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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

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A parallel Implementation of WAND on GPUs

A Parallel Implementation of WAND on GPUs

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euromicro International conference on parallel, distributed and network-based processing

作者： Roussian Gaioso Veronica Gil-Costa Helio Guardia Hermes Senger Department of Computer Science Federal University of São Carlos Brazil CONICET-UNSL San Luis Argentina

In this paper we propose and evaluate new strategies for the parallel top-k query processing on GPUs. Our strategies are based on the document-at-a-time approach and have been implemented and tested with the WAND ranking algorithm. In our first strategy (named homogeneous), the posting lists are evenly partitioned among thread blocks. Our second algorithm, named heterogeneous, partitions the posting lists according to document identifier intervals, thus partitions may have different sizes. We also propose three threshold sharing policies, named Local, Safe-R and Safe-WR, which emulate the WAND algorithm global pruning technique. We evaluated our proposals using AND/OR queries, and the results show that the homogeneous algorithm allows better speedups through higher occupancy of the SMs, but at the cost of a lower recall. the heterogeneous algorithm produces the exact top-k documents and shows promising speedups. Also, the Shared-R and Shared-WR policies for threshold propagation allowed better performance, provided there is enough amount of work per thread block, which proved true for queries composed of at least a few millions documents.

关键词： Instruction sets Graphics processing units Indexes Partitioning algorithms Heuristic algorithms Clustering algorithms Search engines

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3D+T motion analysis: motion sensor network versus multiple video cameras 12

3D+T motion analysis: motion sensor network versus multiple ...

引用

12th conference of Optics and Photonics for Information processing

作者： Leduc, Jean-Pierre Reliance Core Consulting 2121 Eisenhower AveSuite 200 Alexandria VA 22314 USA

ISBN: (数字)9781510620742

ISBN: (纸本)9781510620742

this paper addresses the problem of motion analysis performed from digital data captured by a network of motion sensors scattered over a 3D+T field of interest. the digital signals captured in the field are transmitted through a telecommunication network to be processed in a remote monitoring center by an artificial intelligent system based on a dual control involving both deep learning and model based algorithms. Motion analysis proceeds through consecutive steps as detection, motion-oriented classification, parameter estimation, tracking and building trajectories. Eventually, the system can detect and predict abnormalities, incidents and accidents. In all current applications, it is commonly thought that motion analysis is to be performed from data streams captured from multiple video-cameras distributed in a network following a so-called "camera-everywhere" approach. A basic observation of how animal biology proceeds shows that information analyzed by the cortex usually originates from one global eye and from a network of sensors non-uniformly distributed over the the entire skin. Telecommunications are performed through a network of nerves that acts as a bundle of telephone lines that connects each sensor located in the eye and in the skin to specific and dedicated areas of the cortex. this paper shows the relevance of this natural way to perform full motion analysis from a network of motion sensor distributed over a field of interest where the 3D+T motion analysis is performed. Using all video-cameras in a network involves two main drawbacks. First, this setting requires to transmit over a telecommunication network an amount of information of which more than ninety nine percent of the content is useless for motion analysis. Second, additional software algorithms that are consuming time and resources have to be implemented to extract the motion information from the video signal and build the trajectories. Video cameras are in fact useful for both pattern recogni

关键词： Motion Analysis Kinematical Parameters Trajectories Nanosensors photodetectors Group Representations motion detection motion tracking Q-Learning

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Efficient Multi-Core AUTOSAR-Platform based on an Input/Output Gateway Core 25

Efficient Multi-Core AUTOSAR-Platform based on an Input/Outp...

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Urbina, Moises Obermaisser, Roman Univ Siegen Inst Embedded Syst Siegen Germany

ISBN: (纸本)9781509060580

the AUTOSAR standard provides support for multicore systems since version 4. However, this AUTOSAR multicore version focuses on inter-core communication with a shared memory approach. In contrast, the paradigm of message-based network-on-chips provides multiples advantages for real-time embedded systems such as automotive electronics including better temporal predictability, fault containment and energy efficiency. In this paper we propose an efficient multicore architecture for AUTOSAR based on time-triggered network-on-chips and dedicated input/output cores. Additionally, a health monitoring service is integrated into the AUTOSAR ECU architecture in order to provide recovery actions in case of failures of the automotive application or the hardware of a specific core in the multiprocessor. the results demonstrate how the operating system overhead decreases considerably when using the defined input/output cores that serve as hardware accelerators for the AUTOSAR software. Also, the reliability of the system is improved significantly due to the implemented health monitoring service.

