterized by energy limitations, and in-network processing or distributed CEP is one possible solution. Operator placement mechanism for distributed CEP has a direct impact on energy consumption. Existing operator placement mechanisms focus on static network topologies and are therefore inappropriate for MANETs scenarios. We propose a novel energy efficient decentralized distributed placement mechanism, designed to achieve fast convergence with minimal data transmission cost while achieving a near optimal placement assignment. We compare our decentralized placement mechanism with a centralized approach under different mobility scenarios. Furthermore, we evaluate the distributed CEP under different workload scenarios in order to gain additional insight into different performance characteristics of the system. Finally, we measure the impact of a simple placement replication scheme on the overall system performance in terms of delay and message overhead. Our decentralized placement mechanism achieves up to almost 50% lower message overhead compared to the centralized approach, and it has lower message overhead across different mobility scenarios compared to the centralized approach. The placement replication scheme achieves up to 51% lower delay compared to the decentralized placement mechanism with no replication.

关键词： Computer networks Mobile computing parallel and distributed computing Performance Wireless communications

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Unplugged parallelism for First-Year CS Majors 2022

Unplugged Parallelism for First-Year CS Majors

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53rd Annual ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE)

作者： Anthony, Barbara M. Erdil, D. Cenk Glebova, Olga Montante, Robert Southwestern Univ Georgetown 78626 TX Guyana Sacred Heart Univ Fairfield CT 06825 USA Georgia State Univ Atlanta GA 30302 USA Bloomsburg Univ Bloomsburg PA 17815 USA

ISBN: (纸本)9781450390705

We use "unplugged" activities to introduce parallel concepts in a first-year seminar for Computer Science majors. Student teams explore parallel approaches to computational tasks. Pre- and post-activity surveys, and a reflection paper, measure the impact of these activities on students' views about parallel programming. Our goal is to encourage parallel thinking about programming tasks before sequential approaches become ingrained. Computer Science curricula have traditionally focused on sequential approaches to programming, which were well matched to earlier computer systems. However, current systems almost all use multiprocessor CPUs, and are frequently used in clusters or networks of multiple computers. Recent curricular guidelines from organizations such as ACM and ABET recommend exposure to parallel computing concepts.

关键词： parallel and distributed computing unplugged activities computing education

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MRA*: parallel and distributed Path in Large-Scale Graph Using MapReduce-A* Based Approach 3rd

MRA*: Parallel and Distributed Path in Large-Scale Graph Usi...

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3rd International Symposium on Ubiquitous Networking (UNet)

作者： Hamilton Adoni, Wilfried Yves Nahhal, Tarik Aghezzaf, Brahim Elbyed, Abdeltif Hassan II Univ Fac Sci Casablanca LIMSAD Km 8 Route EI JadidaBP 5366 Casablanca 20100 Morocco

ISBN: (纸本)9783319681795;9783319681788

In this paper, we present a contribution for the Single Source Shortest Path Problem (SSSPP) in large-scale graph with A* algorithm. A* is one of the most efficient graph traversal algorithm because it is driven by a heuristic which determines the optimal path. A* approach is not efficient when the graph is too large to be processed due to exponential time complexity. We propose a MapReduce-based approach called MRA*: MapReduce-A* which consists to combine the A* algorithm with MapReduce paradigm to compute the shortest path in parallel and distributed environment. We perform experiments in a Hadoop multi-node cluster and our results prove that the proposed approach outperforms A* algorithm and reduces significantly the computational time.

关键词： A* algorithm Large-scale graph Shortest path problem Hadoop MapReduce parallel and distributed computing

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Extending heterogeneous applications to remote co-processors with rOpenCL 32

Extending heterogeneous applications to remote co-processors...

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32nd IEEE International Symposium on Computer Architecture and High-Performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core Architectures (WAMCA)

作者： Alves, Rui Rufino, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9781728199245

In heterogeneous computing systems, general purpose CPUs are coupled with co-processors of different architectures, like GPUs and FPGAs. Applications may take advantage of this heterogeneous device ensemble to accelerate execution. However, developing heterogeneous applications requires specific programming models, under which applications unfold into code components targeting different computing devices. OpenCL is one of the main programming models for heterogeneous applications, set apart from others due to its openness, vendor independence and support for different co-processors. In the original OpenCL application model, a heterogeneous application starts in a certain host node, and then resorts to the local co-processors attached to that host. Therefore, co-processors at other nodes, networked with the host node, are inaccessible and cannot be used to accelerate the application. rOpenCL (remote OpenCL) overcomes this limitation for a significant set of the OpenCL 1.2 API, offering OpenCL applications transparent access to remote devices through a TPC/IP based network. This paper presents the architecture and the most relevant implementation details of rOpenCL, together with the results of a preliminary set of reference benchmarks. These prove the stability of the current prototype and show that, in many scenarios, the network overhead is smaller than expected.

关键词： OpenCL heterogeneous computing API forwarding remote execution parallel and distributed computing

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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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distributed generative data mining

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7th Industrial Conference on Data Mining

作者： Ramos, Ruy Camacho, Rui LIACC Rua Ceuta 118-6 P-4050190 Oporto Portugal EEUP P-4200465 Oporto Portugal

ISBN: (纸本)9783540734345

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

关键词： data mining parallel and distributed computing inductive logic programming

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