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共 1745 条记录，以下是351-360 订阅

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Accelerating Conjugate Gradient using OmpSs

Accelerating Conjugate Gradient using OmpSs

引用

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

作者： Sandra Catalán Xavier Martorell Jesús Labarta Tetsuzo Usui Leonel Antonio Toledo Díaz Pedro Valero-Lara Barcelona Supercomputing Center Barcelona Spain Universitat Polit??cnica de Catalunya Barcelona Spain Universitat Polit`ecnica de Catalunya Barcelona Spain Fujitsu Limited Kawasaki Japan

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we present the benefits of using the clause concurrent of OmpSs when performing reductions, more specifically, when applied to the dot product (DOT) operations. We analyze its benefits through the implementation of different versions of the Conjugate Gradient (CG) method. We start from a parallel version of the code based on tasks and dependencies; later, we introduce the use of the concurrent clause, which allows to overlap the execution of tasks that have data dependencies among them. In this way, we want to show the benefits of the concurrent clause, which might be included in OpenMP standard as previously done with other OmpSs features. Our tests, performed on a single node of the (Intel-based) Marenostrum 4 Supercomputer and a single socket of the (ARM-based) Dibona cluster, show that the use of the concurrent clause may improve performance with respect to the version where only tasks and dependencies are used around 37% and 23% respectively.

关键词： Task analysis US Department of Transportation Kernel parallel processing Sockets Libraries Runtime

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Low Computational Data Fusion Approach Using INS and UWB for UAV Navigation Tasks in GPS-Denied Environments

Low Computational Data Fusion Approach Using INS and UWB for...

引用

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

作者： Xiaoying Kong Gengfa Fang Li Liu Tich Phuoc Tran Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Faculty of Engineering and Information Technologies The University of Sydney Sydney Australia School of Science UNSW Canberra Canberra Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

this paper presents a low computational approach for unmanned aerial vehicles (UAV) navigation in GPS-denied environments. this approach is aiming to reduce computation load for UAV flying mission constraints. Small size, light weight on board hardware are constraints for UAV deployment and flying missions. the on board processor should not be built with high complexity and should consume as little computing as possible. Most existing approaches use Kalman filter, extended Kalman filter, Unscented filter, or particle filter to fuse different types of onboard sensor data to estimate UAV position. We developed a data fusion architecture that does not use these filters. We use an ultra-light-coupling fusion architecture. In this architecture, primary sensor and secondary sensor data are fused. When the secondary sensor is unavailable in most of the time, the UAV navigation uses the output of the primary sensor. When the secondary sensor signal is available, the primary sensor is re-aligned using the secondary sensor signal to bond the errors. In our approach, the primary sensor is Inertial Measurement Unit (IMU), and the secondary sensor inputs are from Ultra-wideband system (UWB). this approach is validated using demonstration of comparison of computing load, and simulation results for accuracy and reliability testing using UAV flying mission scenario.

关键词： Handheld computers conferences distributed computing Systems architecture Couplings Computational modeling Load modeling

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Robust, Low-Loss RCIGCT Technology and MV applications

Robust, Low-Loss RCIGCT Technology and MV Applications

引用

international conference on Power Electronics (ICPE)

作者： Peter K. Steimer David Weiss Bjϕrn Odegard BB Switzerland Ltd Corporate Research Center Dattwil Switzerland ABB Power Grid Technologies Turgi Switzerland

ISBN: (纸本)9781728116129

the combination of the robust low-loss RC-IGCT technology with the modular multi-level converter (MMC) technology is a key enabler for high-power MV applications from 50 to 500 MV A. the RCIGCT technology covers cost-efficiently phase currents in the range from 1000Arms up to 3500 Arms. Future bi-mode BGCT technologies will enable even higher currents without any parallel connection up to 5000 Arms. the ceramic presspack housing of the RCIGCT in combination with the IGCT clamp network allows a unique and safe plasma containment in all fault cases. Future high power MV application like pumped hydropower, MVDC collection grids for renewables, interties for distribution grids and future MVDC distribution backbones can be served at leading costs and efficiency levels. the low-loss RCIGCT technology is inherently a future option at higher voltage levels, i.e. in HVDC applications.

