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Auto-Divide GNN: Accelerating GNN Training with Subgraph Division 29th

Auto-Divide GNN: Accelerating GNN Training with Subgraph Div...

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Chen, Hongyu Ran, Zhejiang Ge, Keshi Lai, Zhiquan Jiang, Jingfei Li, Dongsheng Natl Univ Def Technol Natl Lab Parallel & Distributed Proc PDL Comp Coll Changsha Peoples R China

ISBN: (纸本)9783031396977;9783031396984

Graph Neural networks (GNNs) have gained considerable attention in recent years for their exceptional performance on graph-structured data. Sampling-based GNN training is the most common method used for training GNNs on large-scale graphs, and it is often accelerated by caching feature data on the GPU. However, the emergence of more complex models and datasets with higher feature dimension requires more GPU memory for training, which limits the acceleration performance of GPU caching and can even result in out-ofmemory errors. To release more GPU memory for the cache in a transparent way, we propose a subgraph division method, which trains several smaller micrographs instead of an entire subgraph at each training iteration. However, it is non-trivial to combine subgraph division with GPU caching due to the redundancy between micrographs. To tackle this challenge, we introduce an auto-profile method that searches for the best-performing subgraph division scheme based on training perception and probability analysis. Additionally, we develop an estimation-based caching strategy to lift the caching hitting rate against the diverse graph structures. These ideas are integrated to Auto-Divide GNN, a framework for accelerating sampling-based GNN training. The multi-GPU evaluations on three representative GNN models and five large graphs demonstrate that Auto-Divide GNN achieves significant speedups of up to 5.61x and 3.13x over two state-of-the-art GNN frameworks, DGL and PaGraph, respectively.

关键词： Graph Neural networks Accelerate Training Subgraph Division GPU Caching

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Breaking the Mold: Exploring Innovative Strategies for Hosting the Olympic Games 24

Breaking the Mold: Exploring Innovative Strategies for Hosti...

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2024 international Academic conference on Edge computing, parallel and distributed computing, ECPDC 2024

作者： Wang, Ziyue Du, Zhuohang Wang, Lei School of Economics Management South China Normal University Guangzhou510006 China Aberdeen Institute of Data Science & Artifcial Intelligence South China Normal University Guangdong Foshan528225 China South China Normal University School of Computer Science Guangdong Guangzhou510631 China

ISBN: (纸本)9798400718168

This study addresses the declining trend in the number of countries hosting the Olympics, exploring how to systematically evaluate the benefits of hosting and proposing innovative strategies to enhance sustainability and success. Using a TOPSIS model-based evaluation system, incorporating economic, social, and environmental dimensions, the research comprehensively examines the impact of the Olympics on host countries. Machine learning algorithms, integrating LM and GA algorithms into the BP neural network, improve training efficiency, seek optimal solutions, and address challenges in model training acceleration and solution optimization in time series forecasting. Introducing "regional hosting"and "split hosting"strategies, the study dynamically simulates these strategies using the GA-LM-BP neural network model, assessing their impact on long-term benefits. Quantitative comparisons of NCV index growth rates over ten years for the two strategies reveal positive short-term and long-term comprehensive values (NCV) of the Olympics, particularly in tourism. The "regional hosting"strategy shows a significant increase in the overall benefit index when the Olympics are permanently hosted in one country. The "split hosting"strategy demonstrates nearly equal increases in NCV values for each host country. Based on experimental results, solutions for sustainable Olympic hosting are proposed, anticipating future improvements in more valuable hosting strategies. © 2024 ACM.

关键词： Neural networks

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Hybridization of Tabu Search and Grey Wolf Optimizer for Improved Workflow Scheduling and Optimization of Resources in Edge computing 2

Hybridization of Tabu Search and Grey Wolf Optimizer for Imp...

