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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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Simulation of parallel processing for Distributed Multi-Target Tracking in Smart Camera Networks 16

Simulation of Parallel Processing for Distributed Multi-Targ...

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10th international conference on Distributed Smart Cameras (ICDSC)

作者： Jafarizadeh, Mehdi YadollahPour, Naeeme Shahid Beheshti Univ Comp Engn Dept Tehran Iran Univ Tehran Dept Management & Accounting Tehran Iran

ISBN: (纸本)9781450347860

In order to reduce the real-time processing gap of distributed algorithms on smart camera networks, we have evaluated a parallel processing method which exploits the processing capabilities of free cameras as auxiliary for busy ones. the communication infrastructure or camera processing capabilities are not included in the traditional evaluation methods like datasets or even virtual reality tools, so they are not suitable to analyze the complexity of distributed algorithms. For this purpose, we have developed a modular framework based on OMNET++ simulation environment named CAM-DIST, in which the velocity model of soccer players are emulated. Simulation results show that parallel processing improves overall efficiency, but could have side effects on individual camera estimations;so choosing optimal sharing value and destinations will enhance the performance.

关键词： Smart camera networks OMNET plus complexity analysis of distributed algorithms

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Architecture for Dynamic Allocation of Computer Vision Tasks 16

Architecture for Dynamic Allocation of Computer Vision Tasks

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10th international conference on Distributed Smart Cameras (ICDSC)

作者： Weissenfeld, Axel Opitz, Andreas Pflugfelder, Roman Fernandez, Gustavo Austrian Inst Technol Donau City Str 1 A-1220 Vienna Austria

ISBN: (纸本)9781450347860

the use of reconfigurable computer vision architecture for image processing tasks is an important and challenging application in real time systems with limited resources. It is an emerging field as new computing architectures are developed, new algorithms are proposed and users define new emerging applications in surveillance. In this paper, a computer vision architecture capable of reconfiguring the processing chain of computer vision algorithms is summarised. the processing chain consists of multiple computer vision tasks, which can be distributed over various computing units. One key characteristic of the designed architecture is graceful degradation, which prevents the system from failure. this system characteristic is achieved by distributing computer vision tasks to other nodes and parametrizing each task depending on the specified quality-of-service. Experiments using an object detector applied to a public dataset are presented.

关键词： Passenger tracking reconfigurable vision processing architectures QoS Management

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Two architectures for parallel processing of Huge Amounts of Text 10

Two Architectures for Parallel Processing of Huge Amounts of...

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10th international conference on Language Resources and Evaluation (LREC)

作者： Kattenberg, Mathijs Beloki, Zuhaitz Soroa, Aitor Artola, Xabier Fokkens, Antske Huygen, Paul Verstoep, Kees SURFsara Sci Pk 140 NL-1098 XG Amsterdam Netherlands Univ Basque Country IXA NLP Grp 649 Posta Kutxa Donostia San Sebastian 20080 Spain Vrije Univ Network Inst Boelelaan 1105 NL-1081 HV Amsterdam Netherlands

ISBN: (纸本)9782951740891

this paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing. the two architectures focus on different processing scenarios, namely batch-processing and streaming processing. the batch-processing scenario aims at optimizing the overall throughput of the system, i.e., minimizing the overall time spent on processing all documents. the streaming architecture aims to minimize the time to process real-time incoming documents and is therefore especially suitable for live feeds. the paper presents experiments with both architectures, and reports the overall gain when they are used for batch as well as for streaming processing. All the software described in the paper is publicly available under free licenses.

关键词： parallel processing big data system architecture

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Scale-invariant recognition by weight-shared CNNs in parallel 9

Scale-invariant recognition by weight-shared CNNs in paralle...

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9th Asian conference on Machine Learning, ACML 2017

作者： Takahashi, Ryo Matsubara, Takashi Uehara, Kuniaki Graduate School of System Informatics Kobe University 1-1 Rokko-dai Nada Kobe Hyogo657-8501 Japan

Deep convolutional neural networks (CNNs) have become one of the most successful methods for image processing tasks in past few years. Recent studies on modern residual architectures, enabling CNNs to be much deeper, have achieved much better results thanks to their high expressive ability by numerous parameters. In general, CNNs are known to have the robustness to the small parallel shift of objects in images by their local receptive fields, weight parameters shared by each unit, and pooling layers sandwiching them. However, CNNs have a limited robustness to the other geometric transformations such as scaling and rotation, and this lack becomes an obstacle to performance improvement even now. this paper proposes a novel network architecture, the weight-shared multi-stage network (WSMS-Net), and focuses on acquiring the scale invariance by constructing of multiple stages of CNNs. the WSMS-Net is easily combined with existing deep CNNs, enables existing deep CNNs to acquire a robustness to the scaling, and therefore, achieves higher classification accuracy on CIFAR-10, CIFAR-100 and ImageNet datasets. © 2017 R. Takahashi, T. Matsubara & K. Uehara.

