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A New parallel Methodology for the Network Analysis of COVID-19 Data

A New Parallel Methodology for the Network Analysis of COVID...

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26th international conference on parallel and distributed computing (Euro-Par)

作者： Agapito, Giuseppe Milano, Marianna Cannataro, Mario Magna Graecia Univ Catanzaro Data Analyt Res Ctr I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Legal Econ & Social Sci I-88100 Catanzaro Italy

ISBN: (纸本)9783030715939;9783030715922

Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across the world. In this article, we present a new methodology for network-based analysis of Italian COVID-19 data. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data;(ii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships;(iii) the discovering communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19. Experiments was performed on real datasets about Italian regions, and they although the limited size of the Italian COVID-19 dataset, a quite linear speed-up was obtained up to six cores.

关键词： COVID-19 Network analysis parallel computing

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Network Coding for Scalable Genomic Data Transmission in Public Health Crises 3

Network Coding for Scalable Genomic Data Transmission in Pub...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Alam, Sultan Kumar, Ram Sankat, Monica School of Computing Science and Engineering Vit Bhopal University Sehore466114 India

ISBN: (纸本)9798331519582

Quick genomic data transfer acts as an essential tool that public health teams need during emergencies like the COVID-19 outbreak. Network coding stands as a technology evaluated for improving large-scale genomic data transmissions in distributed networks. The experimental results show that network coding leads to substantial improvement of transmission throughput through reduces times by 47.2% relative to other standard approaches. Network coding technology displays superior error resilience capability because it recovers 92.3% of data at 10% error rate when compared to normal transmission methods which recover only 65.4%. The introduction of slight computational workloads provides high data integrity protection by maintaining reconstruction accuracy above 98.4% and achieving complete integrity for both small and large datasets. The utilization rate of networks can reach 89.5% across multiple server environments. The study demonstrates how network coding technology optimizes genetic data sharing processes by delivering quicker and more resilient and expandable information distribution for genomic research along with public health intervention programs. © 2025 IEEE.

关键词： parallel processing systems

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Efficient and Systematic Partitioning of Large and Deep Neural networks for parallelization 27th

Efficient and Systematic Partitioning of Large and Deep Neur...

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Wang, Haoran Li, Chong Tachon, Thibaut Wang, Hongxing Yang, Sheng Limet, Sebastien Robert, Sophie Huawei Technol Paris France Univ Orleans Orleans France

ISBN: (纸本)9783030856656;9783030856649

Deep neural networks (DNNs) are playing an increasingly important role in our daily life. Since the size of DNNs is continuously growing up, it is highly important to train them effectively by distributing computation on multiple connected devices. The efficiency of training depends on the quality of chosen parallelization strategy. Being able to find a good parallelization strategy for a DNN in a reasonable amount of time is not trivial. Previous research demonstrated the possibility to systematically generate good parallelization strategies. However, systematic partitioning still suffers from either a heavy preprocessing or poor quality of parallelization. In this paper, we take a purely symbolic analysis approach by leveraging the features of DNNs like dense tensor balanced computation. We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy. The experiments show that our solution significantly reduces the parallelization strategy generation time from hours to seconds while maintaining the parallelization quality.

关键词： distributed algorithm distributed machine learning Neural network partitioning

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Architecture of a distributed parallel computing System Using Docker Cluster 6

Architecture of a Distributed Parallel Computing System Usin...

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6th international Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Sokolov, Aleksandr Larionov, Andrey Mukhtarov, Amir Fedotov, Ivan V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

One of the critical problems in computer modeling is getting numerical results from heavy computational models for large input parameter sets. In particular, this problem arises when researchers need to get accurate results for data visualization or machine learning. For instance, executing a simulation model on thousands of input parameter combinations can take days or weeks. Other tasks include estimating queuing networks properties, solving optimization problems, and various physics and economics models. This article describes an architecture of a distributed system that uses the docker technology for problem execution. The system includes a backend server, SQL and Redis databases, supervisor service and workers, where the problems are computed. Supervisor checks Redis queue for new tasks and distributes them between workers. Any input problem is automatically split into smaller chunks - tasks that workers compute. The user interacts with the system using the web interface written in JavaScript. For problem execution, the user creates a JSON file with problem description and docker image in which tasks will be executed. The system can be deployed in any public cloud. We implemented several different strategies for tasks prioritization and distribution among workers. Numerical results, presented in the paper, demonstrate the influence of the choice of the method of tasks distribution and prioritization on the duration of the computation under moderate workload. © 2022 IEEE.

