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distributed Computing and Artificial Intelligence, 12th international conference 1

引用

丛书名： Advances in Intelligent Systems and Computing

2015年

作者： Sigeru Omatu, Qutaibah M. Malluhi, Sara Rodríguez Gonzalez, Grzegorz Bocewicz, Edgardo Bucciarelli, Gianfranco Giulioni, Farkhund Iqba (eds.)

ISBN: (数字)9783319196374;9783319196381

ISBN: (纸本)9783319196374;9783319196381

The 12th international Symposium on distributed Computing and Artificial Intelligence 2015 (DCAI 2015) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of todays society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real *** symposium is organized by the Osaka Institute of Technology, Qatar University and the University of Salamanca.

关键词： Electronic data processing Artificial intelligence

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Open JDK Meets Xeon Phi: A Comprehensive Study of Java HPC on Intel Many-Core Architecture

Open JDK Meets Xeon Phi: A Comprehensive Study of Java HPC o...

引用

international conference on parallel processing Workshops (ICPPW)

作者： Yang Yu Tianyang Lei Haibo Chen Binyu Zang School of Computer Science Fudan University Institute of Parallel and Distributed Systems Shanghai Jiao Tong University

The increasing demand for performance has stimulated the wide adoption of many-core accelerators like Intel Xeon Phi Coprocessor, which is based on Intel's Many Integrated Core architecture. While many HPC applications running in native mode have been tuned to run efficiently on Xeon Phi, it is still unclear how a managed runtime like JVM performs on such an architecture. In this paper, we present the first measurement study of a set of Java HPC applications on Xeon Phi under JVM. One key obstacle to the study is that there is currently little support of Java for Xeon Phi. This paper presents the result based on the first porting of Open JDK platform to Xeon Phi, in which Hot Spot VM acts as the kernel execution engine. The main difficulty includes the incompatibility between Xeon Phi ISA and the assembly library of Hotspot VM. By evaluating the multithreaded Java Grande benchmark suite, we quantitatively study the performance and scalability issues of JVM on Xeon Phi and draw several conclusions from the study. To fully utilize the vector computing capability, we also present a semi-automatic vectorization in Hot Spot VM. Together with this optimization and tuning, our optimized JVM achieves up to 3.4X speedup with 60 physical cores compared to that on Xeon CPU processor. Our study indicates that it is viable and potentially performance-beneficial to run applications written for managed runtime on Xeon Phi.

关键词： Java Coprocessors Computer architecture Benchmark testing Libraries Instruction sets Scalability

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Dynamically Deploying Packet processing applications on Running Systems without Halting

Dynamically Deploying Packet Processing Applications on Runn...

引用

international conference on Network-Based Information Systems (NBIS)

作者： Ralph Duncan Peder Jungck Kenneth Ross Alfredo Chorro-Rivas Jim Frandeen Minh Nguyen Lookingglass Cyber Solutions Sunnyvale California USA BAE Systems Intelligence and Security McLean Virginia USA

As computer networks become increasingly central to societal functions, avoiding their disruption grows in importance. Thus, this paper describes techniques for dynamically replacing packet processing programs without halting the programs or the packet traffic. The techniques are geared to the application domain and our coarse-grained parallelism packet processing model but are not exclusive to either. The approach allows old and new deployments' executable code to coexist until the next deployment. We write new code and table information to the running system without perturbing the threads processing packets currently in flight. Actual cut-over is effected by an atomic write to a single variable. Our implementation is realized by separate tables that represent program structure and packet routing topology, by associating a deployment indicator with each thread, and by enhanced linking and loading software tools. The techniques have been in fielded products for several years.

关键词： Ports (Computers) Routing parallel processing Joining processes Context Cloud computing

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FLOWPROPHET: Generic and Accurate Traffic Prediction for Data-parallel Cluster Computing

FLOWPROPHET: Generic and Accurate Traffic Prediction for Dat...