关键词： Image classification

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

15th International Conference on Algorithms and Architecture...

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

ISBN: (纸本)9783319271361

the proceedings contain 59 papers. the special focus in this conference is on Applications of parallel and Distributed Computing. the topics include: On exploring a virtual agent negotiation inspired approach for route guidance in urban traffic networks;optimization of binomial option pricing on intel MIC heterogeneous system;stencil computations on HPC-oriented ARMv8 64-bit multi-core processor;a particle swarm optimization algorithm for controller placement problem in software defined network;a streaming execution method for multi-services in mobile cloud computing;economy-oriented deadline scheduling policy for render system using IaaS cloud;towards detailed tissue-scale 3D simulations of electrical activity and calcium handling in the human cardiac ventricle;task parallel implementation of matrix multiplication on multi-socket multi-core architectures;refactoring for separation of concurrent concerns;exploiting scalable parallelism for remote sensing analysis models by data transformation graph;resource-efficient vibration data collection in cyber-physical systems;a new approach for vehicle recognition and tracking in multi-camera traffic system;a scalable distributed fingerprint identification system;energy saving and load balancing for SDN based on multi-objective particle swarm optimization;pre-stack kirchhoff time migration on hadoop and spark;a cyber physical system with GPU for CNC applications;a solution of the controller placement problem in software defined networks;parallel column subset selection of kernel matrix for scaling up support vector machines;real-time deconvolution with GPU and spark for big imaging data analysis and parallel kirchhoff pre-stack depth migration on large high performance clusters.

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

15th International Conference on Algorithms and Architecture...

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

ISBN: (纸本)9783319271217

the proceedings contain 52 papers. the special focus in this conference is on Big Data and Its Applications. the topics include: Preference-aware HDFS for hybrid storage;urban traffic congestion prediction using floating car trajectory data;a metadata cooperative caching architecture based on SSD and DRAM for file systems;parallel training GBRT based on kmeans histogram approximation for big data;an intelligent clustering algorithm based on mutual reinforcement;an effective method for gender classification with convolutional neural networks;a highly efficient indexing and retrieving method for astronomical big data of time series images;specialized FPGA-based accelerator architecture for data-intensive k-means algorithms;effectively identifying hot data in large-scale I/O streams with enhanced temporal locality;a search-efficient hybrid storage system for massive text data;enhancing parallel data loading for large scale scientific database;tradeoff between the price of distributing a database and its collusion resistance based on concatenated codes;a mapreduce reinforced distributed sequential pattern mining algorithm;a fast documents classification method based on simhash;identification of natural images and computer generated graphics using multi-fractal differences of PRNU;enriching document representation with the deviations of word co-occurrence frequencies;big data analytics and visualization with spatio-temporal correlations for traffic accidents;a novel app recommendation method based on SVD and social influence;a segmentation hybrid index structure for temporal data and a refactored content-aware host-side SSD cache.

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parallelism hardware computation for Artificial Neural Network

Parallelism hardware computation for Artificial Neural Netwo...

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IEEE/ACS international conference on Computer Systems and Applications

作者： GAM Marwa Boubaker Mohamed Chalbi Najoua Bedoui Mohamed Hedi ENISO Université de Monastir LabTIM Université de Monastir Monastir Tunisie

this paper presents an optimizing methodology for the implementation of a Learning Vector Quantization (LVQ) Artificial Neural Network (ANN). Starting from a high-level algorithmic specification, we suggest a design methodology of the LVQ-dedicated architecture. Our approach is based on the creation of partially parallel architectures to optimize the performance of our ANN for different topologies. In this manuscript, we used parallelism for the supervisor part of the application, which is responsible for calculating minimum distances, weights and labels, to solve the problems of application delay and the power consumed. In this work we integrate the Partial Dynamic Reconfiguration (PDR) to facilitate the use of different architectures by the user. the latter can implement the architecture and performance that suits their need. therefore, our approach has reduced the latency of parallel architectures with respect to the sequential architecture of an LVQ for variable topologies. To validate our approach, the optimized LVQ implementation was tried on the Zynq device.

关键词： Artificial neural networks Computer architecture Heuristic algorithms parallel processing Topology Neurons Hardware

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

15th International Conference on Algorithms and Architecture...