关键词： Supervisory personnel

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Research on long and short-term social recommendation based on convolutional and gated recurrent units

Research on long and short-term social recommendation based ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Zihe Jia Peng Xue Zhiqiang Dai Qian Gao Xiaomeng Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users’ long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data; secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier; finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved.

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Analysis of Apple Plant Leaf Diseases Detection and Classification: A Review

Analysis of Apple Plant Leaf Diseases Detection and Classifi...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Satish Kumar Rakesh Kumar Meenu Gupta Department of Computer Science and Engineering Chandigarh University Punjab

ISBN: (纸本)9781665454025

Agriculture has a vital role in the development of any country like India where the majority of the population’s livelihood depends on agriculture. Every year, diseases and pests harm apple production by a significant amount of money. Farmers face a challenge in identifying distinct apple illnesses because the symptoms of different illnesses can be extremely similar and occur at the same time. Therefore, Leaf Disease Prediction (LDP) in apple cultivation is considerably important to overcome these problems. In this work, an overview of the taxonomy of apple plant diseases is discussed with their symptoms, cause, and effect. This study concluded that the method used for automatic detection and classification of apple plant diseases is still in its infancy. Hence, a new efficient model is required for the detection and classification of apple plant leaf disease that can save the farmer from loss as well as help them in increasing the production of apples.

关键词： Plant diseases Taxonomy Neural networks Production Grid computing Agriculture Faces

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Evaluating Multi-Level Checkpointing for distributed Deep Neural Network Training

Evaluating Multi-Level Checkpointing for Distributed Deep Ne...

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IEEE/ACM 7th international Workshop on Heterogeneous High- Performance Reconfigurable computing (H2RC) Part of international conference for High Performance computing, Networking, Storage and Analysis (SC)

作者： Anthony, Quentin Dai, Donglai X ScaleSolut LLC Columbus OH 43221 USA Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665483889

Deep learning (DL) applications are becoming one of the most important applications for HPC and cloud systems. The massive datasets and deep neural networks (DNN) used by DL applications introduce many HPC challenges. Therefore, HPC checkpoint/restart tools are an attractive choice. However, most data-parallel DL training jobs use a naive scheme, called root checkpointing, which is subject to blocking semantics and straggling forward progress. In this work, we apply a multi-level checkpointing tool (SCR-Exa) to distributed DL applications. We examine the performance of two DNN models at scale on Lassen (a leading TOP500 system), while ensuring the DNN's accuracy is maintained after restart from simulated system failures. Our test results show that multi-level checkpointing schemes are able to achieve nearly constant overhead at scale. To the best our knowledge, this study presents the first evaluation to demonstrate strong scalability of a checkpointing scheme for distributed DL without making framework-specific changes.

关键词： Deep Learning Neural networks Faulttolerance Checkpointing High Performance computing Performance Characterization MVAPICH2 MPI PyTorch Horovod

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Temporal Closing Path for PLM-based Temporal Knowledge Graph Completion

Temporal Closing Path for PLM-based Temporal Knowledge Graph...

