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检索条件"主题词=Parallel and distributed processing"

共 69 条记录，以下是41-50 订阅

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Effectively Handling New Relationship Formations in Closeness Centrality Analysis of Social Networks using Anytime Anywhere Methodology 6

Effectively Handling New Relationship Formations in Closenes...

引用

2016 IEEE International Conferences on BDCloud/SocialCom/SustainCom 2016

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781509039364

The flood of real time social data, generated by various social media applications and sensors, is enabling researchers to gain critical insights into important social modeling and analysis problems such as the evolution of social relationships and analysis of emergent social processes. However, current computational tools have to address the grand challenge of analyzing large and dynamic social networks within strict time constraints before the available social data can be effectively utilized. The computational issues are further exacerbated by the network size, which can range in the millions of nodes, and by the need for analytical tools to work with various computational architectures. Existing methodologies primarily deal with dynamic relationships in social networks by simply re-computing the results, and relying on massive parallel and distributed processing resources to maintain time constraints. In previous work, we introduced an overarching parallel/distributed algorithm design framework called the anytime anywhere framework, which leverages the inherent iterative property of graph algorithms to generate partial results, whose quality increase with the processing time, and which efficiently incorporates network changes. In this paper, we focus on closeness centrality algorithm design for dynamic social networks where new relationships are formed due to edge additions. Using both theoretical analysis and empirical results, we will demonstrate how this algorithm efficiently reuses the partial results and reduces the need for re-computations.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime algorithms

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Efficient Anytime Anywhere Algorithms for Vertex Additions in Large and Dynamic Graphs 31

Efficient Anytime Anywhere Algorithms for Vertex Additions i...

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31st IEEE International parallel and distributed processing Symposium Workshops (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9780769561493

Over the past decade, there has been a dramatic increase in the availability of large and dynamic social network datasets. Conducting social network analysis (SNA) on these networks is critical for understanding underlying social phenomena. However, continuously evolving graph structures require massive recomputations and conducting SNA is infeasible if the computations have to be restarted for every change. Many recent works have proposed large-scale graph processing systems and frameworks, but less attention has been given to scalable SNA algorithm designs that can efficiently adapt to dynamic graph changes. Moreover, continuously adapting to dynamic graph changes such as node/vertex/actor additions/deletions in a parallel/distributed computational environment can skew the initial graph partitions, leading to load imbalance issues and performance degradation. Previous approaches that focus on computing SNA measures on dynamic graphs either ignore this critical load-balancing aspect or focus only on measures that are straightforward and inherently adjustable to changes in the graph topology. In this work, we have designed an anytime anywhere closeness centrality algorithm that can efficiently incorporate vertex additions while avoiding massive recomputations, by leveraging a generic framework for designing parallel/distributed algorithms called anytime anywhere. Furthermore, we have also performed an analysis of the effectiveness of various processor assignment strategies to mitigate the load imbalances caused by dynamic graph changes.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime anywhere algorithms dynamic vertex addition

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Diffusely distributed parallelization of MOEA/D with Edge Weight Vectors Sharing

Diffusely Distributed Parallelization of MOEA/D with Edge We...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Midtlyng, Mads Sato, Mikiko Hosei Univ Fac Comp & Informat Sci Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan

ISBN: (纸本)9798400701207

This paper proposes the partitioning method with edge weight vectors sharing for parallel distributed MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the weight vector in the objective function space, the T-neighborhood is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a partitioning method to share edge weight vectors among all partitions, and then assign other weight vectors uniformly to each partition. Using the constrained knapsack problem as a benchmark problem, we show that it is possible to eliminate the need for migration processing to correct the value of the ideal point and improve the problem that the T-neighborhood is divided, and the distribution of the solution becomes sparse.

关键词： MOEA/D parallel and distributed processing distributed memory environment multi-objective evolutionary algorithms

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A hierarchic hybrid scheduling algorithm for static task with precedence constraints 15

A hierarchic hybrid scheduling algorithm for static task wit...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on parallel and distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Xie, Yuanyuan Wu, Jiang Liu, Fengzhu Northwest Univ Xian Coll Informat Sci & Technol Xian 710127 Peoples R China

ISBN: (纸本)9781509032051

In distributed computing, the task scheduling problem is an NP-hard problem. Satisfying the task dependencies strictly and minimizing the execution time are the desired aims of the task scheduling algorithm. However, most scheduling algorithms focuses on minimizing the execution time without a clear strategy to preserve the precedence constraints. In this paper, a new two-phase algorithm called Hierarchic Hybrid Heuristic-Genetic Scheduling (H3GS) is introduced and developed for heterogeneous distributed computing systems (HeDCSs). In the first phase, the paper proposes a improvement list-based heuristic algorithm, called H2EFT, and the main principle of the algorithm is to divide the Directed Acyclic Graph (DAG) into levels, which simplifies the task dependencies and preserves the precedence constraints of tasks. The second phase implements a hierarchic genetic algorithm, called HGAS, which proceeds to evolve shorter schedules by inserting the H2EFT schedule into the initial populations and repairing the invalid schedule on the basis of the DAG hierarchy. Consequently, the H3GS algorithm delivers high quality solution in reasonable computing time based on a hierarchical DAG. The comparison performance results show that H3GS has outperformed the HEFT, H2EFT, and H2GS algorithms both in efficiency, complexity, and quality.

