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41st international symposium on Mathematical Foundations of Computer Science, MFCS 2016

作者： Jeffery, Stacey Le Gall, François Institute for Quantum Information and Matter California Institute of Technology Pasadena United States Graduate School of Informatics Kyoto University Kyoto Japan

ISBN: (纸本)9783959770163

Computing set joins of two inputs is a common task in database theory. Recently, Van Gucht, Williams, Woodruff and Zhang [PODS 2015] considered the complexity of such problems in the natural model of (classical) two-party communication complexity and obtained tight bounds for the complexity of several important distributed set joins. In this paper we initiate the study of the quantum communication complexity of distributed set joins. We design a quantum protocol for distributed Boolean matrix multiplication, which corresponds to computing the composition join of two databases, showing that the product of two n × n Boolean matrices, each owned by one of two respective parties, can be computed with eO(√ n3/4) qubits of communication, where denotes the number of non-zero entries of the product. Since Van Gucht et al. showed that the classical communication complexity of this problem is (n √), our quantum algorithm outperforms classical protocols whenever the output matrix is sparse. We also show a quantum lower bound and a matching classical upper bound on the communication complexity of distributed matrix multiplication over F2. Besides their applications to database theory, the communication complexity of set joins is interesting due to its connections to direct product theorems in communication complexity. In this work we also introduce a notion of all-pairs product theorem, and relate this notion to standard direct product theorems in communication complexity. © Stacey Jeffery and François Le Gall.

关键词： Database systems

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21st IEEE workshop on dependable parallel, distributed and network-centric systems (DPDNS 16)

Proceedings - 2016 IEEE 30th International Parallel and Dist...

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proceedings - 2016 IEEE 30th international parallel and distributed Processing symposium, IPDPS 2016 2016年 1269-1269页

作者： Avresky, Dimiter Maehle, Erik Palmieri, Roberto IRIANC Boston United States Munich Germany University of Luebeck Germany Virginia Tech. University United States

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Face Recognition Using Local Binary Patterns Histograms (LBPH) on an FPGA-Based System on Chip (SoC)

Face Recognition Using Local Binary Patterns Histograms (LBP...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Nikolaos Stekas Dirk Van Den Heuvel Delft University of Technology Delft Netherlands TOPIC Embedded Syst. Netherlands

ISBN: (纸本)9781509036837

The need for facial recognition systems that are fast and accurate is continuously increasing. In this paper, a face recognition implementation on a System on Chip (SoC), integrated with an FPGA, is presented. This implementation utilizes Local Binary Patterns Histograms to extract features from test face images and Manhattan distance to retrieve the correct match from the system's face database. The SoC utilized is a Zynq-7030. The feature extraction and the distance computations, between the database, are implemented on the FPGA. The ARM processor of the SoC is responsible for receiving the input stream and presenting the output result, using the acquired distances. Real-time face recognition, with an execution time of 8.6 ms and accuracy of 79%, is achieved through this implementation.

关键词： Face recognition Histograms Field programmable gate arrays databases Face Feature extraction Real-time systems

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SamzaSQL: Scalable Fast Data Management with Streaming SQL

SamzaSQL: Scalable Fast Data Management with Streaming SQL

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Milinda Pathirage Julian Hyde Yi Pan Beth Plale School of Informatics and Computing Indiana University LinkedIn Hortonworks

ISBN: (纸本)9781509036837

As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often low-level, requiring substantial custom code that adds to the programmer learning curve and maintenance overhead. Additionally, these systems often lack SQL querying capabilities that have proven popular on Big Data systems like Hive, Impala or Presto. We define a minimal set of extensions to standard SQL for data stream querying and manipulation. These extensions are prototyped in SamzaSQL, a new tool for streaming SQL that compiles streaming SQL into physical plans that are executed on Samza, an open-source distributed stream processing framework. We compare the performance of streaming SQL queries against native Samza applications and discuss usability improvements. SamzaSQL is a part of the open source Apache Samza project and will be available for general use.

关键词： Yarn Standards distributed databases Fault tolerance Fault tolerant systems Computer architecture Big data

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Plurality consensus in arbitrary graphs: Lessons learned from load balancing 24

Plurality consensus in arbitrary graphs: Lessons learned fro...