关键词： Insulated gate bipolar transistors Ceramics Plasmas HVDC transmission Silicon Circuit faults thyristors

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A parallel Uncertain Frequent Itemset Mining Algorithm with Spark

A Parallel Uncertain Frequent Itemset Mining Algorithm with ...

引用

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

作者： Jiaman Ding Haibin Li Yuanyuan Wang Lianyin Jia Jinguo You Yang Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability.

关键词： Itemsets Data mining Probability Database systems

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An adaptive cloud-based IoT back-end architecture and its applications 26

An adaptive cloud-based IoT back-end architecture and its ap...

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26th Euromicro international conference on parallel, distributed, and Network-Based Processing (PDP)

作者： Marosi, Attila Csaba Farkas, Attila Lovas, Robert Hungarian Acad Sci MTA SZTAKI Inst Comp Sci & Control Lab Parallel & Distributed Syst POB 63 H-1518 Budapest Hungary

ISBN: (纸本)9781538649756

Internet of things (IoT) is playing increasingly more fundamental role in wide range of sectors, including industry, agriculture, health care, and other services. In many cases, cloud computing serves as an elastic and efficient paradigm for implementing IoT back-ends. With the emerging lightweight software container technologies, the feasible approaches and design options for such IoT back-ends have been significantly enriched. In our paper we present the evolution of an IoT back-end, which is responsible for collecting (among others) meteorological, image and soil data from cultivated fields in order to enable precision farming. the different versions, namely the cloud VM-based and the Docker containerized variants, provide highly scalable and vendor independent (cloud provider agnostic) solutions, therefore they can form a robust and adaptive framework for further pilot applications areas, e.g. Connected Cars and Industry 4.0, as the presented benchmarks illustrate the throughput and other parameters of the current implementation in the paper.

关键词： IoT precision agriculture big data data ingestion containers

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Proceedings - 5th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2018

Proceedings - 5th IEEE/ACM International Conference on Big D...

引用

5th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2018

ISBN: (纸本)9781538655023

the proceedings contain 27 papers. the topics discussed include: CloneHadoop: process cloning to reduce hadoop's long tail;fault-tolerant query execution over distributed bitmap indices;skipping unused events to speed up rollback-recovery in distributed data-parallel CEP;prediction of air pollution through machine learning approaches on the cloud;non-linear machine learning with active sampling for MOX Drift compensation;detecting system anomalies in multivariate time series with information transfer and random walk;a decentralized sns system based on XMPP with connection control in large-scale disasters;a mobile application for dog breed detection and recognition based on deep learning;prediction of bus delay over intervals on various kinds of routes using bus probe data;and development of a radiology decision support system for the classification of MRI brain scans.

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Scalable distributed Temporal Reasoning

Scalable Distributed Temporal Reasoning

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12th KIPS international conference on Ubiquitous Information technologies and applications (CUTE) / 9th international conference on Computer Science and its applications (CSA)

作者： Kim, Jonghoon Kim, Incheol Kyonggi Univ Suwon South Korea

ISBN: (纸本)9789811076053;9789811076046

In this paper, we propose the design and implementation of a large-scale qualitative temporal reasoner, MRQUTER, which can perform reasoning over large Web-scale knowledge bases. this temporal reasoner is built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs and incorporates some optimization techniques into each reasoning job component, implemented using a pair of Map and Reduce functions. through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

关键词： Qualitative temporal reasoning MapReduce Temporal relations

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New parallel Algorithms for All Pairwise Computation on Large HPC Clusters

New Parallel Algorithms for All Pairwise Computation on Larg...

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

作者： Tao Tang Hao Wu Wei Bao Pengyi Yang Dong Yuan Bing Bing Zhou Faculty of Engineering and IT University of Technology Sydney Sydney Australia School of Computer Science University of Sydney Sydney Australia School of Mathematics & Statistics University of Sydney Sydney Australia School of Elec. & Inf. Engineering University of Sydney Sydney Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

