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

共 4331 条记录，以下是921-930 订阅

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Outsmarting the Atmospheric Turbulence for Ground-based Telescopes Using the Stochastic Levenberg-Marquardt Method 27th

Outsmarting the Atmospheric Turbulence for Ground-Based Tele...

引用

27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Hong, Yuxi Bergou, El Houcine Doucet, Nicolas Zhang, Hao Cranney, Jesse Ltaief, Hatem Gratadour, Damien Rigaut, Francois Keyes, David King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Mohammed VI Polytech Univ Ben Guerir Morocco Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9783030856656;9783030856649

One of the main challenges for ground-based optical astronomy is to compensate for atmospheric turbulence in near real-time. The goal is to obtain images as close as possible to the diffraction limit of the telescope. This challenge is addressed on the latest generation of giant optical telescopes by deploying multi-conjugate adaptive optics (MCAO) systems performing predictive tomography of the turbulence and multi-layer compensation. Such complex systems require a high fidelity estimate of the turbulence profile above the telescope, to be updated regularly during operations as turbulence conditions evolve. In this paper, we modify the traditional Levenberg-Marquardt (LM) algorithm by considering stochastically chosen subsystems of the full problem to identify the required parameters efficiently, while coping with the real-time challenge. While LM operates on the full set data samples, the resulting Stochastic LM (SLM) method randomly selects subsamples to compute corresponding approximate gradients and Hessians. Hence, SLM reduces the algorithmic complexity per iteration and shortens the overall time to solution, while maintaining LM's numerical robustness. We present a new convergence analysis for SLM, implement the algorithm with optimized GPU kernels, and deploy it on shared-memory systems with multiple GPU accelerators. We assess SLM in the adaptive optics system configurations in the context of the MCAO-Assisted Visible Imager & Spectrograph (MAVIS) instrument for the Very Large Telescope (VLT). We demonstrate performance superiority of SLM over the traditional LM algorithm and the classical stochastic first-order methods. At the scale of VLT AO, SLM finishes the optimization process and accurately retrieves the parameters (e.g., turbulence strength and wind speed profiles) in less than a second using up to eight NVIDIA A100 GPUs, which permits high acuity real-time throughput over a night of observations.

关键词： Non-linear optimization problems Stochastic Levenberg-Marquardt GPU computing Adaptive optics Computational astronomy Real-time processing

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Dynamic QoS Prediction based on Attention Mechanism and Recurrent Neural network

Dynamic QoS Prediction Based on Attention Mechanism and Recu...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yingxue Wang Qin Lu Yichao Wang Mengwei Wu Weixiao Li School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Information Technology Center China Mobile Communication Group Co. Ltd Beijing China

With the growth of science and technology, there are more and more services in the network, and their functions similar. It has become crucial to figure out how to recommend real-time personalized services based on user and service history. Quality of Service (QoS), as a noncritical attribute of Web services, it is a vital criterion to measure the usage performance and utility of service. The dynamic prediction of QoS is a key factor in the recommendation process. In this paper, a new time-aware QoS prediction model is proposed. It extracte global features while considering its dynamics, it capture the implicit features within a time period using Convolutional Neural networks(CNN) and Attention Mechanisms. The model not only takes into account the impact of time, but also better learns the implicit information between users and services during the time interval, forming global features within time. It study QoS at various times from the entire time period, capture key features, thereby make dynamic QoS prediction. The proposed model has better results by extensive experiments on real dataset.

关键词： Recurrent neural networks Web services Quality of service Predictive models Feature extraction Real-time systems Data mining

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A Blockchain- based Copyright Protection System for Short Videos

A Blockchain- Based Copyright Protection System for Short Vi...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Wenhao Zhao Xin Lin Yuxin Chen Yue Hong Wei Zheng School of Informatics Xiamen University Xiamen China

Short video making industry has boomed along with the advent of the wireless network in recent years. However, fre-quent copyright infringements with short videos might seriously jeopardize the development of short video making industry. To avoid this, it is required that the copyright infringement could be detected efficiently and accurately when an end-user uploads his/her video clips to an online platform. Copyright infringement detection for short videos is challenging as there might be a great number of videos in the repository and complicated copyright trading needs to be taken into account. In this paper, we propose a novel framework based on blockchain to automate the detection of copyright infringement for short videos. In the proposed framework, a similar video retrieval algorithm is used to check whether the uploaded video is too similar to any one in the repository. And if it is, the copyright ownership will be examined through a novel data structure called IP (Intellectual Property) Tree, which is used to manage the copyright authorization via blockchain, so that it can be determined whether infringement occurs. The evaluation results indicate the effectiveness of our framework on infringement detection for short videos.

