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

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Rewriting complex SPARQL analytical queries for efficient cloud-based processing

Rewriting complex SPARQL analytical queries for efficient cl...

引用

IEEE international conference on Big Data

作者： Padmashree Ravindra HyeongSik Kim Kemafor Anyanwu Microsoft Corporation Redmond USA North Carolina State University Raleigh USA

Many emerging Semantic Web applications combine and aggregate data across domains for analysis. Such analytical queries compute aggregates over multiple groupings of data, resulting in query plans with complex grouping-aggregation constraints. In the context of an RDF analytical query, each such grouping maps to a graph pattern subquery with multiple join operations, and related groups often result in overlapping graph patterns within the same query. In this paper, we propose a holistic approach to optimize RDF analytical queries by refactoring queries to achieve shared execution of common subexpressions that enables parallel evaluation of groupings as well as aggregations. Such a rewriting enables shorter execution workflows, particularly beneficial for scale-out processing on distributed Cloud systems with multiple I/O phases. Experiments on real-world and synthetic benchmarks confirm that such a rewriting can achieve more efficient execution plans when compared to relational-style SPARQL query plans executed on popular Cloud systems.

关键词： Resource description framework Pattern matching Optimized production technology Electronic mail Aggregates Context

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Stock market prediction using Hadoop Map-Reduce ecosystem

Stock market prediction using Hadoop Map-Reduce ecosystem

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Arun Kumar Dubey Vanita Jain A.P Mittal Bharati VidyaPeeth's College of Engineering New Delhi BharatiVidyaPeeth's College of Engineering New Delhi NSIT Delhi

Now a day's Academia and Industry people are working on large amount of data, in petabyte, and they are using technique of Map Reduce for data analysis. The input for such framework is very large and main requirement for theses inputs are that all the files cannot be kept on single node. After putting all data on single machine, we have to process it parellely. Hadoop is a framework which enables applications to work on large amounts of data on clusters with thousands of nodes. A distributed file system (HDFS) stores the data on these nodes, enabling a high bandwidth across the cluster. Hadoop also implements a parallel computational algorithm, MapReduce, which divides the main task into small chunks and these work in parallel known as mapping, and all the results are combined into a final output, the reduce stage. This paper is based on Hadoop Based Stock forecasting using neural networks. Stock Market has high profit and high risk features thts why its prediction must be in the parallel of accuracy, the main issue about such data are , these are very complex nonlinear function and can only be learnt by a data mining method such as neural networks to recognize future market trend. This project focuses mainly on learning of feed-forward artificial neural network (ANN) on a hadoop framework. We have tried to utilize distributing capability of Hadoop ecosystem which is parallel too. Map-Reduce for managing training of large datasets on the neural network. Our experimental results basically show the speedup achieved by increasing number of processors to the hadoop cluster for an artificial neural network.

关键词： Training Biological neural networks parallel processing File systems distributed databases Indexes

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LSRB-CSR: A Low Overhead Storage Format for SpMV on the GPU Systems

LSRB-CSR: A Low Overhead Storage Format for SpMV on the GPU ...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Lifeng Liu Meilin Liu Chongjun Wang Jun Wang Department of Computer Science and Engineering Wright State University Dayton OH USA Nanjing University Nanjing China University of Central Florida Orlando FL USA

Sparse matrix vector multiplication (SpMV) is a basic building block of many scientific applications. Several GPU accelerated SpMV algorithms for the CSR format suffer from workload unbalance for irregular matrices. In this paper, we propose a new auxiliary array assisted CSR format called local segmented reduction based CSR (LSRB-CSR), which enables synchronization free preprocessing and efficient SpMV algorithm with the light weight auxiliary arrays. It is efficient for both regular matrices and irregular matrices with tiny preprocessing overhead. We compare our LSRB-CSR based SpMV algorithm with the CSR-based SpMV from cuSPARSE, the SpMV algorithm based on segmented reduction adopted by CUDPP library, and the CSR5-based SpMV algorithm for both regular and irregular sparse matrices. Compared to cuSparse, our LSRB-CSR based SpMV algorithm could improve the performance by 26% on regular matrices and up to 4750% on irregular matrices. Compared to CUDPP, our LSRB-CSR based SpMV algorithm could improve the average SpMV performance by 210% on regular matrices and 250% on irregular matrices. Our LSRB-CSR based SpMV algorithm has comparable performance as the CSR5 based SpMV algorithm for regular matrices, and achieves better performance over the CSR5 based SpMV algorithm for irregular matrices. Experimental results show that the conversion overhead from the CSR to the LSRB-CSR is only 1/10 of the overhead from the CSR to the CSR5 on average.

