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检索条件"任意字段=10th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1767 条记录，以下是751-760 订阅

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Performance modeling of computation and communication tradeoffs in vertex-centric graph processing clusters

Performance modeling of computation and communication tradeo...

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international conference on Collaborative computing: Networking, applications and Worksharing (CollaborateCom)

作者： Amirreza Abdolrashidi Lakshmish Ramaswamy David Seamus Narron Department of Computer Science The University of Georgia

distributed vertex-centric graph processing systems have been recently proposed to perform different types of analytics on large graphs. these systems utilize the parallelism of shared nothing clusters. In this work we propose a novel model for the performance cost of such *** also define novel metrics related to the workload balance and network communication cost of clusters processing massive real graph datasets. We empirically investigate the effects of different graph partitioning mechanisms and their tradeoff for two different categories of graph processing algorithms.

关键词： Program processors Computational modeling Partitioning algorithms Clustering algorithms Synchronization parallel processing

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Dealing with Skewed Data in Structured Overlays Using Variable Hash Functions

Dealing with Skewed Data in Structured Overlays Using Variab...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Maeva Antoine Fabrice Huet University of Nice Sophia Antipolis Sophira Antipolis France

Storing highly skewed data in a distributed system has become a very frequent issue, in particular with the emergence of semantic Web and Big Data. this often leads to biased data dissemination among nodes. Addressing load imbalance is necessary, especially to minimize response time and avoid workload being handled by only one or few nodes. Our contribution aims at dynamically managing load imbalance by allowing multiple hash functions on different peers, while maintaining consistency of the overlay. Our experiments, on highly skewed data sets from the semantic web, show we can distribute data on at least 300 times more peers than when not using any load balancing strategy.

关键词： Peer-to-peer computing Resource description framework Load management Routing Network topology distributed databases

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distributed pseudo-random number generation and its application to cloud database

Distributed pseudo-random number generation and its applicat...

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10th international conference on Information Security Practice and Experience, ISPEC 2014

作者： Chen, Jiageng Miyaji, Atsuko Su, Chunhua School of Information Science Japan Advanced Institute of Science and Technology Japan

ISBN: (纸本)9783319063195

Cloud database is now a rapidly growing trend in cloud computing market recently. It enables the clients run their computation on out-sourcing databases or access to some distributed database service on the cloud. At the same time, the security and privacy concerns is major challenge for cloud database to continue growing. To enhance the security and privacy of the cloud database technology, the pseudo-random number generation (PRNG) plays an important roles in data encryptions and privacy-preserving data processing as solutions. In this paper, we focus on the security and privacy risks in cloud database and provide a solution for the clients who want to generate the pseudo-random number collaboratively in a distributed way which can be reasonably secure, fast and low cost to meet requirement of cloud database. We provide two solutions in this paper, the first one is a construction of distributed PRNG which is faster than the traditional Linux PRNG. the second one is a protocol for users to execute the random data perturbation collaboratively before uploading the data to the cloud database. © 2014 Springer international Publishing.

关键词： Random number generation

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Bandwidth Modeling in Large distributed Systems for Big Data applications

Bandwidth Modeling in Large Distributed Systems for Big Data...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Bahman Javadi Boyu Zhang Michela Taufer School of Computing Engineering and Mathematics University of Western Sydney Australia Dept. of Computer and Information Sciences University of Delaware USA

the emergence of Big Data applications provides new challenges in data management such as processing and movement of masses of data. Volunteer computing has proven itself as a distributed paradigm that can fully support Big Data generation. this paradigm uses a large number of heterogeneous and unreliable Internet-connected hosts to provide Peta-scale computing power for scientific projects. With the increase in data size and number of devices that can potentially join a volunteer computing project, the host bandwidth can become a main hindrance to the analysis of the data generated by these projects, especially if the analysis is a concurrent approach based on either in-situ or in-transit processing. In this paper, we propose a bandwidth model for volunteer computing projects based on the real trace data taken from the Docking@Home project with more than 280,000 hosts over a 5-year period. We validate the proposed statistical model using model-based and simulation-based techniques. Our modeling provides us with valuable insights on the concurrent integration of data generation with in-situ and in-transit analysis in the volunteer computing paradigm.

关键词： Bandwidth Computational modeling Servers Computer applications distributed processing Data models Predictive models

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Cloud Data Management for Scientific Workflows: Research Issues, Methodologies, and State-of-the-Art

Cloud Data Management for Scientific Workflows: Research Iss...

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international conference on Semantics, Knowledge and Grid (SKG)

作者： Dong Yuan Lizhen Cui Xiao Liu School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Computer Science and Technology Shandong University Jinan China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9781479967162

Data-intensive scientific applications are posing many challenges in distributed computing systems. In the scientific field, the application data are expected to double every year over the next decade and further. With this continuing data explosion, high performance computing systems are needed to store and process data efficiently, and workflow technologies are facilitated to automate these scientific applications. Scientific workflows are typically very complex. they usually have a large number of tasks and need a long time for execution. Running scientific workflow applications usually need not only high performance computing resources but also massive storage. the emergence of cloud computing technologies offers a new way to develop scientific workflow systems. Scientists can upload their data and launch their applications on the scientific cloud workflow systems from everywhere in the world via the Internet, and they only need to pay for the resources that they use for their applications. As all the data are managed in the cloud, it is easy to share data among scientists. this kind of model is very convenient for users, but remains a big challenge to the system. this paper proposes several research topics of data management in scientific cloud workflow systems, and discusses their research methodologies and state-of-the-art solutions.

