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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11321 条记录，以下是3671-3680 订阅

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Choreography-Based Analysis of distributed Message Passing Programs

Choreography-Based Analysis of Distributed Message Passing P...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Ramsay Taylor Emilio Tuosto Neil Walkinshaw John Derrick Dept. of Computer Science University of Leicester

We report on the analysis of gen_server, a popular Erlang library to build client-server applications. Our analysis uses a tool based on choreographic models. We discuss how, once the library has been modelled in terms of communicating finite state machines, an automated analysis can be used to detect potential communication errors. The results of our analysis suggest how to properly use gen_server in order to guarantee the absence of communication errors.

关键词： Libraries System recovery Computer science Analytical models Computational modeling Protocols Message passing

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A comparative study on black-box testing with open source applications

A comparative study on black-box testing with open source ap...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Shaochun Xu Lichao Chen Chunning Wang Oleksandr Rud Algoma University Sault Ste Marie Canada Taiyuan University of Science and Technology Taiyuan China China Geological Library Beijing China

Although a lot of researches have been carried out to evaluate efficiency and effectiveness of different testing techniques there is still lacking of experimental work on functional testing, particularly for black-box testing on open-source software. In this work, four black-box testing techniques were chosen to test on two open source applications. The results have confirmed some conclusions which were obtained on closed-source software. In addition, a few new observations have also been obtained.

关键词： Testing Redundancy Complexity theory

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AutoPath: Harnessing parallel Execution Paths for Efficient Resource Allocation in Multi-stage Big Data Framerworks

AutoPath: Harnessing Parallel Execution Paths for Efficient ...

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international conference on Computer Communication and Networks

作者： Han Gao Zhengyu Yang Janki Bhimani Teng Wang Jiayin Wang Bo Sheng Ningfang Mi Dept. of Electrical & Computer Engineering Northeastern University Boston MA Dept. of Computer Science University of Massachusetts Boston Boston MA

Due to the flexibility of data operations and scalability of in-memory cache, Spark has revealed the potential to become the standard distributed framework to replace Hadoop for data-intensive processing in both industry and academia. However, we observe that the built-in scheduling algorithms in Spark (i.e., FIFO and FAIR) are not optimized for the applications with multiple parallel and independent branches in stages. Specifically, the child stage needs to wait and collect data from all its parent branches, but this wait has no guaranteed upper bound since it is tightly coupled with each branch's workload characteristic, stage order, and their corresponding allocated computing resource. To address this challenge, we investigate a superior solution which ensures all branches acquire suitable resources according to their workload demand in order to let the finish time of each branch be as close as possible. Based on this, we propose a novel scheduling policy, named AutoPath, which can effectively reduce the overall makespan of such kind of applications by detecting and leveraging the parallel path, and adaptively assigning computing resources based on the estimated workload demands during runtime. We implemented the new scheduling scheme in Spark v1.5.0 and evaluated it with selected representative workloads. The experiments demonstrate that our new scheduler effectively reduces the makespan and improves resource utilizations for these applications, compared to the current FIFO and FAIR schedulers.

关键词： Spark Scheduling Resource Management Task Assignment Workload Evaluation & Estimation

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Topic Modeling and Visualization for Big Data in Social Sciences 13

Topic Modeling and Visualization for Big Data in Social Scie...

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13th IEEE international conference on Ubiquitous Intelligence and Computing, 13th IEEE international conference on Advanced and Trusted Computing, 16th IEEE international conference on Scalable Computing and Communications, IEEE international conference on Cloud and Big Data Computing, IEEE international conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016

作者： Sukhija, Nitin Tatineni, Mahidhar Brown, Nicole Moer, Mark Van Rodriguez, Paul Callicott, Spencer Distributed Analytics and Security Institute Mississippi State University Mississippi StateMS39762 United States National Center for Supercomputing Applications IL United States San Diego Supercomputer Center University of California San Diego San Diego United States