关键词： Arrays Graphics processing units Sparse matrices Synchronization Instruction sets Libraries Acceleration

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Supporting Elasticity in OpenMP applications

Supporting Elasticity in OpenMP Applications

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22nd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Galante, Guilherme Bona, Luis C. E. Univ Fed Parana Dept Informat BR-80060000 Curitiba Parana Brazil

ISBN: (纸本)9781479927289

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. To explore this feature, several works addressed the development of frameworks and platforms focusing the construction of elastic parallel and distributed applications for IaaS clouds. However, none of these works addressed the exploration of elasticity in multithreaded applications. In this paper, we propose a mechanism to provide elasticity support for OpenMP applications, making possible the dynamic provisioning of cloud resources taking into account the program structure and runtime requirements. In our proposal, the OpenMP directives were modified to support the dynamic adjustment of resources and a set of routines were included to the user-level library to enable the configuration of the elastic execution. Dynamic memory allocation support was also included in elastic OpenMP library. We also present the architecture and implementation of the proposed mechanism. The experiments validate our approach and show some possibilities to use the elastic OpenMP.

关键词： Cloud computing OpenMP elasticity parallel applications

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Robust grayscale watermarking technique based on face detection

Robust grayscale watermarking technique based on face detect...

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IEEE Signal processing and Communications applications (SIU)

作者： Lauri Laur Morteza Daneshmand Mary Agoyi Gholamreza Anbarjafari Institute of Computer Science University of Tartu Tartu Estonia iCV Group University of Tartu Tartu Estonia Faculty of Engineering Cyprus International University Turkey

Due to increase of usage of digital media distributed over the Internet, concerns about security and piracy have emerged. The amount of digital media reproduction has brought a need for content watermarking. In this paper robust grayscale watermarking technique based on face detection is proposed. Face detection algorithm is used to find a face on host image and this part of image is transformed into frequency domain using Discrete Wavelet Transform. Chirp z-transform is applied on low-frequency subband from previous step and LU decomposition is used on the outcome. Diagonal matrix from LU decomposition is further decomposed using Singular Value Decomposition and watermark is embedded into singular values. Numerous experiments are run on that algorithm and results are compared with novel and state-of-the-art techniques. The results show that proposed method has good imperceptibility and robustness characteristics.

关键词： Watermarking Robustness Chirp Cameras Face Transform coding Discrete wavelet transforms

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IC-Data: Improving Compressed Data processing in Hadoop

IC-Data: Improving Compressed Data Processing in Hadoop

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international conference on High Performance Computing

作者： Adnan Haider Xi Yang Ning Liu Xian-He Sun Shuibing He Computer Science Department Illinois Institute of Technology Chicago United States of America Computer School Wuhan University Wuhan China

ISBN: (纸本)9781467384872

As dataset sizes for data analytic applications and scientific applications running on Hadoop increases, data compression has become essential to store this data within a reasonable storage cost. Although data is often stored compressed, currently Hadoop takes 49% longer to process compressed data compared to uncompressed data. processing compressed data reduces the amount of task parallelism and creates uneven workload distribution both of which are fundamental issues the MapReduce parallel programming paradigm should alleviate. In this paper, we propose the design and implementation of a Network Overlapped Compression scheme, NOC, and Compression Aware Storage scheme, CAS. NOC reduces data load time and hides compression overhead by interleaving network I/O with compression. CAS increases parallelism by dynamically changing a file's block size based on compression ratio. Additionally, we develop a MapReduce Module which recognizes the characteristics of compressed data to improve resource allocation and load balance. Collectively, NOC, CAS, and the MapReduce Module decrease job execution time on average by 66% and data load time by 31%.

