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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Load Balancing based Scheduling for Mixed Real Time Tasks on Multicore Systems

Load Balancing based Scheduling for Mixed Real Time Tasks on...

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Shruti Jadon Pradyuman K.K. Urmil Kalaria K.R. Varsha Karthik Gupta Dept of Computer Science PES University Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Effective load balancing is essential for ensuring the efficient operation of multi-core systems, particularly in environments with diverse task priorities and deadlines. The primary objective is to intelligently distribute tasks among cores to optimize system responsiveness, prioritizing the timely completion of urgent tasks. Through rigorous computer simulations, this paper explores various load-balancing techniques tailored for multi-core systems operating in mixed real-time environments. Notably, the focus is on strategies applicable to such systems, excluding hardware-level optimizations and specific real-time scheduling algorithms. The paper introduces an algorithm aimed at enhancing both load balancing and response time. This involves leveraging the LBPSA (Load Balancing-based Partitioned Scheduling Algorithm) for load balancing and implementing the TBS (Total Bandwidth Server) migration method to further improve response time. The insights and findings presented offer valuable guidance for researchers, system designers, and practitioners involved in load balancing within mixed real-time multi-core systems across a broad spectrum of applications. The LBPSA and TBS methods are presented as contributions of the paper, enhancing the existing body of knowledge in load-balancing techniques for multi-core systems.

关键词： Scheduling algorithms Multicore processing Bandwidth Mean square error methods Load management Real-time systems Partitioning algorithms Time factors Servers Optimization

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Restoration Method of Motion Blurred image Based on Feature Fusion and Particle Swarm Optimization Algorithm

Restoration Method of Motion Blurred Image Based on Feature ...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Feng Liu Physical Education Research Department Xinjiang University Urumqi China

The current computer situation is that people generally have home computers. With the rise and prosperity of the Internet era such as short video, people have higher and higher requirements for high-definition status of pictures and videos. In addition, some people hope to use Internet technology to repair and clarify ancient photos and sports videos. Therefore, in order to make the motion blurred image clearer, this paper studies the image restoration method with the help of feature fusion and particle swarm optimization algorithm. In this paper, we mainly use analytic hierarchy process (AHP) and experimental case analysis and comparison to restore blurred images in different motion states. The experimental results show that the maximum time difference of the proposed method for processing motion blurred images can be 13.1s, which is much smaller than the other two methods.

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***: Integrated parallel Density-Based Clustering Through Locality-Sensitive Hashing 28th

***: Integrated Parallel Density-Based Clustering Through Lo...

引用

28th International conference on parallel and distributed Computing (Euro-Par)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783031125973;9783031125966

Locality-sensitive hashing (LSH) is an established method for fast data indexing and approximate similarity search, with useful parallelism properties. Although indexes and similarity measures are key for data clustering, little has been investigated on the benefits of LSH in the problem. Our proposition is that LSH can be extremely beneficial for parallelizing high-dimensional density-based clustering e.g., DBSCAN, a versatile method able to detect clusters of different shapes and sizes. We contribute to fill the gap between the advancements in LSH and density-based clustering. We show how approximate DBSCAN clustering can be fused into the process of creating an LSH index, and, through parallelization and fine-grained synchronization, also utilize efficiently available computing capacity. The resulting method, ***, can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse its properties and evaluate our prototype implementation on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. Our results show that *** effectively complements established state-of-the-art methods by up to several orders of magnitude of speed-up on higher dimensional datasets, with tunable high clustering accuracy through LSH parameters.

关键词： Clustering algorithms

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Accelerating Deep Learning Tasks with Optimized GPU-assisted image Decoding 26

Accelerating Deep Learning Tasks with Optimized GPU-assisted...

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26th IEEE International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Wang, Lipeng Luo, Qiong Yan, Shengen HKUST Dept Comp Sci & Engn Hong Kong Peoples R China SenseTime Res Shenzhen Peoples R China

ISBN: (纸本)9781728190747

In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time;(2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching, systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fist mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5x over existing GPU-assisted image decoding libraries on real-world DL tasks.

关键词： deep learning image decoding parallel processing heterogeneous processing GPU

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study

An empirical study on the structure evolution of deep learni...

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IEEE International conference on Joint Cloud Computing (JCC)

作者： Huanxi Liu Xiang He Dawei Feng Han Bao National Laboratory for Parallel and Distributed Processing (PDL) National University of Defense Technology China School of Information and Communication National University of Defense Technology China

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

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Query Optimization and Rebalancing methods based on CMD 2021

Query Optimization and Rebalancing Methods based on CMD

引用

3rd International conference on Big Data Engineering and Technology (BDET)

作者： Wang, Shuo Zhang, Zhaogong Meng, Qingyu Heilongjiang Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450389280

As the amount of user data increase, the computer performance and I/O speed required for data processing and analysis are getting higher and higher. distributed file system has become the primary option for big data storage and query. According to the characteristics of high dimensionality and sparseness of data, this paper uses the distributed storage idea of CMD (coordinate modulo distribution) to store data in blocks. We only need to use cheap storage devices to form a distributed storage system, which solves the problem of big data disk I/O read performance to a certain extent. We have improved range query function under the CMD storage method;at the same time, the optimized B+ tree index technology has been used to solve the precise search problem of sparse data. Finally, in view of the unbalanced distribution of different sub-node data, we propose a new data rebalancing method on the CMD storage method.