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international Joint conference on Neural networks (IJCNN)

作者： Xin Zhou Yongxue Shan Zixuan Dong Haijiao Liu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Denfense Technology Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Temporal Knowledge Graph Completion (TKGC) aims to predict missing parts of quadruples, which is crucial for real-life knowledge graphs. Compared with methods that only use graph neural networks, the emergence of pre-trained model has introduced a trend of simultaneously leveraging text and graph structure information. However, most current methods based on pre-trained models struggle to effectively utilize both text and multi-hop graph structure information concurrently, resulting in insufficient association mining of relations. To address the challenge, we propose a novel model: Temporal Closing Path for Pre-trained Language Model-based TKGC (TCP-PLM). We obtain the temporal closing relation path of the target relation through sampling, and use the relation path as a bridge to simultaneously utilize text and multi-hop graph structure information. Moreover, the relation path serves as a tool for mining associations between relations. At the same time, due to the design of entity-independent relation paths, our model can also handle the inductive setting. Our experiments on three benchmarks, along with extensive analysis, demonstrate that our model not only achieves substantial performance enhancements across four metrics compared to other models but also adeptly handles inductive settings.

关键词： Training Measurement Knowledge engineering Bridges Analytical models Knowledge graphs Transforms

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Deep Reinforcement Learning based Dynamic Channel Bonding for Wi-Fi networks

Deep Reinforcement Learning based Dynamic Channel Bonding fo...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Hao Chen Peng Liu Lizhao You Ziyang Guo Jiajun Luo Xinghua Sun Taotao Wang Liqun Fu Department of Information and Communication Engineering Xiamen University China Shenzhen Research Institute of Xiamen University Shenzhen China Wireless Technology Lab 2012 Laboratories Huawei Technologies Co. Ltd China School of Electronics and Communication Engineering Sun Yat-sen University China College of Electronics and Information Engineering Shenzhen University Shenzhen China

This paper presents a decentralized Deep Reinforcement Learning (DRL)-based dynamic channel bonding (DCB) algorithm (i.e., drlDCB) for Wi-Fi networks. Most existing RL-based channel bonding algorithms either are centralized (i.e., requiring a central controller and global information for training), or can easily fall into local optimum (e.g., over-using channels for demand) as the number of stations increases. Moreover, they may converge slowly due to the use of measured throughput or delay over a duration as reward. In contrast, drlDCB is a purely distributed algorithm where each access point gets trained using local information, and addresses the local optimum and the slow convergence problems through the following new designs: 1) a new piecewise throughput model to avoid occupying too many channels if demand is satisfied, and 2) a new reward design using estimated throughput. The use of estimated throughput allows per-frame adaptation and fast convergence speed, while the accurate estimation generalizes the application scenarios, allowing an offline-trained model to be used in a dynamic environment. Simulation results in the static scenario with fixed loads and the dynamic scenario with random loads show that drlDCB outperforms state-of-the-art DCB algorithms in terms of network throughput, delay and convergence speed.

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Temporal Dependent Local Learning for Deep Spiking Neural networks

Temporal Dependent Local Learning for Deep Spiking Neural Ne...

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international Joint conference on Neural networks (IJCNN)

作者： Ma, Chenxiang Xu, Junhai Yu, Qiang Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China

ISBN: (纸本)9780738133669

Spiking neural networks (SNNs) are promising to replicate the efficiency of the brain by utilizing a paradigm of spike-based computation. Training a deep SNN is of great importance for solving practical tasks as well as discovering the fascinating capability of spike-based computation. The biologically plausible scheme of local learning motivates many approaches that enable training deep networks in an efficient parallel way. However, most of the existing spike-based local learning approaches show relatively low performances on challenging tasks. In this paper, we propose a new spike-based temporal dependent local learning (TDLL) algorithm, where each hidden layer of a deep SNN is independently trained with an auxiliary trainable spiking projection layer, and temporal dependency is fully employed to construct local errors for adjusting parameters. We examine the performance of the proposed TDLL with various networks on the MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets. Experimental results highlight that our method can scale up to larger networks, and more importantly, achieves relatively high accuracies on all benchmarks, which are even competitive with the ones obtained by global backpropagation-based methods. This work therefore contributes to providing an effective and efficient local learning method for deep SNNs, which could greatly benefit the developments of distributed neuromorphic computing.

关键词： Training Learning systems Neuromorphic engineering Convolution Heuristic algorithms Data visualization Computer architecture

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