关键词： Industries Authorization Wireless networks Intellectual property Data structures Blockchains

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Performance, Energy and parallelism: Using Near Data processing in Utility and Cloud Computing

Performance, Energy and Parallelism: Using Near Data Process...

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IEEE International conference on Utility and Cloud Computing (UCC)

作者： Gul Agha Dipayan Mukherjee Atul Sandur Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA

Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. The paper describes our work building on ideas based on the Actor model of computation. These include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. The paper will conclude with a presentation with a summary of open research problems.

关键词： Performance evaluation Energy consumption Cloud computing Solid modeling Computational modeling distributed databases Programming

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CNNBooster: Accelerating CNN Inference with Latency-aware Channel Pruning for GPU

CNNBooster: Accelerating CNN Inference with Latency-aware Ch...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yuting Zhu Hongxu Jiang Runhua Zhang Yonghua Zhang Dong Dong Beijing Key Laboratory of Digital Media of Beihang University Beihang University Beijing China

Channel pruning is one of the mainly used meth-ods in current network model compression. However, existing channel pruning methods lack effective hardware runtime latency guidance, making the reduction in model size not fully converted into inference latency reduction, which results in poor inference performance of the pruned network models. This paper proposes a CNN channel pruning framework, CNNBooster, that incorporates hardware runtime latency in-formation, whose two major contributions are as follows: First, CNNBooster automatically analyses the latency behavior of various CNNs with channel reduction on different GPU hard-ware platforms, which achieves efficient localization of pruna-ble coordinates. Second, CNNBooster uses a flexible grained latency-aware and param-aware pruning algorithm based on prunable coordinates to achieve a significant reduction in model inference latency and parameter size. We evaluate CNNBooster with three benchmark models (VGG16, ResNet18 and MobilenetV1) on three NVIDIA-series platforms (V100, RTX 2080 Ti and Jetson Nano). The experimental results show that CNNBooster can achieve a maximum 67.51 % latency re-duction and 1.4x performance improvement compare with current state-of-the-art works.

关键词： Location awareness Runtime Graphics processing units Benchmark testing Hardware Inference algorithms Behavioral sciences

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An Unsupervised Approach For Detecting Lateral Movement Logins based On Knowledge Graph

An Unsupervised Approach For Detecting Lateral Movement Logi...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaohan Ma Chen Li Bibo Tu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The lateral movement that relies on privacy theft is receiving increasing attention. This type of attack can be achieved by Cross-Host Login. Constructing an authentication graph between hosts is a common way to detect lateral movement, but the previous graph-based detection methods lack an effective means to integrate the account information into graph-based features. We abstract the login events as edges on the knowledge graph and account as the relations on the edges. Then we utilize a graph auto-encoder model comprised of an entity encoder and a scoring function (decoder), where entities are represented as low-dimensional vectors learned from the relational graph neural network. The decoder scores the edges to get the likelihood of the edges belonging to normal behaviors. We also propose a novel negative sample approach to reduce the false alarm rate. We apply this technique to authentication data derived from a large-scale real-world environment. The results show our approach can detect malicious authentication events associated with lateral movement with a true positive rate of 95 % and false positive rate of 10%, compared to 85% and 0.9% by the previous random walk approach as a baseline.

关键词： Privacy Image edge detection Authentication Knowledge graphs Feature extraction Resource description framework Graph neural networks

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License plate recognition system based on image recognition

License plate recognition system based on image recognition

引用

IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Liang Wang Yimei Huanga Chengqun Liang Jinrong Zhou Taoqiang Zhu Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

As people’s living standards rise, efficient and intelligent traffic management system has become the demand of the society. Although there are many researches on vehicle license plate recognition technology in China, there are still many shortcomings in practical application. To solve the above problems, this paper uses OpenCV machine vision library based on Python development platform and convolutional neural network to study the process of license plate recognition, and proposes a vehicle license plate recognition system based on machine vision technology and pattern recognition method. System using digital image correlation algorithm to detect and locate license plate, based on the analysis to extract the edge of the information, character segmentation and character quantity calculation, at last, we use Convolutional Neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates in formation. At last, we use convolution neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates information, its accuracy is as high as 98%, has the good recognition effect.