关键词： AUTOSAR mu ECU Proxy Module Health Monitoring

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parallel processing of Big Heterogeneous Data for Security Monitoring of IoT networks 25

Parallel Processing of Big Heterogeneous Data for Security M...

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Saenko, Igor Kotenko, Igor Kushnerevich, Alexey Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat Lab Comp Secur Problems St Petersburg Russia

ISBN: (纸本)9781509060580

networks of the Internet of things (IoT) nowadays find greater widespread in many domains. Particularities of creation of IoT make the problem of their security monitoring rather actual;it is caused by necessity of processing of big amounts of heterogeneous data in real time. the problem may be solved by means of implementation of the parallel system for security data processing within IoT on the fly basing on complex event processing (CEP) technology. the paper considers basic solutions for creating such a system. the proposed system is oriented for usage of software environment Hadoop and includes data collection, data storage, data normalization and analysis, and data visualization components. the paper discusses the issues of architecture of this system, its implementation and experimental estimation. the experiments showed that the proposed approach to creation of the system on the basis of CEP technology provides suitable scalability and is capable to meet the requirements of processing data on security events in real time in IoT.

关键词： Internet of things complex event processing Hadoop security monitoring Big Data

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Behavior Analysis for Safety and Security in Automotive Systems 25

Behavior Analysis for Safety and Security in Automotive Syst...

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Rieke, Roland Seidemann, Marc Talla, Elise Kengni Zelle, Daniel Seeger, Bernhard Fraunhofer SIT Darmstadt Germany Philipps Univ Marburg Marburg Germany

ISBN: (纸本)9781509060580

the connection of automotive systems with other systems such as road-side units, other vehicles, and various servers in the Internet opens up new ways for attackers to remotely access safety relevant subsystems within connected cars. the security of connected cars and the whole vehicular ecosystem is thus of utmost importance for consumer trust and acceptance of this emerging technology. this paper describes an approach for on-board detection of unanticipated sequences of events in order to identify suspicious activities. the results show that this approach is fast enough for in-vehicle application at runtime. Several behavior models and synchronization strategies are analyzed in order to narrow down suspicious sequences of events to be sent in a privacy respecting way to a global security operations center for further in-depth analysis.

关键词： automotive security connected car predictive security analysis security modeling and simulation security monitoring complex event processing process discovery

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A Tracking Algorithm for Particle-like Moving Objects 25

A Tracking Algorithm for Particle-like Moving Objects

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Spataro, Davide Arcuri, Paola De Rango, Alessio Spataro, William D'Ambrosio, Donato Mari, Alice Univ Calabria Dept Math & Comp Sci Ponte P Bucci 30B Arcavacata Di Rende Italy Univ Edinburgh Inst BioEngn Kings Bldg Edinburgh EH9 3BF Midlothian Scotland

ISBN: (纸本)9781509060580

Particle tracking plays an important role in numerous fields of science. In this paper, we present TraCCA, an algorithm for detecting and tracking particles based on geometrical difference evaluation and centroid displacement analysis to reconstruct the trajectories. this method works for n-dimensional input data provided that particles are represented by at least a centroid space coordinate and a geometrical entity which describe their shape. Since 2-D images are a common source of such data, we also present a framework for image-manipulation based on Extended Cellular Automata (XCA). We have applied and validated TraCCA in investigating the motility of B. subtilis. injected in a microfluidic device using 4100 images taken at 100 frames per second. Results show that the framework is able to reconstruct the trajectories as computed motion parameters are in accordance with the ones reported in the literature.

关键词： cellular automata tracking image processing bacteria motility

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parallel Simulation of Sinoatrial Node Cells Synchronization

Parallel Simulation of Sinoatrial Node Cells Synchronization

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euromicro conference on parallel, distributed and network-based processing