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2nd IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Rahul, Punar Singh, A. J. Himachal Pradesh Univ Dept Comp Sci Shimla India

ISBN: (纸本)9781665411202

Users may access virtual, scalable, and dynamic resources using cloud computing, which is a novel technology that charges them only for the resources they use. This technology is reliant on a network to function. Several services, like as e-commerce apps, use the whole network as a result of the sheer scope of the network. Cloud computing has progressed through three phases in terms of practical development: distributed computing, parallel computing, and grid computing. Cloud computing is a cutting-edge technology that installs networks of computers, many of which are situated in faraway locations, to conduct operations on massive amounts of data in real time, main challenge in cloud workflow Scheduling in edge context. In proposed approach we used hybrid approach using Tabu and Grey Wolf Optimization. In experiment improved results of approx. 5-10% average in all performance metrics are obtained.

关键词： Edge computing Scheduling collaborative scheduling task scheduling Workflow scheduling

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Load Balancing and parallel Computation Model for Performance and Accuracy over the Cluster of Nodes 2

Load Balancing and Parallel Computation Model for Performanc...

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2nd international conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Vasuda, V. Srividya, K. Anusha, M. Vasavi College Of Engineering Hyderabad Telangana India

ISBN: (纸本)9781665466431

Cloud computing can be online based network engineering which contributed with a rapid advancement at the progress of communication technological innovation by supplying assistance to clients of assorted conditions with aid from online computing sources. It's terms of hardware and software apps together side software growth testing and platforms applications because tools. Large-scale heterogeneous distributed computing surroundings give the assurance of usage of a huge quantity of computing tools in a comparatively low price. As a way to lessen the software development and setup onto such complicated surroundings, high speed parallel programming languages exist which have to be encouraged by complex operating techniques. There are numerous advantages for consumers in terms of cost and flexibility that come with Cloud computing's anticipated uptake. Building on well-established research in Internet solutions, networks and utility computing, virtualization et cetera Service-Oriented Architectures and the Internet of Services (IoS) have implications for a wide range of technological issues such as parallel computing and load balancing as well as high availability and scalability. Effective load balancing methods are essential to solving these issues. Since such systems' size and complexity make it impossible to concentrate job execution on a few select servers, a parallel distributed solution is required. Adaptive task load model is the name of the method wesuggest in our article for balancing the workload (ATLM). We developed an adaptive parallel distributed computing paradigm as a result of this (ADPM). While still maintaining the model's integrity, ADPM employs a more flexible synchronization approach to cut down on the amount of time synchronous operations use. As well as the ATLM load balancing technique, which solves the straggler issue caused by the performance disparity between nodes, ADPM also applies it to ensure model correctness. The results indicate that

关键词： Application programs

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AAWAS: An Application and Workload-Aware Scheduling Technique for Efficient Task Allocation in Cloud computing Environments 2

AAWAS: An Application and Workload-Aware Scheduling Techniqu...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Vidhyasagar, B.S. Arvindhan, M. Arunnehru, J. Anwarbasha, H. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Amity School of Engineering & Technology Madhya Pradesh Gwalior India Srm Institute of Science and Technology Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798350390179

One interesting aspect of distributed systems is cloud computing. It offers its services on an as-needed, pay-per-use basis. Research challenges in cloud computing include the scheduling and balance of tasks. Scheduling tasks entails assigning them to available resources (Virtual Machines), whereas load balancing is spreading out the work over multiple available resources. We offer a job scheduling technique that takes into account anticipated demands on compute nodes. We begin by investigating the reasons for the disparity in workloads and the viability of redistributing computing resources. All of the complex simulations are run in clouds. The process consisted of two distinct parts. The simulation begins with a randomly generated workload. Second, we combine the workload prediction model with an application and workload-aware scheduling algorithm (AAWAS) [14]. We present a parallel job scheduling approach using computational node workload prediction AAWAS to simplify the AAWAS algorithm. Experimental results reveal that AAWA is superior to other algorithms in terms of minimizing makespan and optimizing resource utilization, and the proposed method is compared to FCFS, SJF, Min-Min, and EDF. To make Task scheduling in compute clouds faces various challenges due to the highly dynamic environment, such as availability, access policies, security, and reliability of computing resources. Furthermore, the collaborative nature of cloud computing projects on the internet adds complexity to task scheduling. From simulation results, it is evident that our proposed scheduler AAWAS outperforms A2C, DQN, and PPO across the board by 4.1%, 2.6%, and 3.4%, respectively. Because AAWS relies on policy for learning rather than Q-value, its superiority can be defended. Several environmental factors affect AAWAS and contribute to its consistent learning process and the high chance of accurate predictions. As a result, AAWASC may learn from experiences independently of target networks,