关键词： Image segmentation Image color analysis Image edge detection Neural networks Object segmentation Feature extraction Convolutional neural networks

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Task Migration based on Deep Reinforcement Learning in Mobile Crowdsourcing

Task Migration Based on Deep Reinforcement Learning in Mobil...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yongqiang Gao Zhigang Wang Zemin Li Zhenkun Li College of Computer Science Inner Mongolia University Hohhot China

As mobile devices become more powerful, mobile crowdsourcing began to emerge in technology circles. It provides a new way to reduce the load on edge servers. In mobile edge computing, the computation task needs to be dynamically migrated between multiple service nodes in order to maintain service continuity. But a service node is either an edge server or a mobile device in mobile crowdsourcing, which makes task migration more complex. In this paper, we propose a deep reinforcement learning-based task migration strategy for mobile crowdsourcing systems with the goal of meeting the task deadline requirement and minimizing the average cost of task completion. The proposed strategy firstly applies the long short-term memory (LSTM) network to predict the future movement of mobile devices and then utilizes a hybrid algorithm based on asynchronous deep Q-learning (ADQN) and ant colony optimization (ACO) to generate the optimal task migration strategy. The proposed strategy is evaluated by a simulation process. The results show that the proposed strategy performs better than the current state-of-the-art methods applied to similar problems.

关键词： Crowdsourcing Performance evaluation Costs Q-learning Prediction algorithms Mobile handsets Trajectory

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Fairness-aware Latency Minimisation in Digital Twin-aided Edge Computing with Ultra-Reliable and Low-Latency Communications: A distributed Optimisation Approach (Invited Paper)

Fairness-aware Latency Minimisation in Digital Twin-aided Ed...

引用

Asilomar conference on Signals, Systems & Computers

作者： Dang Van Huynh Van-Dinh Nguyen Saeed R. Khosravirad Trung Q. Duong Queen's University Belfast UK University of Luxembourg Luxembourg Nokia Bell Labs USA

The advanced development of communication technologies and computing platforms open opportunities to enable a wide range of time-sensitive services. However, designing an effective optimisation solution to deal with joint communication and computation resources is a challenging research direction. This paper addresses a fairness-aware latency minimisation problem in the digital twin (DT) aided edge computing with ultrareliable and low latency communications (URLLC). The optimal solution is obtained by jointly optimising various variables, namely, bandwidth allocation, transmit power, task offloading policies, and the processing rate of user equipment (UE) and edge server (ES). The formulated optimisation problem is highly complicated with many non-convex constraints and strong coupling variables. To deal with the problem, we propose a distributed optimisation solution based on the global consensus approach and the successive convex approximation framework (SCA). Selected numerical results are provided to validate the proposed solution in terms of minimise latency as well as improved fairness among all UEs in the DT network.

关键词： Simulation Ultra reliable low latency communication parallel processing Minimization Large-scale systems Servers Resource management

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Recursive Task Generation for Scalable SDF Graph Execution on Multicore Processors

Recursive Task Generation for Scalable SDF Graph Execution o...

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euromicro conference on parallel, distributed and network-based processing

作者： Georgios Georgakarakos Johan Lilius Faculty of Science and Engineering Åbo Akademi University Turku Finland

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Dataflow modelling is a popular technique for describing parallel algorithms. Using dataflow, algorithm parallelism can be modelled and analysed efficiently at a high level of abstraction. However, challenges arise when translating dataflow semantics into executable code, mainly due to scheduling and synchronization overheads. Invoking task programming models in order to generate efficient code from dataflow representations has been proposed as a promising methodology to optimise the translation *** this paper, we propose recursive task execution as an optimisation for the dataflow-based code generation process. Our approach is based on extracting synchronous dataflow graph information in order to reduce scheduling overheads and improve load balancing when executing task-based code on multicore processors. We use PREESM dataflow-based prototyping framework to implement and test our concept. Results show that our proposed optimisation enhances code scalability therefore enabling higher application throughput.

关键词： Task analysis Multicore processing Programming Binary trees parallel processing Runtime Synchronization

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