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Locality based data partitioning in Map reduce

Locality based data partitioning in Map reduce

引用

international conference on Electrical, Electronics, and Optimization techniques (ICEEOT)

作者： S. Ancy M. Maheswari Department of Information Technology Jeppiaar Institute of Technology Chennai India DMI College of Engineering Chennai Tamil Nadu IN

Big data is a broad term for data sets so large or complex the traditional data processing applications are inadequate. Challenges include analysis, capture, data duration, search, sharing, storage, transfer, visualization, querying and information privacy. The processing and storage of big data although made efficient and easy by hadoop, there are two things to be considered, a storage part (Hadoop distributed File System) and a processing part (Map reduce). The performance of a map reduce depends on how the data are partitioned and then controlled in parallel manner. The current methods are far from optimal. The skew aware partitioning technology is used to detect the problems related to data partitioning that affects performance. In this paper, we enhance the split mechanism of Hadoop without making it harder than existing. There are a series of works on data partitioning in database communities, we want to employ more intelligent methods, like sampled data for record skew, program code analysis for computation cost estimation etc., to enhance our works for optimizing data partition in MapReduce. Experiments results demonstrated that our solutions can improve the MapReduce processing performance remarkably than traditional Hadoop Implementation.

关键词： Partitioning algorithms File systems Big data distributed databases Algorithm design and analysis Peer-to-peer computing Google

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OpenCV Optimization on Heterogeneous Multi-core Systems for Gesture Recognition applications

OpenCV Optimization on Heterogeneous Multi-core Systems for ...

引用

international conference on parallel processing Workshops (ICPPW)

作者： Hsiang-Wei Sung Yuan-Ming Chang Shao-Chung Wang Jenq-Kuen Lee Department of Computer Science National Tsing-Hua University Hsinchu Taiwan

ISBN: (纸本)9781509028269

Increasingly there are a variety of important applications for image processing on mobile phones. The performance of image processing applications thus becomes one of the focused points of research activities. OpenCV provides APIs to let programmers develop image processing programs with ease. The new OpenCV 3.0 enables OpenCL flow to aim at making the applications developed with OpenCV run fast on heterogeneous multi-core systems. Although OpenCL programs are portable, the performance still needs to be tuned for different architecture models. In this paper, we demonstrate the optimization flow for Gesture Recognition applications with OpenCV 3.0 on Mali GPUs. In this case study, several optimization techniques are devised for the flow. The techniques include vectorization, the increase of vector width via layout transformation, kernel fusion, etc. Preliminary experimental results show that our scheme is effective to optimize OpenCV 3.0 flow for Gesture Recognition applications on embedded heterogeneous multi-core systems.

关键词： Kernel Gesture recognition Optimization Graphics processing units Computer architecture Feature extraction

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3rd international conference on Future Data and Security Engineering, FDSE 2016

引用

3rd international conference on Future Data and Security Engineering, FDSE 2016

ISBN: (纸本)9783319480565

The proceedings contain 29 papers. The special focus in this conference is on Big Data Analytics, Cloud Data Management, Internet of Things, Security, Privacy Engineering, Data Protection, Data Hiding, Context-Based Data Analysis, Emerging Data Management Systems and applications. The topics include: Incorporating trust, certainty and importance of information into knowledge processing systems - an approach;incremental parallel support vector machines for classifying large-scale multi-class image datasets;a large-scale two-level clustering similarity search with MapReduce;immune approach to the protection of IoT devices;heuristic-guided verification for fast congestion detection on wireless sensor networks;security risk management in the aviation turnaround sector;a novel encryption mechanism for door lock;information and identity theft Without ARP spoofing in LAN environments;a watermarking framework for outsourced and distributed relational databases;face quality measure for face authentication;using graph database for evidence correlation on android smartphones;a secure token-based communication for authentication and authorization servers;an enhancement of the Rew-XAC model for workflow data access control in healthcare;trust and risk-based access control for privacy preserving threat detection systems;fine grained attribute based access control model for privacy protection;automatic extraction of semantic relations from text documents;the present and future of large-scale systems modeling and engineering;non-disjoint multi-agent scheduling problem on identical parallel processors and an evaluative model to assess the organizational efficiency in training corporations.