关键词： Cloud platforms

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

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

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

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. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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1st international conference on Emerging Networking Architecture and Technologies, ICENAT 2022

1st International Conference on Emerging Networking Architec...

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1st international conference on Emerging Networking Architecture and Technologies, ICENAT 2022

ISBN: (纸本)9789811996962

The proceedings contain 50 papers. The special focus in this conference is on Emerging Networking Architecture and Technologies. The topics include: CQCF: A Cyclic Queuing Cluster Forwarding Based Transmission Mechanism for Large-Scale Deterministic Network;a Task-Resource Joint Optimization Model in distributed computing;cache Strategy for Information Center Satellite networks Based on Node Importance;intelligent Online Traffic Optimization Based on Deep Reinforcement Learning for Information-Centric networks;detection and Defense Schemes for Cache Pollution Attack in Content-Centric Network;SBR-based User Intent Extraction Mechanism for Intent-Based Network;SPOF-NDN: A POF-Based NDN Forwarding Scheme;customizable Service Identification Method and System Design for Smart Integration Identifier Network;a Dynamic Live Streaming Service Architecture Integrated Sensing and Control;Detection of SDN Flow Rule Conflicts Based on Knowledge Graph;Risk-Aware SFC Placement Method in Edge Cloud Environment;preface;An In-Network computing Service Placement Mechanism for NUMA-based Software Router;network Traffic Identification Method Based on Temporal and Multi-scale Spatial Fusion;a Trustworthy Content Moderation Scheme Based on Permissioned Blockchain;multi-agent Deep Reinforcement Learning-Based Content Caching in Cache-Enabled networks;mutual Authentication Protocol in a distributed Heterogeneous Environment: A Blockchain-Based Approach;performance Evaluation of Transmission Protocols Based on Satellite networks;optimizing Communication Topology for Collaborative Learning Across Datacenters;design of Reliable parallel Transmission System in Complex Heterogeneous Network;design of Privacy-Preserving in Multi-link Vehicle-Ground Communication;smart Mobile Router Wireless Heterogeneous Network Access Mechanism;A Comparative Study on Routing Convergence of IP-Based and ICN-Based Routing Protocols in Satellite networks;implementation and Deployment of Digital Twin in Cloud-Nativ

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Towards Energy-Aware Federated Traffic Prediction for Cellular networks 8

Towards Energy-Aware Federated Traffic Prediction for Cellul...

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8th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Perifanis, Vasileios Pavlidis, Nikolaos Yilmaz, Selim F. Wilhelmi, Francesc Guerra, Elia Miozzo, Marco Efraimidis, Pavlos S. Dini, Paolo Koutsiamanis, Remous-Aris Democritus Univ Thrace Dept Elect & Comp Engn Xanthi Greece Imperial Coll London Dept Elect & Elect Engn London England Nokia Bell Labs Radio Syst Res Stuttgart Germany Ctr Tecnol Telecomun Catalunya CTTC CERCA Sustainable Artificial Intelligence Barcelona Spain IMT Atlant INRIA LS2N Dept Automat Prod & Comp Sci Nantes France

ISBN: (纸本)9798350316971

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.

关键词： 5G/6G Federated learning Machine learning Cellular traffic prediction Sustainable AI

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