关键词： task scheduling task dependencies hierarchical division parallel and distributed processing heterogeneous systems

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Scheduling Stochastic Tasks with Precedence Constrain on Cluster Systems with Heterogenous Communication Architecture 15th

Scheduling Stochastic Tasks with Precedence Constrain on Clu...

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15th International Conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Liao, Qun Jiang, Shuangshuang Hei, Qiaoxiang Li, Tao Yang, Yulu Nankai Univ Coll Comp & Control Engn Tianjin 300353 Peoples R China

ISBN: (纸本)9783319271613;9783319271606

Scheduling precedence constrained stochastic tasks on heterogenous cluster systems is an important issue which impact the performance of clusters significantly. Different with deterministic tasks, stochastic task model assumes that the workload of task and quantity of data transmission between tasks are stochastic variables, which is more realistic than other task models. Scheduling model and algorithms of precedence constrained stochastic tasks attract a large number of researchers' attention recently. An algorithm SDLS (Stochastic Dynamic Level Scheduling) has been proved performing well in scheduling stochastic tasks on heterogenous clusters. However, the assumption about communication time between tasks in SDLS is much simpler than its assumptions about task computing time, which makes it cannot depict the communication cost among heterogenous links well. In this paper, it is assumed that the quantity of data communication between tasks is a stochastic variable of normal distribution, instead of assuming communication time among heterogenous links a same stochastic variable immediately. Moreover, a modified scheduling model and algorithm SDLS-HC (Stochastic Dynamic Level Scheduling on Heterogenous Communication links) are proposed. Work in this paper focus on considering much more detailed communication cost in task scheduling based on SDLS. Evaluation on many random generated tasks experiments demonstrates that SDLS-HC achieves better performance than SDLS on cluster systems with heterogenous links.

关键词： Stochastic tasks scheduling Directed acyclic graph Heterogenous clusters parallel and distributed processing distributed system

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Handling Vertex Deletions in Memory Scalable Anytime Anywhere Algorithms for Large and Dynamic Social Networks 32

Handling Vertex Deletions in Memory Scalable Anytime Anywher...

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32nd IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781538655559

Due to the dramatic increase in the availability of dynamic data in various domains, including computational social systems, there is a need to formulate processing and analysis methodologies that can efficiently incorporate data changes while reducing recomputations. This is especially critical for network analysis techniques where current methodologies pursue strategies based on maintaining intermediate or partial results. However, there are critical trade-offs with respect to memory and processing time overheads that have prevented these designs from scaling with larger network sizes and higher rates of network dynamism. In this work, we demonstrate the capability of our anytime anywhere framework to formulate closeness centrality algorithms that can adapt to memory availability by varying the number of partial results that are stored and maintained over the course of the analysis. Additionally, using a combination of theoretical analysis and experimental results we compare the performance of these algorithm designs, and identify conditions under which one design performs better than the others.

关键词： social network analysis parallel and distributed processing memory scalability centrality analysis dynamic graphs anytime anywhere algorithms

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Improving MapReduce Performance with Progress and Feedback based Speculative Execution 2

Improving MapReduce Performance with Progress and Feedback b...

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2nd IEEE International Conference on Smart Cloud (SmartCloud)

作者： Ibrahim, Ibrahim Adel Bassiouni, Mostafa Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781538636848

Task stragglers dramatically impede parallel job execution of data-intensive computing in Cloud Datacenters Due to the uneven distribution of input data resulted from heterogeneous data nodes, resource contention situations, and network configurations, it causes delay failures due to the violation of job completion time. However, data-intensive computing frameworks, such as MapReduce or Hadoop, employ a mechanism called speculative execution to deal with the straggler issue, speculative execution provide limited effectiveness because in many cases straggler identification occurs too late within a job lifecycle. Identifying the straggler and the timing of identifying it is very important for Straggler mitigation in Data-intensive cloud computing. Speculative execution method is a widely adopted as a straggler identification and mitigation scheme but it has certain inherent limitations. In this paper, we strive to make Hadoop more efficient in cloud environments. We present Progress and Feedback based Speculative Execution Algorithm (PFSE), a new Straggler identification scheme to identify the straggler MapReduce tasks based on the feedback information received from completed tasks beside the progress of the currently processing task, our extensive simulation shows that PFSE can outperform the dynamic scheduling techniques like Self-Learning MapReduce scheduler (SLM) and LATE. PFSE can assist in enhancing straggler Identification and mitigation for tolerating late-timing failures within data intensive cloud computing.