ISBN: (纸本)9781509027705

Topic modeling is a widely used approach for analyzing large text collections. In particular, Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling approaches to aggregate vocabulary from a document corpus to form latent 'topics'. However, learning meaningful topic models with massive document collections which contain millions of documents, billions of tokens is challenging, given the complexity of the data involved, the difficulty in distributing the computation across multiple computing nodes. In recent years some data processing frameworks, such as Spark, Mallet, others have been developed to address the issues associated with analyzing large volumes of unlabeled text pertaining to various domains in a scalable, efficient manner. In this paper, we will present a preliminary case study demonstrating the scholarship achieved in the study of political consumerism via XSEDE resources. The experimental study will showcase the use of digitized social sciences data, text analytics toolkits to generate topic models, visualize topics for empowering intersectional research engaging the relationship between consumption, race, class, gender in the area of sociology. Consequently, this comparative big data textual analysis involving use of JSTOR data, LDA modeling toolkit's, visualization techniques, computational components is of paramount importance, especially for researchers from academic domain dealing with social science applications involving big data. © 2016 IEEE.

关键词： Visualization

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A General Purpose Branch and Bound parallel Algorithm

A General Purpose Branch and Bound Parallel Algorithm

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Alexandros C. Dimopoulos Christos Pavlatos George Papakonstantinou Harokopio University of Athens Tavros Greece Hellenic Air Force Academy Athens Greece National Technical University of Athens Zografou Greece

In this paper a parallel algorithm for branch and bound applications is proposed. The algorithm is a general purpose one and it can be used to parallelize effortlessly any sequential branch and bound style algorithm, that is written in a certain format. It is a distributed dynamic scheduling algorithm, i.e. each node schedules the load of its cores, it can be used with different programming platforms and architectures and is a hybrid algorithm (OpenMP, MPI). To prove its validity and efficiency the proposed algorithm has been implemented and tested with numerous examples in this paper that are described in detail. A speed-up of about 9 has been achieved for the tested examples, for a cluster of three nodes with four cores each.

关键词： Instruction sets Clustering algorithms Arrays Heuristic algorithms Partitioning algorithms parallel algorithms

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Optimized Belief Propagation Algorithm onto Embedded Multi and Many-Core Systems for Stereo Matching

Optimized Belief Propagation Algorithm onto Embedded Multi a...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Jean-François Nezan Alexandre Mercat Patrice Delmas Georgy Gimelfarb INSA Rennes UMR Rennes France The University of Auckland Auckland CBD New Zealand The University of Auckland Auckland Auckland NZ

Stereo matching techniques aim at reconstructing disparity maps from a pair of images. The use of stereo matching techniques in embedded systems is very challenging due to the complexity of the state-of-the-art algorithms. Local stereo matching algorithms are efficiently implemented on GPU and DSP. This paper presents the optimization of the One Dimension Belief Propagation (BP-1D) algorithm. BP-1D is faster than previous algorithms on monocore DSP and its implementation onto multicore DSPs is straightforward. BP-1D implemented on multicore embedded platforms out-performs previous stereo matching implementations reaching real-time performances for resolutions up to 1080p with a 10 Watts power consumption.

关键词： Digital signal processing Optimization Embedded systems VLIW Memory management Multicore processing

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GDSW: A General Framework for distributed Sliding Window over Data Streams

GDSW: A General Framework for Distributed Sliding Window ove...

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international conference on parallel and distributed Systems (ICPADS)

作者： Huan Chen Yijie Wang Yuan Wang Xingkong Ma Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P. R. China

ISBN: (纸本)9781509053827

The big data era is characterized by the emergence of live data with high volume and fast arrival rate, it poses a new challenge to stream processing applications: how to process the unbounded live data in real time with high throughput. The sliding window technique is widely used to handle the unbounded live data by storing the most recent history of streams. However, existing centralized solutions cannot satisfy the requirements for high processing capacity and low latency due to the single-node bottleneck. Moreover, existing studies on distributed windows primarily focus on specific operators, while a general framework for processing various window-based operators is wanted. In this paper, we firstly classify the window-based operators to two categories: data-independent operators and data-dependent operators. Then, we propose GDSW, a general framework for distributed count-based sliding window, which can handle both of data-independent and data-dependent operators. Besides, in order to balance system load, we further propose a dynamic load balance algorithm called DAD based on buffer usage. Our framework is implemented on Apache Storm 0.10.0. Extensive evaluation shows that GDSW can achieve sub-second latency, and 10X improvement in throughput compared with centralized processing, when processing rapid data rate or big size window.