引用

international conference on distributed Computing Systems

作者： Hao Wang Li Chen Kai Chen Ziyang Li Yiming Zhang Haibing Guan Zhengwei Qi Dongsheng Li Yanhui Geng SJTU HKUST HKUST NUDT HKUST NUDT SJTU Huawei

Data-parallel computing frameworks (DCF) such as MapReduce, Spark, and Dryad etc. Have tremendous applications in big data and cloud computing, and throw tons of flows into data center networks. In this paper, we design and implement FLOWPROPHET, a general framework to predict traffic flows for DCFs. To this end, we analyze and summarize the common features of popular DCFs, and gain a key insight: since application logic in DCFs is naturally expressed by directed acyclic graphs (DAG), DAG contains necessary time and data dependencies for accurate flow prediction. Based on the insight, FLOWPROPHET extracts DAGs from user applications, and uses the time and data dependencies to calculate flow information 4-tuple, (source, destination, flow_size, establish_time), ahead-of-time for all flows. We also provide generic programming interface to FLOWPROPHET, so that current and future DCFs can deploy FLOWPROPHET readily. We implement FLOWPROPHET on both Spark and Hadoop, and perform extensive evaluations on a testbed with 37 physical servers. Our implementation and experiments demonstrate that, with time in advance and minimal cost, FLOWPROPHET can achieve almost 100% accuracy in source, destination, and flow size predictions. With accurate prediction from FLOWPROPHET, the job completion time of a Hadoop TeraSort benchmark is reduced by 12.52% on our cluster with a simple network scheduler.

关键词： Calculators Sparks Optimization Prediction algorithms parallel processing Context Data mining

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Reactive stream processing for data-centric publish/subscribe 15

Reactive stream processing for data-centric publish/subscrib...

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Proceedings of the 9th ACM international conference on distributed Event-Based Systems

作者： Shweta Khare Kyoungho An Aniruddha Gokhale Sumant Tambe Ashish Meena Vanderbilt University Nashville TN Real-Time Innovations Sunnyvale CA

ISBN: (纸本)9781450332866

The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fast-moving and heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers. Dataflow architectures based on event-based design have served well in such applications because events support asynchrony, loose coupling, and helps build resilient, responsive and scalable applications. However, a unified programming model for event processing and distribution that can naturally compose the processing stages in a dataflow while exploiting the inherent parallelism available in the environment and computation is still lacking. To that end, we investigate the benefits of blending Reactive Programming with data distribution frameworks for building distributed, reactive, and high-performance stream-processing applications. Specifically, we present insights from our study integrating and evaluating Microsoft .NET Reactive Extensions (Rx) with OMG Data Distribution Service (DDS), which is a standards-based publish/subscribe middleware suitable for demanding industrial IoT applications. Several key insights from both qualitative and quantitative evaluation of our approach are presented.

关键词： reactive programming data distribution service (DDS) stream processing reactive extensions (Rx) publish/subscribe

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Architecture of efficient word processing using Hadoop MapReduce for big data applications

Architecture of efficient word processing using Hadoop MapRe...

引用

international conference on Man and Machine Interfacing (MAMI)

作者： Bichitra Mandal Srinivas Sethi Ramesh Kumar Sahoo Dept. of CSEA IGIT Sarang India

Understanding the characteristics of MapReduce workloads in a Hadoop, is the key in making optimal and efficient configuration decisions and improving the system efficiency. MapReduce is a very popular parallel processing framework for large-scale data analytics which has become an effective method for processing massive data by using cluster of computers. In the last decade, the amount of customers, services and information increasing rapidly, yielding the big data analysis problem for service systems. To keep up with the increasing volume of datasets, it requires efficient analytical capability to process and analyze data in two phases. They are mapping and reducing. Between mapping and reducing phases, MapReduce requires a shuffling to globally exchange the intermediate data generated by the mapping. In this paper, it is proposed a novel shuffling strategy to enable efficient data movement and reduce for MapReduce shuffling with number of consecutive words and their count in the word processor. To improve its scalability and efficiency of word processor in big data environment, repetition of consecutive words count with shuffling is implemented on Hadoop. It can be implemented in a widely-adopted distributed computing platform and also in single word processor big documents using the MapReduce parallel processing paradigm.

关键词： Big data File systems Text processing Monitoring Videos Databases Electronic mail

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Harnessing Big Data for Wireless Body Area Network applications

Harnessing Big Data for Wireless Body Area Network Applicati...

引用

international conference on Computational Intelligence and Communication Networks

作者： Anagha Jamthe Dharma P. Agrawal EECS Dept. Univ. of Cincinnati Cincinnati OH USA

ISBN: (纸本)9781509000777

Precision medicine initiative recently launched by the Government, focuses on prescribing right drug for right patient at a right dosage. Information of genome variation and lifestyle monitoring of a patient using wireless sensor devices is leveraged for this initiative. Wireless Body Area Network (WBAN) applications used for monitoring pervasive health care generates a very high volume of heterogeneous unstructured data (text, video, and images). We propose cloud based WBAN architecture for efficient processing, storage, indexing and archiving of the highly dense clinical data. We use components from Apache Hadoop's ecosystem for parallel and distributed computing. Our evaluation of system uses two public datasets: physical activity monitoring dataset and genome dataset: for examining the prognostic effect of the immune system on breast cancer in lymph node-negative breast cancer patients. Results obtained are very encouraging and indicate that harnessing big data is a vital step for efficiently processing and analyzing clinical data, which can be adopted in precision medicine initiative.