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24th Annual European symposium on Algorithms, ESA 2016

作者： Berenbrink, Petra Friedetzky, Tom Kling, Peter Mallmann-Trenn, Frederik Wastell, Chris Simon Fraser University Burnaby Canada Durham University Durham United Kingdom École Normale Supérieure Paris France

ISBN: (纸本)9783959770156

We consider plurality consensus in networks of n nodes. Initially, each node has one of k opinions. The nodes execute a (randomized) distributed protocol to agree on the plurality opinion (the opinion initially supported by the most nodes). In certain types of networks the nodes can be quite cheap and simple, and hence one seeks protocols that are not only time efficient but also simple and space efficient. Typically, protocols depend heavily on the employed communication mechanism, which ranges from sequential (only one pair of nodes communicates at any time) to fully parallel (all nodes communicate with all their neighbors at once) and everything in-between. We propose a framework to design protocols for a multitude of communication mechanisms. We introduce protocols that solve the plurality consensus problem and are, with probability 1 - o (1), both time and space efficient. Our protocols are based on an interesting relationship between plurality consensus and distributed load balancing. This relationship allows us to design protocols that generalize the state of the art for a large range of problem parameters. © Petra Berenbrink, Tom Friedetzky, Peter Kling, Frederik Mallmann-Trenn, and Chris Wastell.

关键词： distributed computer systems

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DataNet: A Data Distribution-Aware Method for Sub-Dataset Analysis on distributed File systems

DataNet: A Data Distribution-Aware Method for Sub-Dataset An...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jun Wang Jiangling Yin Jian Zhou Xuhong Zhang Ruijun Wang Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL

ISBN: (纸本)9781509021413

In this paper, we study the problem of sub-dataset analysis over distributed file systems, e.g, the Hadoop file system. Our experiments show that the sub-datasets' distribution over HDFS blocks can often cause the corresponding analysis to suffer from a seriously imbalanced parallel execution. This is because the locality of individual sub-datasets is hidden by the Hadoop file system and the content clustering of sub-datasets results in some computational nodes carrying out much more workload than others. We conduct a comprehensive analysis on how the imbalanced computing patterns occur and their sensitivity to the size of a cluster. We then propose a novel method to optimize sub-dataset analysis over distributed storage systems referred to as DataNet. DataNet aims to achieve distribution-aware and workload-balanced computing and consists of the following three parts. Firstly, we propose an efficient algorithm with linear complexity to obtain the meta-data of sub-dataset distributions. Secondly, we design an elastic storage structure called ElasticMap based on the HashMap and BloomFilter techniques to store the meta-data. Thirdly, we employ a distribution-aware algorithm for sub-dataset applications to achieve a workload-balance in parallel-execution. Our proposed method can benefit different sub-dataset analyses with various computational requirements. Experiments are conducted on PRObEs Marmot 128-node cluster testbed and the results show the performance benefits of DataNet.

关键词： Algorithm design and analysis Motion pictures distributed databases Clustering algorithms Business File systems Facebook

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Advances in Intelligent systems and Applications - Volume 2: proceedings of the international Computer symposium ICS 2012 Held at Hualien, Taiwan, ...

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2016年

作者： Jeng Shyang Pan Ching Nung Yang Chia Chen Lin

ISBN: (纸本)9783662523315

The field of Intelligent systems and Applications has expanded enormously during the last two decades. Theoretical and practical results in this area are growing rapidly due to many successful applications and new theories derived from many diverse problems. This book is dedicated to the Intelligent systems and Applications in many different aspects. In particular, this book is to provide highlights of the current research in Intelligent systems and Applications. It consists of research papers in the following specific topics: l Authentication, Identification, and Signaturel Intrusion Detectionl Steganography, Data Hiding, and Watermarkingl Database, System, and Communication Securityl Computer Vision, Object Tracking, and Pattern Recognitionl Image Processing, Medical Image Processing, and Video Codingl Digital Content, Digital Life, and Human Computer Interactionl parallel, Peer-to-peer, distributed, and Cloud Computingl Software Engineering and Programming Language This book provides a reference to theoretical problems as well as practical solutions and applications for the state-of-the-art results in Intelligent systems and Applications on the aforementioned topics. In particular, both the academic community (graduate students, post-doctors and faculties) in Electrical Engineering, Computer Science, and Applied Mathematics; and the industrial community (engineers, engineering managers, programmers, research lab staffs and managers, security managers) will find this book interesting."