作者： Nicolas Mata Aurelio Castellanos Abrego Norma Pilar Roman Alonso Graciela Castro Garcıa Miguel Alfonso Lopez Garza Gabriel Godınez Fernéndez Jose Rafael Universidad Autonoma Metropolitana Iztapalapa Iztapalapa MX Univ. Autonοma Metropolitana-Iztapalapa Mexico City Mexico

the sinoatrial node (SAN) has the function of initiating a depolarizing wave that runs throughout the heart. this wave produces the muscular contraction necessary to blood pumping in animals. In recent years different works have been proposed to simulate the electric potential behaviour of a single sinoatrial cell (SANC) and groups of cells, hence a set of differential equations needs to be solved for each microsecond of simulation. An important drawback comes up when the synchronization of millions of SANCs is required involving a huge processing time. Since the simulation of the behavior of a set of cells is an open research topic, it is important to propose efficient tools to reduce response times; unfortunately, because the complexity of the existent models of SANC, very scarce work has been done to this end. this paper proposes three parallel algorithms to simulate the synchronization of a set of SANCs based on the model of Severi (2012). the proposed approaches are built using OpenMP, MPI, and CUDA, in order to compare the benefits given by different computing platforms. We found that all parallel versions perform better when defining a cell per processing unit; however the CUDA version gives the best results in scalability and performance.

关键词： Mathematical model Synchronization Computational modeling Electric potential Junctions Heart Graphics processing units

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On the evaluation of energy-efficient deep learning using stacked autoencoders on mobile GPUs 25

On the evaluation of energy-efficient deep learning using st...

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Falcao, G. Alexandre, L. A. Marques, J. Frazao, X. Maria, J. Univ Coimbra Dept Elect & Comp Engn Inst Telecomunicacoes Coimbra Portugal Univ Beira Interior Dept Informat Inst Telecomunicacoes Covilha Portugal

ISBN: (纸本)9781509060580

Over the last years, deep learning architectures have gained attention by winning important international detection and classification challenges. However, due to high levels of energy consumption, the need to use low-power devices at acceptable throughput performance is higher than ever. this paper tries to solve this problem by introducing energy efficient deep learning based on local training and using low-power mobile GPU parallel architectures, all conveniently supported by the same high-level description of the deep network. Also, it proposes to discover the maximum dimensions that a particular type of deep learning architecture-the stacked autoencoder-can support by finding the hardware limitations of a representative group of mobile GPUs and platforms.

关键词： parallel processing Mobile GPU Low-power Energy savings Deep Learning Stacked Autoencoders

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Privacy-Preserving Location-Proximity for Mobile Apps 25

Privacy-Preserving Location-Proximity for Mobile Apps

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Stirbys, Simonas Abu Nabah, Omar Hallgren, Per Sabelfeld, Andrei Gothenburg Univ Dept Comp Sci & Engn Gothenburg Sweden Chalmers Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9781509060580

Location based Services (LBS) have seen alarming privacy breaches in recent years. While there has been much recent progress by the research community on developing privacy-enhancing mechanisms for LBS, their evaluation has been often focused on the privacy guarantees, while the question of whether these mechanisms can be adopted by practical LBS applications has received limited attention. this paper studies the applicability of Privacy-Preserving Location Proximity (PPLP) protocols in the setting of mobile apps. We categorize popular location social apps and analyze the tradeoffs of privacy and functionality with respect to PPLP enhancements. To investigate the practical performance trade-offs, we present an in-depth case study of an Android application that implements InnerCircle, a state-of-the-art protocol for privacy-preserving location proximity. this study indicates that the performance of the privacy-preserving application for coarse-grained precision is comparable to real applications with the same feature set.

关键词： Location based Services Location Privacy Privacy-preserving Technologies

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GPU-based Bio-inspired Model for Solving Association Rules Mining Problem 25

GPU-based Bio-inspired Model for Solving Association Rules M...

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25th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Djenouri, Youcef Bendjoudi, Ahcene Djenouri, Djamel Comuzzi, Marco UNIST 50 UNIST Gil Ulsan 44949 South Korea CERIST DTISI Algiers Algeria

ISBN: (纸本)9781509060580

We explore in this paper the application of bio-inspired approaches to the association rules mining (ARM) problem for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. A new bio-inspired GPU-based model is proposed, which benefits from the massively GPU threading by evaluating multiple rules in parallel on GPU. To validate the proposed model, the most used bio-inspired approaches (GA, PSO, and BSO) have been executed on GPU to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. the results show that the genetic algorithm outperforms PSO and BSO. Moreover, it outperforms the state-of-the-art GPU-based ARM approaches when dealing with the challenging Webdocs instance.

关键词： Bio-Inspired Approaches Association Rule Mining parallel Algorithms GPU Computing

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