All pairwise computation is defined as performing computation between every pair of the elements in a given dataset. It is often a necessary first step in a number of bioinformatics applications. Many of such applications require multiple terabytes of main memory and take multiple peta floating point operations to complete the computation. therefore, large HPC clusters are needed to tackle these large-scale computational problems. Conventionally designed parallel algorithms using data partitioning may have a scalability issue, i.e., for a given problem of fixed size the efficiency may decrease if the number of compute nodes is increased (Amdahl's law). In this paper we introduce a new method for parallel algorithm design. Using this method we first design an efficient one-dimensional (1D) ring algorithm and then a two-dimensional (2D) algorithm based on the 1D ring for all pairwise computation. When increasing the compute nodes, instead of reducing the block size, we make multiple copies of the original data blocks in the 1D ring and distribute them across the added compute nodes in the other dimension. By properly organizing the compute nodes the communication overhead can be reduced to a minimum in this two-dimensional setting. Experiments on a Cray XC40 HPC supercomputer show that our new algorithms are very efficient and scalable for large-scale all pairwise computation on large HPC clusters.

关键词： parallel algorithms Clustering algorithms Australia Two dimensional displays Partitioning algorithms Task analysis Correlation

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Disk throughput Controller for Cloud Data-Centers

Disk Throughput Controller for Cloud Data-Centers

引用

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

作者： M. Reza Hoseinyfarahabady Zahir Tari Albert Y. Zomaya Center for Distributed & High Performance Computing School of Computer Science The University of Sydney New South Wales Australia School of Science RMIT University Melbourne VIC Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

With the increasing popularity of virtual machine monitoring (VMM) technologies, performance variability among collocated virtual machines (VMs) can easily become a severe scalability issue. Particularly, it becomes a necessary for administrative team to control the performance degradation level in a shared environment when multiple I/O-intensive applications simultaneously request their I/O operations [1]. Nevertheless, adding several logical layers between the running applications and the physical storage system, as seen in contemporary virtualized storage devices, makes it considerably difficult to build a low overhead controlling mechanism for such systems (while each VM may running a separate operating system instance) [2]. In this paper, we propose a strategy based on control theory for managing the performance of several I/O requests, such as mean response times and read/write throughput in a consolidated environment where multiple virtual services can share access to a storage system. this scheme uses an approach for measuring the characterization of read/write performance attributes of each virtual services and also takes into account the run-time quality of service enforcement levels requested by them. this is formulated as an optimization problem where a reward function is defined to reduce the overall QoS violation incidents among all consolidated virtual services. Performance evaluation is carried out by comparing the proposed solution with the default embedded Linux controller across a range of emulated application workloads in scenarios with multiple consolidated virtual containers. the results confirm that the proposed solution can reduce the overall QoS violation incident rates in scenarios in which the platform operates at a significant traffic load comparing to the default policy in LXC engine.

关键词： Quality of service Virtual machine monitors Performance evaluation Containers Interference Linux Resource management

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In the Quest of Trade-off between Job parallelism and throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly

In the Quest of Trade-off between Job Parallelism and Throug...

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

作者： Ramesh Pokhrel Ashish Rauniyar Anis Yazidi Department of Computer Science Oslo Metropolitan University Norway Department of Technology Systems University of Oslo Norway

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

With the emergence of the concept of big data, Hadoop MapReduce has been the de facto standard programming model for processing a large amount of data stored on the different cluster nodes in a distributed manner. It is known that the implementation of MapReduce operation with the default configuration yields a low number of parallel running jobs. In fact, poor resource utilization and overall low performance are usually induced by the default configuration. Although a myriad of works has been carried out in the literature for optimally configuring Hadoop MapReduce, the absolute vast majority of those works only consider offline and static configuration. those approaches are clearly ineffective as the load might change during execution requiring tuning again the configuration parameters. In this work, we rather focus on dynamical and adaptively configuring Hadoop MapReduce by changing the system level Maximum Application Master Resource in Percent (MARP) parameter on the fly. We show that adaptively tuning the MARP parameter yields a good trade-off between job parallelism and throughput. To achieve this, an optimal design which we call Adaptive Parameter Tuning of Hadoop (APth) based on a novel variant of the Tsetlin Automata is devised. Comprehensive experimental results show that the resources are optimally and appropriately utilized, resulting in better job parallelism and throughput. Furthermore, it is found that our APth approach spends 47% less time for job execution as compared to the default configuration.

关键词： throughput Tuning Automata Resource management Learning automata parallel processing Task analysis

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