关键词： Cloud computing Computational modeling Data models distributed databases High performance computing Hardware Runtime

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Sparse Matrix-Vector Multiplication: A Data Mapping-Based Architecture

Sparse Matrix-Vector Multiplication: A Data Mapping-Based Ar...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Ahmad Mansour Jürgen Götze Wei-Chun Hsu Shanq-Jang Ruan Information Processing Lab TU Dortmund Dortmund Germany Microsystems Lab Low Power Systems Design Group National Taiwan University of Science and Technology Taipei Taiwan

the performance of the sparse matrix-vector multiplication (SMVM) on a parallel system is strongly affected by the distribution of data among its components. Two costs arise as a result of the used data mapping method: arithmetic and communication. the communication cost often dominates the arithmetic cost, and the gap between these costs tends to increase. therefore, finding a mapping method that reduces the communication cost is of high importance. On the other hand, the load distribution among the processing units must not be sacrificed. In this paper, a data mapping method is proposed for SMVM on Network-on-Chip which achieves balanced working load and reduces the communication cost. Afterwards, an FPGA-based architecture is introduced which is designed to fit with the proposed data mapping method.

关键词： Sparse matrices Computer architecture Finite element analysis Program processors Routing distributed databases Electronic mail

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An improved GPU MapReduce framework for data intensive applications

An improved GPU MapReduce framework for data intensive appli...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Razvan Nitu Elena Apostol Valentin Cristea Faculty of Automatic Control and Computers University Politehnica of Bucharest

the MapReduce paradigm is one of the best solutions for implementing distributed applications which perform intensive data processing. In terms of performance regarding this type of applications, MapReduce can be improved by adding GPU capabilities. In this context, the GPU clusters for large scale computing can bring a considerable increase in the efficiency and speedup of data intensive applications. In this article we present a framework for executing MapReduce using GPU programming. We describe several improvements to the concept of GPU MapReduce and we compare our solution with others.

关键词： Graphics processing units Mars Process control Kernel Memory management parallel processing Vectors

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A Novel Density Based Clustering Algorithm and its parallelization

A Novel Density Based Clustering Algorithm and its Paralleli...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Xiaokang Li Binbin Yu Yinghua Zhou Guangzhong Sun School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

K-Means, a simple but effective clustering algorithm, is widely used in data mining, machine learning and computer vision community. K-Means algorithm consists of initialization of cluster centers and iteration. the initial cluster centers have a great impact on cluster result and algorithm efficiency. More appropriate initial centers of k-Means can get closer to the optimum solution, and even much quicker convergence. In this paper, we propose a novel clustering algorithm, Kmms, which is the abbreviation of k-Means and Mean Shift. It is a density based algorithm. Experiments show our algorithm not only costs less initialization time compared with other density based algorithms, but also achieves better clustering quality and higher efficiency. And compared with the popular k-Means++ algorithm, our method gets comparable accuracy, mostly even better. Furthermore, we parallelize Kmms algorithm based on OPenMP from both initialization and iteration step and prove the convergence of the algorithm.

关键词： distributed computing

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TOUGH2-PETSc: A parallel Solver for TOUGH2

TOUGH2-PETSc: A Parallel Solver for TOUGH2

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Daniel Hathorn Yushu Wu Zizhong Chen Colorado School of Mines Golden CO USA University of California Riverside CA USA

TOUGH2 is a general-purpose numerical simulation program for multi-dimensional, multiphase, multicomponent fluid flows, heat transfer and contaminant transport in porous and fractured media. It has been used worldwide for geothermal reservoir engineering, nuclear waste isolation, environmental assessment and remediation, and modeling flow and transport in variably saturated media. TOUGH2 is very computationally intense, and the accuracy and scope of the simulation is limited by the amount of processing power available on a single computer. this makes it an ideal canadate for parallel computing, as more CPU power and memory is available. Furthermore, TOUGH2's main computational unit is a linear equation solver. In parallel computing, a lot of effort has been spent to develop highly efficient parallel linear equation solvers. In this paper, we present TOUGH2-PETSc, a parallel implementation of TOUGH2 that uses PETSc to solve the linear systems in TOUGH2. PETSc is a library of high-performance linear and non-linear equation solvers that has been throughly tested at scale. Based on TOUGH2 and PETSc, TOUGH2-PETSc gives TOUGH2 users the potential to perform larger scale and higher resolution simulations. Experimental results demonstrate that the parallel TOUGH2-PETSc shows improved performance over the sequential version.

关键词： Mathematical model Water heating Earth Observing System Algorithms Sparse matrices Convergence Reservoirs

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Implementation of P systems by using big data technologies

Implementation of P systems by using big data technologies

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14th international conference on Membrane computing, CMC 2013

作者： Ciobanu, Alex Ipate, Florentin Department of Computer Science Faculty of Mathematics and Computer Science University of Pitesti Str. Targu din Vale 1 110040 Pitesti Romania Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Str. Academiei nr.14 sector 1 Bucharest Romania

ISBN: (纸本)9783642542381

Due to their inherent parallel and non-deterministic nature, P system implementations require vast computing and storage resources. this significantly limits their applications, even more so when the calculation of all possible evolutions of the P system is required. this article exposes the scalability possibilities available with the Big Data ecosystem for P systems implementations, using Map Reduce parallelism to build the P system computation tree. the Hadoop based implementation is then used for generating test suites for cell like P systems, in particular for context-dependent rule coverage testing. Our preliminary evaluations on a benchmark of automatically generated P systems confirm that the proposed approach scales well. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Big data

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