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21st International workshop on Job Scheduling Strategies for parallel Processing, JSSPP 2017

ISBN: (纸本)9783319773971

The proceedings contain 10 papers. The special focus in this conference is on. The topics include: Analysis of Mixed Workloads from Shared Cloud Infrastructure;Tuning EASY-Backfilling Queues;don’t Hurry Be Happy: A Deadline-Based Backfilling Approach;Supporting real-time Jobs on the IBM Blue Gene/Q: Simulation-Based Study;towards Efficient Resource Allocation for distributed Workflows Under Demand Uncertainties;programmable In Situ System for Iterative Workflows;A Data Structure for Planning Based Workload Management of Heterogeneous HPC systems;ScSF: A Scheduling Simulation Framework.

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real-time parallel Computing Design for Implementation of Point/Small Target Detection Algorithm in Visible/Infra-Red Video 5

Real-Time Parallel Computing Design for Implementation of Po...

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5th IEEE International Conference on parallel, distributed and Grid Computing (PDGC)

作者： Saran, Ram Kumar, Avnish Srivastava, Hari Babu Minist Def Instruments Res & Dev Estab DRDO Raipur Rd Dehra Dun 248008 India Minist Def DRDO Laser Sci & Technol Ctr LASTEC Metcalfe House Delhi 110054 India

ISBN: (纸本)9781728106465

Aerial targets such as missiles and aircrafts at far distance projected on image plane as point or small targets in infra-red and visible video. These targets lack apriori information about target dynamic, shape and size. Detection and tracking of such targets has been reported challenging task Hence, point or small size target detection algorithms become focus of long range detection and tracking systems. Generally, pre-processing is carried out on the input frame to predict the background and consequently enhance the target signature. In some scenario, post-processing algorithms are also required to reduce the false alarms. In this paper, we propose an efficient and innovative scheme for real-time implementation of point or small size target detection algorithm on PowerPC Single-Board Computer (SBC) by utilizing the parallel computing feature of AltiVec vector processing unit to achieve real-time processing. Results demonstrate the real-time processing of video with hardware results matching the simulation results.

关键词： parallel Processing Long Range Target Detection Mathematical Morphology AltiVec Vector Processor Pre-processing Post-processing

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workshop Heterogeneous Equipment Information Perception Analysis System

Workshop Heterogeneous Equipment Information Perception Anal...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Pengfei Wu Mengjia Qi Lingyan Gao Wei Zou Qiang Miao Li-lan Liu Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai Aerospace Systems Engineering Institute Shanghai China

In order to meet the high requirements of the shop management system for device data-aware interaction, in the traditional data-aware mode, this paper proposes a development platform based on Kepware as a perceptual software, through the combination of heterogeneous device network design and wireless network (AP), using OPC The interface technology realizes the parallel acquisition of multi-channel data of heterogeneous devices, and verifies the feasibility of the data acquisition method through experiments. The results of the research show that the information of real-time perception and production status monitoring based on Kepware can quickly process, analyze, real-time display and store the data of each channel. This scheme has certain reference value for the data collection of heterogeneous equipment in digital workshop.

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parallel Wavelet-based Bayesian Compressive Sensing based on Gibbs Sampling

Parallel Wavelet-based Bayesian Compressive Sensing based on...

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IEEE International workshop on Signal Processing systems (IEEE SiPS)

作者： Zhou, Jian Chakrabarti, Chaitali Arizona State Univ SECEE Tempe AZ 85287 USA

ISBN: (纸本)9781538663189

Bayesian compressive sensing (BCS) helps address ill-posed signal recovery problems using the Bayesian estimation framework. Gibbs sampling is a technique used in Bayesian estimation that iteratively draws samples from conditional posterior distributions. However, Gibbs sampling is inherently sequential and existing parallel implementations focus on reducing the communication between computing units at the cost of increase in recovery error. In this work, we propose a two-stage parallel coefficient update scheme for wavelet-based Bayesian compressive sensing, where the first stage approximates the real distributions of the wavelet coefficients and the second stage computes the final estimate of the coefficients. While in the first stage the parallel computing units share information with each other, in the second stage, the parallel units work independently. We propose a new coefficient update scheme that updates coefficients in both stages based on data generated a few rounds ago. Such a scheme helps in relaxing the timing constraints for communication in the first stage and computations in the second stage. We design the corresponding parallel architecture and synthesize it in 7 nm technology node. We show that in a system with 8 computing units, our method helps reduce the execution time by 17.4x compared to a sequential implementation without any increase in the signal recovery error.

关键词： Gibbs sampling parallel implementation Bayesian compressive sensing

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Enabling Technologies for distributed Temperature Monitoring of Smart Power Cables

Enabling Technologies for Distributed Temperature Monitoring...