关键词： Data compression Codecs Runtime distributed databases Data analysis Facebook

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FuzzyGPU: a fuzzy arithmetic library for GPU

FuzzyGPU: a fuzzy arithmetic library for GPU

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22nd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Defour, David Marin, Manuel Univ Perpignan DALI F-66860 Perpignan France Univ Montpellier 2 LIRMM UMR 5506 F-34095 Montpellier France CNRS LIRMM UMR 5506 F-34095 Montpellier France

ISBN: (纸本)9781479927289

Data are traditionally represented using native format such as integer or floating-point numbers in various flavor. However, some applications rely on more complex representation format. This is the case when uncertainty needs to be apprehended. Fuzzy arithmetic is one of the major tools to address this problem, but the execution time of basic operations such as addition or multiplication makes its usage prohibitive. In this article, thanks to a new representation format and modern GPU characteristics we show that it is possible to greatly reduce the execution time of those operations. These techniques have been implemented in fuzzyGPU, a freely distributed library of common operations over fuzzy number.

关键词： floating point arithmetic fuzzy set theory graphics processing units number theory addition operation execution time reduction floating-point numbers fuzzy arithmetic library fuzzy number fuzzyGPU integer numbers multiplication operation representation format uncertainty management usage prohibitive Encoding Graphics processing units Kernel Libraries Memory management Uncertainty

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distributed Beamforming Relay Selection to Increase Base Station Anonymity in Wireless Ad Hoc Networks

Distributed Beamforming Relay Selection to Increase Base Sta...

引用

international conference on Computer Communications and Networks (ICCCN)

作者： Jon R. Ward Mohamed Younis Department of Computer Science and Electrical Engineering University of Maryland Baltimore County

Wireless ad hoc networks have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, nodes act as data sources and forward information to a central base station (BS) that may also perform network management tasks. The critical role of the BS makes it a target for an adversary's attack. Even if an ad hoc network employs conventional security primitives such as encryption and authentication, an adversary can apply traffic analysis techniques to find the BS. Therefore, the BS should be kept anonymous to protect its identity, role, and location. Previous work has demonstrated distributed beamforming to be an effective technique to boost BS anonymity in wireless ad hoc networks; however, the increased anonymity and corresponding energy consumption depend on the quality and quantity of selected helper relays. In this paper we present a novel, distributed approach for determining a set of relays per hop that boosts BS anonymity using evidence theory analysis while minimizing energy consumption. The identified relay set is further prioritized using local wireless channel statistics. The simulation results demonstrate the effectiveness our approach.

关键词： Relays Array signal processing Protocols Synchronization Mobile ad hoc networks Wireless communication

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HPC applications deployment on distributed heterogeneous computing platforms via OMF, OML and P2PDC

HPC Applications deployment on distributed heterogeneous com...

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22nd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： El Baz, Didier The Tung Nguyen Jourjon, Guillaume Rakotoarivelo, Thierry CNRS LAAS F-31077 Toulouse France Univ Toulouse Toulouse France NICTA Eveleigh NSW Australia

ISBN: (纸本)9781479927289

A new tool and web portal are presented for deployment of High Performance Computing applications on distributed heterogeneous computing platforms. This tool relies on the decentralized environment P2PDC and the OMF and OML multithreaded control, instrumentation and measurement libraries. Deployment on PlanetLab of a numerical simulation application is studied. A first series of computational results is displayed and analyzed.

关键词： component HPC heterogeneous computing peer-to-peer computing application deployment PlanetLab numerical simulation asynchronous iterations

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Marriage Between Coordinated and Uncoordinated Checkpointing for the Exascale Era

Marriage Between Coordinated and Uncoordinated Checkpointing...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Omer Subasi Ferad Zyulkyarov Osman Unsal Jesus Labarta Barcelona Supercomputing Center Universitat Politecnica de Catalunya

The state-of-the-art checkpointing techniques are projected to be prohibitively expensive in the Exascale era. These techniques are most often holistic in nature which prevents them to leverage programming model and paradigm specific advantages so as to be viable for the Exascale era. In this work, we present a unified non-hierarchical model to combine uncoordinated checkpointing with coordinated system-wide checkpointing to capitalize on programming model specific advantages. We develop closed-form formulas for performance improvement and the optimal checkpoint interval of the unified model in our analytical assessment. As an instantiation of our model, we propose to unify task-level checkpointing with a system-wide checkpointing scheme for task-parallel HPC applications. This instantiation has three distinct advantages: first it reduces performance overheads by decreasing the frequency of checkpoints in the unified system, second it features fast failure recovery by using in-memory task-local checkpoints instead of on-disk global checkpoints, and third it does not compromise from the high failure coverage typical of system-wide checkpointing.

关键词： Checkpointing parallel processing Mathematical model Performance gain Fault tolerance Fault tolerant systems

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