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An Efficient parallel Approach of Parsing and Indexing for Large-Scale XML Datasets

An Efficient Parallel Approach of Parsing and Indexing for L...

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international conference on parallel and distributed Systems (ICPADS)

作者： Kunfang Song Hongwei Lu Xiao Qin School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Computer Science and Software Engineering Auburn University Auburn American

ISBN: (纸本)9781509053827

MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. We propose an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. Our solution seamlessly integrates data storage, labelling, indexing, and parallel queries to process a massive amount of XML data. Specifically, we introduce an SDN labelling algorithm and a distributed hierarchical index using DHTs, we develop an efficient data retrieval approach called B-SLCA. More importantly, we design an advanced two-phase MapReduce solution that is able to efficiently address the issues of labelling, indexing, and query processing on big XML data. We implemented our solution on a real-world Hadoop cluster processing the real-world datasets. Our experimental results show that SDN outperforms NCIM by up to a factor of 1.36 with an average of 1.17, our BSLCA outperforms BwdSLCA by up to a factor of 1.96 with an average of 1.2.

关键词： XML Labeling Indexing Semantics Keyword search Query processing Encoding

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Algorithmic techniques for Solving Graph Problems on the Automata Processor

Algorithmic Techniques for Solving Graph Problems on the Aut...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Indranil Roy Nagakishore Jammula Srinivas Aluru Georgia Institute of Technology Atlanta Georgia USA

ISBN: (纸本)9781509021413

The Automata Processor is a new accelerator technology that supports direct hardware implementation of a set of non-deterministic finite automata over a streaming input, and is designed for complex string pattern matching applications. In this paper, we broaden the scope of this architecture beyond its primary design goal, by developing algorithmic techniques to solve problems on unweighted graphs. We present a strategy to represent nodes and edges in a graph using strings, and use this transformation to develop algorithms for several classic graph problems including finding Hamiltonian paths and cycles, connected components, and breadth-first search. Our algorithms rely on a core set of automata building blocks which we designed for this purpose, and illustrate various design considerations that developers must bear in mind when harnessing this new technology. We expect that this work provides the foundations for solving graph problems using the Automata Processor.

关键词： Automata Routing Clocks Algorithm design and analysis Pattern matching Computer architecture Hardware

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parallel gathering discovery over big trajectory data

Parallel gathering discovery over big trajectory data

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IEEE international conference on Big Data

作者： Yongyi Xian Yan Liu Chuanfei Xu Faculty of Engineering and Computer Science Concordia University Montreal Canada

ISBN: (纸本)9781467390064

The advances in location-acquisition technologies have generated massive spatio-temporal trajectory data, which represent the mobility of a diversity of moving objects over time, such as people, vehicles, and animals. Discovery of traveling companions on trajectory data has many real-world applications. Most of existing discovery approaches are limited to centralized computing, while these techniques for handling large-scale trajectory data require considerable performance improvement. parallel computing essentially provides an alternative method for handling this problem. In this work, we first present the design and implementation of both batch and streaming gathering patterns discovery algorithm in a distributed parallel computing fashion. Afterwards, we further propose several optimization techniques for efficient computation. Finally we conduct extensive experiments based on a public dataset to evaluate the efficiency of our approaches and effectiveness of optimizations using Amazon EC2 clusters.

关键词： Trajectory parallel processing Sparks Data models Clustering algorithms Algorithm design and analysis Optimization

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Application-Level Determinism in distributed Systems

Application-Level Determinism in Distributed Systems

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international conference on parallel and distributed Systems (ICPADS)

作者： Christopher Boelmann Lorenz Schwittmann Marian Waltereit Matthäus Wander Torben Weis Distributed Systems Group University of Duisburg-Essen Duisburg Germany