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15th international conference on algorithms and architectures for parallel processing, ICA3PP 2015

ISBN: (纸本)9783319271187

the proceedings contain 58 papers. the special focus in this conference is on parallel and Distributed architectures. the topics include: parallelizing block cryptography algorithms on speculative multicores;performance characterization and optimization for intel xeon phi coprocessor;an extended MDS code to improve single write performance of disk arrays for correcting triple disk failures;a distributed location-based service discovery protocol for vehicular ad-hoc networks;unified virtual memory support for deep CNN accelerator on SoC FPGA;dynamic time slice scheduler for virtual machine monitor;memory-aware NoC application mapping based on adaptive genetic algorithm;a study on non-volatile 3D stacked memory for big data applications;parallel implementation of dense optical flow computation on many-core processor;a power-conserving online scheduling scheme for video streaming services;prevent deadlock and remove blocking for self-timed systems;improving the memory efficiency of in-memory mapreduce based HPC systems;dual best-first search mapping algorithm for shared-cache multicore processors;energy efficient network-on-chip router with heterogeneous virtual channels;availability and network-aware mapreduce task scheduling over the internet;an optimized algorithm based on CRS codes in big data storage systems;quantum computer simulation on multi-GPU incorporating data locality;query execution optimization based on incremental update in database distributed middleware;coding-based cooperative caching in data broadcast environments;usage history-directed power management for smartphones;a mobile application distribution method and a clustering algorithm based on rough sets for the recommendation domain in trust-based access control.

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A Machine Learning Approach for Efficient parallel Simulation of Beam Dynamics on GPUs

A Machine Learning Approach for Efficient Parallel Simulatio...

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international conference on parallel processing (ICPP)

作者： Kamesh Arumugam Desh Ranjan Mohammad Zubair Balša Terzić Alexander Godunov Tunazzina Islam Center for Accelerator Science Old Dominion University Norfolk Virginia Department of Physics Old Dominion University Norfolk Virginia

parallel computing architectures like GPUs have traditionally been used to accelerate applications with dense and highly-structured workloads; however, many important applications in science and engineering are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Numerical simulation of charged particle beam dynamics is one such application where the distribution of work and data in the accurate computation of collective effects at each time step is irregular and exhibits control-flow and memory access patterns that are not readily amenable to GPU's architecture. algorithms with these properties tend to present both significant branch and memory divergence on GPUs which leads to severe performance *** present a novel cache-aware algorithm that uses machine learning to address this problem. the algorithm presented here uses supervised learning to adaptively model and track irregular access patterns in the computation of collective effects at each time step of the simulation to anticipate the future control-flow and data access patterns. Access pattern forecast are then used to formulate runtime decisions that minimize branch and memory divergence on GPUs, thereby improving the performance of collective effects computation at a future time step based on the observations from earlier time steps. Experimental results on NVIDIA Tesla K40 GPU shows that our approach is effective in maximizing data reuse, ensuring workload balance among parallel threads, and in minimizing both branch and memory divergence. Further, the parallel implementation delivers up to 485 Gflops of double precision performance, which translates to a speedup of up to 2.5X compared to the fastest known GPU implementation.

关键词： Computational modeling Data models Heuristic algorithms Graphics processing units Predictive models Two dimensional displays Instruction sets

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Low-Power architectures for miRNA-Target Genome Wide Analysis

Low-Power Architectures for miRNA-Target Genome Wide Analysi...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Stefano Beretta Lucia Morganti Elena Corni Andrea Ferraro Daniele Cesini Daniele D'Agostino Luciano Milanesi Ivan Merelli Istituto di Tecnologie Biomediche Consiglio Nazionale delle Ricerche Segrate Lombardia IT Italian Inst. for Nucl. Phys. Bologna Italy Inst. for Appl. Math. & Inf. Technol. “E. Magenes” Genoa Italy

ISBN: (纸本)9781509060597

In molecular biology, the interaction mechanisms between microRNAs (miRNAs) with their messenger RNA targets are poorly understood. this is the reason why many miRNA-target prediction methods are available, but their results are often inconsistent. A lot of efforts focus on the quality of the sequence match between miRNA and target rather than on the role of the mRNA secondary structure in which the target is embedded. Nonetheless, it is known that the miRNA secondary structures contribute to target recognition, because there is an energetic cost to freeing base-pairing interactions within mRNA to make the target accessible for miRNA binding. this approach is implemented by PITA (Probability of Interaction by Target Accessibility), a very computational-intensive tool that is able to provide accurate results even when little is know about the conservation of the miRNA. In this paper we propose a new implementation of PITA, called lPITA, able to exploit a coarse-grained parallelism over low power architectures to reduce both execution times and the power consumption.

关键词： Computer architecture Prediction algorithms Genomics Bioinformatics RNA Power demand Master-slave

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