关键词： distributed databases Storms parallel processing Semantics Sparks distributed processing Real-time systems

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12th international conference on Computing and Information Technology, IC2IT 2016

12th International Conference on Computing and Information T...

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12th international conference on Computing and Information Technology, IC2IT 2016

ISBN: (纸本)9783319404141

The proceedings contain 31 papers. The special focus in this conference is on User Centric Data Mining and Text processing. The topics include: User-triggered structural changes in OSN-alike distributed content networks;modeling disease spread at global mass gatherings;concept-based sentiment analysis for opinion texts with multiple-languages;automatic advisor for detecting summarizable chat conversations in online instant messages;optical character recognition for Nepali, english character and simple sketch using neural network;comparative results of attribute reduction techniques for Thai handwritten recognition with support vector machines;human action invarianceness for invarianceness using integration moment for human action recognition in video;parallel shallow water simulations by finite volume method with CUDA;intrusion detection system based on cost based support vector machine;predicting quality-assured consensual answers in community-based question answering systems;influence of ERP employment on work skills;use a simple set of features in QRS complex to identify individuals;the research on improving algorithms for hilltop to improve search quality;a fast outlier detection algorithm for big datasets;fast computing of microarray data using resilient distributed dataset of apache spark;heuristic search algorithms for constructing optimal latin hypercube designs;a hybrid multi-objective genetic algorithm with a new local search approach for solving the post enrolment based course timetabling problem;the adaptive biometrics authentication for accessing cloud computing services using iphone;an insurmountable and fail-secure network interface and finding an optimal parameter for threshold cryptography.

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Using Nested Graphs to Distribute parallel and distributed Multi-agent Systems

Using Nested Graphs to Distribute Parallel and Distributed M...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Alban Rousset Bénédicte Herrmann Christophe Lang Laurent Philippe Hadrien Bride Femto-ST Inst. Universite de Franche-Comte Besancon Franche-ComtÃ© FR Femto-ST Institute Univ. of Franche-Comte Besançon France

Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. In this context multi-agent systems are often used to model and simulate complex systems. Depending on the characteristics of the modelled system, methods used to represent the system may vary. Whatever the modelling techniques used, increasing the size and the precision of a model increases the amount of computation needed, requiring the use of parallel systems when it becomes too large. Usually, to efficiently run on parallel resources, the model must be adapted to be distributed. In this paper, we propose a new modelling approach, based on nested graphs, that allows the design of large, complex and multi-scale multi-agent models which can be efficiently distributed on parallel resources. A PDMAS (parallel and distributed Multi-Agent Platform) that supports this approach and efficiently run parallel multi-agent models is introduced.

关键词： Computational modeling Multi-agent systems Load modeling Program processors Adaptation models Context Context modeling

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Energy Efficient Scheduling of Real Time Signal processing applications through Combined DVFS and DPM

Energy Efficient Scheduling of Real Time Signal Processing A...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Erwan Nogues Maxime Pelcat Daniel Menard Alexandre Mercat INSA Rennes UEB 35000 Rennes FRANCE

This paper proposes a framework to design energy efficient signal processing systems. The energy efficiency is provided by combining Dynamic Frequency and Voltage Scaling (DVFS) and Dynamic Power Management (DPM). The framework is based on Synchronous Dataflow (SDF) modeling of signal processing applications. A transformation to a single rate form is performed to expose the application parallelism. An automated scheduling is then performed, minimizing the constraint of energy efficiency and providing DVFS and DPM decisions. This framework uses an architecture model including the number of available cores, the per-actor processing load and the energy per-cycle, derived from time and power measurements of modelled applications. After introducing the proposed framework, the energy characterization of *** SoC systems is described. A generic approach is presented to generate the energy model of a platform from power measurements as customized polynomials. Finally, the experimental results on a Samsung Exynos 5410 *** processor show that the energy optimal execution is not obtained by Linux governors that can execute either as-fast-as-possible or as-slow-as-possible. Instead, the most energy efficient scheduling is obtained by adapting both DVFS and DPM to application needs.

关键词： Mathematical model Clocks Power demand Linux Energy consumption Signal processing Power measurement

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