关键词： Cloud Computing MapReduce: Data-Intensive Computing parallel and distributed processing Straggler Identification Speculative Execution

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Cloud Platform using Big Data and HPC Technologies for distributed and parallels Treatments 9

Cloud Platform using Big Data and HPC Technologies for Distr...

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9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Debauche, Olivier Mahmoudi, Sidi Ahmed Mahmoudi, Said Manneback, Pierre Univ Mons Fac Engn Comp Sci Unit 20 Pl Parc B-7000 Mons Belgium Univ Liege ULiege Gembloux Agrobio Tech Biosyst Engn Dept Biosyst Dynam & Exchanges Axis Passage Deportes 2 B-5030 Gembloux Belgium

Smart agriculture is one of the most diverse research. In addition, the quantity of data to be stored and the choice of the most efficient algorithms to process are significant elements in this field. The storage of collecting data from Internet of Things (IoT), existing on distributed, local databases and open data need a particular infrastructure to federate all these data to make complex treatments. The storage of this wide range of data that comes at high frequency and variable throughput is particularly difficult. In this paper, we propose the use of distributed databases and high-performance computing architecture in order to exploit multiple re-configurable computing and application specific processing such as CPUs, GPUs, TPUs and FPGAs efficiently. This exploitation allows an accurate training for an application to machine learning, deep learning and unsupervised modeling algorithms. The last ones are used for training supervised algorithms on images when it labels a set of images and unsupervised algorithms on IoT data which are unlabeled with variable qualities. The processing of data is based on Hadoop 3.1 MapReduce to achieve parallel processing and use containerization technologies to distribute treatments on Multi GPU, MIC and FPGA. This architecture allows efficient treatments of data coming from several sources with a cloud high-performance heterogeneous architecture. The proposed 4 layers infrastructure can also implement FPGA and MIC which are now natively supported by recent version of Hadoop. Moreover, with the advent of new technologies like Intel (R) Movidius (TM);it is now possible to deploy CNN at the Fog level in the IoT network and to make inference with the cloud and therefore limit significantly the network traffic that result in reducing the move of large amounts of data to the cloud. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： GPU FPGA MIC CPU TPU Cloud Big Data parallel and distributed processing heterogeneous cloud architecture

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Dependability Evaluation of parallel Differential Evolutionary Particle Swarm Optimization for Online Optimal Operational Planning of Energy Plants

Dependability Evaluation of Parallel Differential Evolutiona...

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IEEE Region 10 Conference (TENCON)

作者： Nishimura, Norihiro Fukuyama, Yoshikazu Matsui, Tetsuro Meiji Univ Sch Interdisciplinary Math Sci Tokyo Japan Fuji Elect Co Ltd Corp R&D Headquarters Tokyo Japan

ISBN: (纸本)9781509025978

This paper evaluates speed-up and dependability of parallel differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The planning can be formulated as a mixed integer nonlinear optimization problem (MINLP). When optimal operational planning of numbers of energy plants are calculated simultaneously in a data center, it is required to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes and countermeasures for various faults of the processes should be considered. On-line optimal operational planning requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping quality of solutions are required even if some of the calculation results cannot be returned from distributed processes. Using the proposed parallel DEEPSO based method, it is observed that calculation time becomes about 3 times faster than a sequential calculation, and high quality of solutions can be kept even with high fault probabilities.

关键词： Differential Evolutionary Particle Swarm Optimization Dependability Energy Management System Optimal Operational Planning of Energy Plant parallel and distributed processing

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LUC: Limiting the Unintended Consequences of power scaling on parallel transaction-oriented workloads 29

LUC: Limiting the Unintended Consequences of power scaling o...

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29th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Chang, Hung-Ching Li, Bo Back, Godmar Butt, Ali R. Cameron, Kirk W. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9781479986484

Following an exhaustive set of experiments, we identify slowdowns in I/O performance that occur when processor power and frequency are increased. Our initial analyses indicate slowdowns are more likely to occur and more acute when the number of parallel I/O threads increases and the variability between runs is high. We use a microbenchmark-driven methodology to simplify isolation of the root causes of I/O performance loss. We classify the observed performance loss into two categories: file synchronization and file write delays. We introduce LUC, a runtime system to Limit the Unintended Consequences of power scaling and dynamically improve I/O performance. We demonstrate the effectiveness of the LUC system running on two platforms for two critical parallel transaction-oriented workloads including a mail server (varMail) and online transaction processing (oltp).

关键词： parallel and distributed processing I/O performance power runtime systems

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