Deterministic and reproducible program execution eases the development and debugging of distributed systems. However, deterministic execution comes at high performance costs and is hard to achieve, especially when running on different hardware. In this paper we introduce the concept of application-level determinism and describe how the parallel programming model Spawn & Merge can be used for scalable and deterministic distributed computation. Application-level deterministic applications yield reproducible deterministic results independent of the number of nodes participating in the computation, even though intermediate tasks may be executed in an unpredictable schedule. To achieve consistency independent of the order in which operations have been applied we present a new Operational Transformation algorithm, which mitigates the performance loss of introducing determinism with Spawn & Merge. We show that such deterministic processing can scale across a cluster of compute nodes and discuss for which kind of workload the programming model is feasible. Furthermore, for high and low workloads, we evaluate the cost of adding determinism to be 28% and 40% higher than perfect parallel computation.

关键词： Synchronization Data models Computational modeling parallel processing Programming Roads Data structures

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Row Key Designs of NoSQL Database Tables and Their Impact on Write Performance

Row Key Designs of NoSQL Database Tables and Their Impact on...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Eftim Zdravevski Petre Lameski Andrea Kulakov Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia

In several NoSQL database systems, among which is HBase, only one index is available for the tables, which is also the row key and the clustered index. Using other indexes does not come out of the box. As a result, the row key design is the most important thing when designing tables, because an inappropriate design can lead to detrimental consequences on performances and costs. Particular row key designs are suitable for different problems, and in this paper we analyze the performance, characteristics and applicability of each of them. In particular we investigate the effect of using various techniques for modeling row keys: sequences, salting, padding, hashing, and modulo operations. We propose four different designs based on these techniques and we analyze their performance on different HBase clusters when loading HDFS files with various sizes. The experiments show that particular designs consistently outperform others on differently sized clusters in both execution time and even load distribution across nodes.

关键词： Servers Indexes Companies distributed databases Google Load modeling Loading

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Fast Classification of MPI applications Using Lamport's Logical Clocks

Fast Classification of MPI Applications Using Lamport's Logi...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Zhou Tong Scott Pakin Michael Lang Xin Yuan Department of Computer Science Florida State University Tallahassee Florida USA Computer Computational and Stat. Sci. Div. Los Alamos National Laboratory Los Alamos NM

ISBN: (纸本)9781509021413

We present a novel trace-based analysis tool that rapidly classifies an MPI application as bandwidth-bound, latency-bound, load-imbalance-bound, or computation-bound for different interconnection networks. The tool uses an extension of Lamport's logical clock to track application progress in the trace replay. Ithas two unique features. First, it predicts application performance for many latency and bandwidth parameters from a single replay of the trace. Second, it infers the performance characteristics of an application and classifies the application using the predicted performance trend for a range of network configurations instead of using the predicted performance for a particular network configuration. We describe the techniques used in the tool and its design and implementation, and report our performance study of the tool and our experience with classifying nine applications and mini-apps from the DOE Design Forward project as well as the NAS parallel Benchmarks.

关键词： Clocks Radiation detectors Bandwidth Computational modeling Receivers Protocols Performance analysis

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Enabling the high level synthesis of data analytics accelerators

Enabling the high level synthesis of data analytics accelera...

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international conference on Hardware/Software Codesign and System Synthesis (CODES)

作者： Marco Minutoli Vito Giovanni Castellana Antonino Tumeo Marco Lattuada Fabrizio Ferrandi Antonino Tumeo High Performance Computing Pacific Northwest National Laboratory Richland WA USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (纸本)9781509035908

Conventional High Level Synthesis (HLS) tools mainly target compute intensive kernels typical of digital signal processing applications. We are developing techniques and architectural templates to enable HLS of data analytics applications. These applications are memory intensive, present fine-grained, unpredictable data accesses, and irregular, dynamic task parallelism. We discuss an architectural template based around a distributed controller to efficiently exploit thread level parallelism. We present a memory interface that supports parallel memory subsystems and enables implementing atomic memory operations. We introduce a dynamic task scheduling approach to efficiently execute heavily unbalanced workload. The templates are validated by synthesizing queries from the Lehigh University Benchmark (LUBM), a well know SPARQL benchmark.

关键词： Resource description framework Databases Data analysis Benchmark testing High level synthesis parallel processing Dynamic scheduling

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