关键词： Coordinate modulo distribution Multidimensional parallel query Data rebalance parallel storage

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Towards a Healthier Online Environment: Machine Learning for Detecting and Addressing Cyberbullying Comments

Towards a Healthier Online Environment: Machine Learning for...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： G S Pradeep Ghantasala P Vidyullatha Rajesh Sharma R U Ananthanagu Akey Sungheetha Kumar Dilip Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Dept. of CSE Koneru Lakshmaiah Education Foundation Guntur India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

With the internet enduring to engage in a momentous part in our lives, it is crucial to prioritize maintaining inclusive and positive interactions on internet platforms. With the rise in internet usage, platforms like Twitter have become a hub for individuals to freely express their thoughts and engage with others. However, this increased user base has also led to an increase in cyber bullying comments which could include hate speech, harassment, or assault language. Sentiment analysis comprises consuming natural language processing methods to consider the sentimentality conveyed with a slice of text, like as a comment or a tweet. It can help identify whether a comment is positive, negative, or neutral, allowing platforms to take appropriate action. This paper focusses on Machine Learning Models to deploy sentiment analysis to classify cyber bullying comments and promoting positive interactions on internet platforms. By leveraging this technology, platforms like Twitter can take proactive measures to adopt a protected and more comprehensive online environment, ultimately enhancing the user experience for everyone involved.

关键词： Support vector machines Sentiment analysis Accuracy Blogs Cyberbullying Machine learning Transformers Data models Classification algorithms Bayes methods

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1st International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning Algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation Techniques and Optimization Algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-II: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power Applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in distributed Cyber Physical System;Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP Applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG Signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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New distributed Partitioning Approach for Large Spatial Networks

New Distributed Partitioning Approach for Large Spatial Netw...

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Digital Age & Technological Advances for Sustainable Development (ICDATA), International conference on

作者： Hafssa Aggour Aziz Mabrouk Approximation Problem Modeling & Data Science Research Team Abdelmalek Essaadi University Tétouan Morocco

In urban areas, geographic spread of spatial networks continually produces massive data. In fact, the analysis of these spatial networks is squarely related to the partitioning of networks into sub-networks in a balanced manner using scalable analysis methods with the aim of minimizing processing time, namely, partitioning parallel and distributed with (Big Data, ML). Our objective, consist to partition in a distributed way a large spatial network into several Subnets. Therefore, we are conducting a comparative study of distributed partitioning algorithms such as Clustering and Hierarchical Clustering methods with the method that we have proposed in another paper [26] allowing to decompose the spatial network into sub-networks taking into account the Euclidean proximity between the nodes and the different Voronoï generators. the comparative study of the three partitioning methods that we have implemented, namely, Clustering, Hierarchical Clustering and our proposed method, have allowed us to deduce that our method is the most reliable and competitive compared to others, in terms of balance partitions as well as in terms of optimizing the partitioning time.

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FedCS: Communication-Efficient Federated Learning with Compressive Sensing

FedCS: Communication-Efficient Federated Learning with Compr...

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International conference on parallel and distributed Systems (ICPADS)

作者： Ye Liu Shan Chang Yiqi Liu Computer Science and Technology Donghua University Shanghai China

In Federated Learning (FL), two-way model exchanges are required between the server and the workers every training round. Due to the large size of machine learning models, communications between them lead to high training delay and economic cost. At present, communication-efficient FL methods, for examples, top-k sparsification and quantization, taking advantages of the sparseness of model gradients and the fact that gradient-based model updating can tolerance small deviations, effectively reduce the communication cost of single training round. However, these gradient-based communication-efficient schemes cannot be applied to downlink communication. In addition, they cannot be used in conjunction with those communicationfrequency-suppressed methods, e.g., FedAvg, which hinders them from further improving training efficiency. In this paper, we propose FedCS, a compressive sensing based FL method, which can effectively compress and accurately reconstruct non-sparse model (both local and global) parameters (iveights), and can reduce the overall communication cost up to 10 $\times$ as compared to FedAvg without decreasing test accuracy. We introduce 1) a dictionary learning scheme with a quasi-validation set, which helps to project non-sparse parameters onto a sparse domain; 2) ajoint reconstruction scheme, by using which the server recovers global model parameters by executing the reconstruction algorithm only once a round, regardless of the number of compressed local models; 3) a compression ratio adjustment strategy, which balances the trade-off between total communication cost and model accuracy. We perform FedCS on three image classification tasks, and compare it with FedAvg, FedPAQ and T-FedAvg (two improvements of FedAvg). Experimental results demonstrate that FedCS outperforms comparison methods in all tasks, and always maintains a comparable test accuracy to FedAvg, even using a small quasi-validation set and on Non-IId data.

关键词： Training Adaptation models image coding Costs Federated learning Servers Task analysis

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