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IEEE workshop on Environmental, Energy, and Structural Monitoring systems (EESMS)

作者： Coletta, Guido Persiano, Giovanni Vito Vaccaro, Alfredo Villacci, Domenico Univ Sannio Dept Engn DING Piazza Roma 21 I-82100 Benevento Italy

ISBN: (纸本)9781538664056

The increasing diffusion of renewable energy generators throughout the power transmission systems, and the difficulties in building new transmission assets are leading the power system components to work closest to their design limits, increasing the risk of congestion and reducing their operation reliability and safety. The need for fully exploiting the advantages of renewable energy sources, mitigating the effects of system congestions, is pushing power system operators to apply advanced loading policies based on Dynamic Thermal Rating, considering the actual boundary conditions for conductors heat exchange processes. The possibility to apply these techniques in power cables loading is a very strategic issue, since it could reduce the congestion among critical market zones. Although the application of these techniques in real operating scenario is still at its infancy, and limited to several prototype implementations, in the scientific literature a large range of techniques are proposed for dynamic line temperature monitoring. A very interesting methodology to afford this problem lies in fiber optic-based distributed temperature sensing methodologies, which can play a key role in cables thermal monitoring by providing reliable time-spatial temperature profiles, and being immune to any kind of electromagnetic disturb. Armed with such a vision in this paper detailed experimental results obtained by applying an advanced fiber optic-based sensor for temperature monitoring of a real power cable under different operation and laying conditions are presented and discussed.

关键词： Dynamic Thermal Rating (DTR) Cable Lines distributed Temperature Sensing Optical Fiber Brillouin Scattering Smart Cables

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Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics

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IEEE TRANSACTIONS ON parallel AND distributed systems 2018年第9期29卷 2004-2017页

作者： Zhang, Quan Zhang, Qingyang Shi, Weisong Zhong, Hong Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, Firework, which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for Firework to allow developers to program on Firework. This paper describes the system design, implementation, and programming interface of Firework. The experimental results of a video analytics application demonstrate that Firework reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution.

关键词： distributed big data processing edge computing internet of everything

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distributed Joins and Data Placement for Minimal Network Traffic

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ACM TRANSACTIONS ON DATABASE systems 2018年第3期43卷 1–45页

作者： Polychroniou, Orestis Zhang, Wangda Ross, Kenneth A. Oracle Labs Redwood Shores CA USA Columbia Univ 500 West 120th St New York NY 10027 USA

Network communication is the slowest component of many operators in distributed parallel databases deployed for large-scale analytics. Whereas considerable work has focused on speeding up databases on modern hardware, communication reduction has received less attention. Existing parallel DBMSs rely on algorithms designed for disks with minor modifications for networks. A more complicated algorithm may burden the CPUs but could avoid redundant transfers of tuples across the network. We introduce track join, a new distributed join algorithm that minimizes network traffic by generating an optimal transfer schedule for each distinct join key. Track join extends the trade-off options between CPU and network. Track join explicitly detects and exploits locality, also allowing for advanced placement of tuples beyond hash partitioning on a single attribute. We propose a novel data placement algorithm based on track join that minimizes the total network cost of multiple joins across different dimensions in an analytical workload. Our evaluation shows that track join outperforms hash join on the most expensive queries of real workloads regarding both network traffic and execution time. Finally, we show that our data placement optimization approach is both robust and effective in minimizing the total network cost of joins in analytical workloads.

关键词： distributed joins network optimization data placement

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Proceedings of PyHPC 2017: 7th workshop on Python for High-Performance and Scientific Computing - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of PyHPC 2017: 7th Workshop on Python for High-P...

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7th workshop on Python for High-Performance and Scientific Computing, PyHPC 2017

ISBN: (纸本)9781450354806

The proceedings contain 8 papers. The topics discussed include: enabling python to execute efficiently in heterogeneous distributed infrastructures with PyCOMPSs;efficient pattern matching in python;real-time financial risk measurement of dynamic complex portfolios with Python and PyOpenCL;Python in the NERSC exascale science applications program for data;real-time thermal medium-based breathing analysis with Python;GPUMap: a transparently GPU-accelerated Python map function;nbodykit: a Python toolkit for cosmology simulations and data analysis on parallel HPC systems;and Python and HPC for high energy physics data analyses.

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Experimental Verification and Analysis of Dynamic Loop Scheduling in Scientific Applications 17

Experimental Verification and Analysis of Dynamic Loop Sched...

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17th Annual International Symposium on parallel and distributed Computing (ISPDC)

作者： Mohammed, Ali Eleliemy, Ahmed Ciorba, Florina M. Kasielke, Franziska Banicescu, Ioana Univ Basel Dept Math & Comp Sci Basel Switzerland Tech Univ Dresden Ctr Informat Serv & High Performance Comp Dresden Germany Mississippi State Univ Dept Comp Sci & Engn Starkville MS USA

ISBN: (纸本)9781538653302

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of 'How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?' In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy, and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

关键词： Dynamic loop scheduling Self-scheduling parallel spin-image Simulation Performance MSG SimDag

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Sparse Adaptation of distributed Blind Source Separation in Acoustic Sensor Networks

Sparse Adaptation of Distributed Blind Source Separation in ...

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IEEE workshop on Applications of Signal Processing to Audio and Acoustics

作者： Michael Günther Haitham Afifi Andreas Brendel Holger Karl Walter Kellermann Multimedia Communications and Signal Processing Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) Erlangen Germany Computer Networks Group Paderborn University Paderborn Germany

By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CON-volutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.

关键词： Task analysis real-time systems Acoustics Blind source separation Acoustic sensors Wireless communication

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