关键词： Big data Cloud computing Electrocardiogram (ECG) Hadoop distributed File System (HDFS) Map Reduce Precision medicine Wireless Body Area Network (WBAN)

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11th international Symposium on Advances in Visual Computing, ISVC 2015

11th International Symposium on Advances in Visual Computing...

引用

11th international Symposium on Advances in Visual Computing, ISVC 2015

ISBN: (纸本)9783319278568

The proceedings contain 81 papers. The special focus in this conference is on Computational Bioimaging, Computer Graphics, Motion and Tracking. The topics include: Graph-based visualization of neuronal connectivity using matrix block partitioning and edge bundling;fuzzy skeletonization improves the performance of characterizing trabecular bone micro-architecture;thermal infrared image processing to assess heat generated by magnetic nanoparticles for hyperthermia applications;visualization techniques for the developing chicken heart;an interactive rendering framework for health care support;image annotation incorporating low-rankness, tag and visual correlation and inhomogeneous errors;extracting surface geometry from particle-based fracture simulations;interactive procedural building generation using kaleidoscopic iterated function systems;motion priors estimation for robust matching initialization in automotive applications;multi-target tracking using sample-based data association for mixed images;a hierarchical frame-by-frame association method based on graph matching for multi-object tracking;experimental evaluation of rigid registration using phase correlation under illumination changes;multi-modal computer vision for the detection of multi-scale crowd physical motions and behavior in confined spaces;hmm based evaluation of physical therapy movements using kinect tracking;segmentation of partially overlapping nanoparticles using concave points;an efficient non-parametric background modeling technique with CUDA heterogeneous parallel architecture;finding the n-cuts of watershed partitions for image segmentation;estimating the dominant orientation of an object using image segmentation and principal component analysis and unscented transformation and matrix rank optimization for moving objects detection in aerial imagery.

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GPU accelerated background subtraction

GPU accelerated background subtraction

引用

international conference on Communication Technology (ICCT)

作者： Dan Liu Science and Technology on Communication Security Laboratory Chengdu China

ISBN: (纸本)9781467370066

Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of computation efficiency. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. This paper describes ViBe algorithm for background subtraction and presents implementation of it using the CUDA technology for parallel processing. The experiments indicate that our implementation outperforms the majority of earlier state-of-the-art algorithms not only in terms of accuracy, but also in terms of processing speed.

关键词： Graphics processing units

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parallel Read Error Correction for Big Genomic Datasets

Parallel Read Error Correction for Big Genomic Datasets

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international conference on High Performance Computing

作者： Nagakishore Jammula Sriram Chockalingam Srinivas Aluru Georgia Institute of Technology Atlanta Georgia USA Indian Institute of Technology Mumbai India

ISBN: (纸本)9781467384872

Genome sequencing, using instruments in vogue today, deciphers in the order of a billion short genomic fragments per run. These fragments are a few hundred bases long and are commonly referred to as `reads'. Reads contain errors due to limitations of sequencing technology. Read error correction enhances the quality of results produced by applications in areas such as genomics, metagenomics, and transcriptomics. Use of error corrected reads also improves the runtime and the memory usage of such applications. Sequential error correction tools cannot cope with the large number of reads produced by modern day sequencing instruments. A distributed-memory parallel Spectrum-based Error Correction (PSbEC) algorithm was proposed to overcome this drawback [1]. In this work, we propose techniques to address three major shortcomings of the PSbEC algorithm. Our optimizations enhance the scope and the speedup of the PSbEC algorithm, thereby enabling error correction of big genomic datasets. More specifically, by combining our optimizations, we are able to achieve a cumulative speedup of up to 11 X. Further, we demonstrate error correction of a human dataset containing nearly 1.55 billion reads. This work stands as the first demonstration of distributed-memory genomic read error correction for a dataset consisting of more than a billion reads.

关键词： Error correction Genomics Bioinformatics Sequential analysis Instruments Optimization parallel algorithms

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