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Visualization and Analysis for Near-Real-Time Decision Making in distributed Workflows

Visualization and Analysis for Near-Real-Time Decision Makin...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David Pugmire James Kress Jong Choi Scott Klasky Tahsin Kurc Randy Michael Churchill Matthew Wolf Greg Eisenhower Hank Childs Kesheng Wu Alexander Sim Junmin Gu Jonathan Low Oak Ridge National Laboratory University of Oregon Oak Ridge National Laboratory Stony Brook University Oak Ridge National Laboratory Princeton Plasma Physics Laboratory Georgia Institute of Technology University of Oregon Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory A*STAR Computational Resource Centre

ISBN: (纸本)9781509036837

Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

关键词： Data visualization Data models Computational modeling distributed databases Plasmas Middleware Wide area networks

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Open Source Initiatives and Frameworks Addressing distributed Real-Time Data Analytics

Open Source Initiatives and Frameworks Addressing Distribute...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sarwar Jahan Morshed Juwel Rana Marcelo Milrad Linnaeus University Sweden Daffodil International University Bangladesh Linnaeus University Växjö Sweden Department of Media Technology Linnaeus University Växjö Sweden

ISBN: (纸本)9781509036837

The continuous evolution of digital services, is resulting in the generation of extremely large data sets that are created in almost real time. Exploring new opportunities for improving the quality of these digital services, as well as providing better-personalized experiences to digital users are two major challenges to be addressed. Different methods, tools, and techniques existed today to generate actionable insights from digital services data. Traditionally, big data problems are handled on historical data-sets. However, there is a growing demand on real-time data analytics to offer new services to users and to provide pro-active customers' care, personalized ads, emergency aids, just to give a few examples. Spite of the fact that there are few existing frameworks for real-time analytics, however, utilizing those for solving distributed real-time big data analytical problems stills remains a challenge. Existing real-time data analytics (RTDA) frameworks are not covering all the features that requires for distributed computation in real-time. Therefore, in this paper, we present a qualitative overview and analysis on some of the mostly used existing RTDA frameworks. Specifically, Apache Spark, Apache Flink, Apache Storm, and Apache Samza are covered and discussed in this paper.

关键词： Real-time systems Sparks Data analysis distributed databases Storms Big data

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A Medium-Grained Algorithm for Sparse Tensor Factorization

A Medium-Grained Algorithm for Sparse Tensor Factorization

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international symposium on parallel and distributed Processing (IPDPS)

作者： Shaden Smith George Karypis University of Minnesota Minneapolis MN US Department of Computer Science and Engineering University of Minnesota

ISBN: (纸本)9781509021413

Modeling multi-way data can be accomplished using tensors, which are data structures indexed along three or more dimensions. Tensors are increasingly used to analyze extremely large and sparse multi-way datasets in life sciences, engineering, and business. The canonical polyadic decomposition (CPD) is a popular tensor factorization for discovering latent features and is most commonly found via the method of alternating least squares (CPD-ALS). The computational time and memory required to compute CPD limits the size and dimensionality of the tensors that can be solved on a typical workstation, making distributed solution approaches the only viable option. Most methods for distributed-memory systems have focused on distributing the tensor in a coarse-grained, one-dimensional fashion that prohibitively requires the dense matrix factors to be fully replicated on each node. Recent work overcomes this limitation by using a fine-grained decomposition of the tensor nonzeros, at the cost of computationally expensive hypergraph partitioning. To that effect, we present a medium-grained decomposition that avoids complete factor replication and communication, while eliminating the need for expensive pre-processing steps. We use a hybrid MPI+OpenMP implementation that exploits multi-core architectures with a low memory footprint. We theoretically analyze the scalability of the coarse-, medium-, and fine-grained decompositions and experimentally compare them across a variety of datasets. Experiments show that the medium-grained decomposition reduces communication volume by 36-90% compared to the coarse-grained decomposition, is 41-76x faster than a state-of-the-art MPI code, and is 1.5-5.0x faster than the fine-grained decomposition with 1024 cores.

关键词： Tensile stress Matrix decomposition Sparse matrices distributed databases Memory management parallel processing Scalability

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