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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Efficient distributed parallel Inference Strategies via Block-based DNN Structure in Edge-to-IoT Continuum 24

Efficient Distributed Parallel Inference Strategies via Bloc...

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9th international conference on Intelligent Information Technology (ICIIT)

作者： Choi, Inhun Akhter, Sharmen Jeong, Hong-Ju Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Gyeonggi Do 17104 South Korea Kyung Hee Univ Dept Artificial Intelligence Yeonggi Do 17104 South Korea

ISBN: (纸本)9798400716713

Recently, AI and deep neural networks have found extensive applications in mobile devices, drones, carts, and more. To meet the demands of processing large-scale data and providing DNN inference services with minimal latency, there is a need. However, IoT devices, with their limited computing capabilities, are not well-suited for AI inference. Moreover, considering the diverse requirements of different services, it is necessary to provide inference services that address these variations. To address these challenges, many previous studies have explored collaborative approaches between edge servers and cloud servers by partitioning DNN models. However, these methods face difficulties in finding optimal partitioning points for splitting DNN models and are heavily influenced by network bandwidth since intermediate computation results need to be transmitted to other devices. In this paper, we propose the Adaptive block-based DNN network inference framework. This involves breaking down a large DNN model into block-level networks, training them using knowledge distillation techniques to enable inference only through each block network. Subsequently, dynamic block-level inference calculations are offloaded based on the computing capabilities of edge clusters, providing inference results. Even when using multiple devices, our method is not affected by network bandwidth since only input images need to be transmitted. Experimental results demonstrate that our approach consistently reduces inference latency as the number of devices increases. Additionally, by controlling the trade-off between latency and accuracy, we can provide inference services tailored to various latency requirements.

关键词： DNN inference Block-based DNN network trade-off between latency and accuracy

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A Priority-Driven Deep Reinforcement Learning Scheduler for Resource Management 34

A Priority-Driven Deep Reinforcement Learning Scheduler for ...

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34th Annual international conference on Collaborative Advances in Software and computing, CASCON 2024

作者： Ayadi, Hajer An, Aijun Shao, Yiming Deng, Junjie Huang, Jimmy X. Feiman, Michael Zhou, Hao York university TorontoON Canada IBM Spectrum Computing TorontoON Canada

ISBN: (纸本)9798331504830

Deep neural networks (DNNs) have gained popularity in various fields, including computer vision and speech recognition. However, the growing size of data and complexity of models have made training DNNs increasingly time-consuming. To accelerate computation, parallel and distributed training of DNNs using multiple GPUs is widely adopted. This approach, however, introduces significant challenges in managing and scheduling GPU resources. One critical challenge is job prioritization, which ensures that urgent jobs are processed promptly. Current solutions often overlook prioritization settings, leading to suboptimal performance. For example, neglecting prioritization can result in long delays for urgent jobs. Conversely, overly prioritizing urgent jobs without balancing the needs of other jobs can lead to extended completion times (JCT) for less urgent tasks. While existing approaches that incorporate prioritization typically rely on heuristic-based methods, which may not be optimal, deep reinforcement learning (DRL)-based methods offer greater adaptability and can generally outperform traditional heuristic-based approaches. To address these challenges, we introduce Prioritize, a novel GPU job scheduler designed to enhance GPU resource management in distributed DNN training. Prioritize employs a DRL model to efficiently select jobs based on their priority. To minimize the JCT for lower-priority jobs, Prioritize dynamically increases their priority over time, thereby reducing their queueing delays. The DRL model maximizes a priority-based reward to ensure effective job selection. Extensive experiments demonstrate that Prioritize outperforms several baseline methods in terms of queueing time, JCT, and Makespan. The proposed approach not only achieves superior scheduling performance but also effectively addresses prioritization challenges, making Prioritize a promising solution for efficient GPU job scheduling in distributed DNN training scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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Adaboost-based Graph Convolutional networks with Channel Attention for Intrusion Detection in Internet-of-Things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of Things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. This leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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IMpart: A Partitioning-based parallel Approach to Accelerate Influence Maximization 29

IMpart: A Partitioning-based Parallel Approach to Accelerate...

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29th Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Barik, Reet Minutoli, Marco Halappanavar, Mahantesh Kalyanaraman, Ananth Washington State Univ Pullman WA 99164 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781665494236

Influence maximization (IM) is a fundamental operation among graph problems that involve simulating a stochastic diffusion process on real-world networks. Given a graph G(V, E), the objective is to identify a small set of key influential "seeds"i.e., a fixed-size set of k nodes, which when influenced is likely to lead to the maximum number of nodes in the network getting influenced. The problem has numerous applications including (but not limited to) viral marketing in social networks, epidemic control in contact networks, and in finding influential proteins in molecular networks. Despite its importance, application of influence maximization at scale continues to pose significant challenges. While the problem is NP-hard, efficient approximation algorithms that use greedy hill climbing are used in practice. However those algorithms consume hours of multithreaded execution time even on modest-sized inputs with hundreds of thousands of nodes. In this paper, we present IMpart, a partitioning-based approach to accelerate greedy hill climbing based IM approaches on both shared and distributed memory computers. In particular, we present two parallel algorithmsone that uses graph partitioning (IMpart-metis) and another that uses community-aware partitioning (IMpart-gratis)with provable guarantees on the quality of approximation. Experimental results show that our approaches are able to deliver two to three orders of magnitude speedup over a state-of-the-art multithreaded hill climbing implementation with negligible loss in quality. For instance, on one of the modest-sized inputs (Slashdot: 73K nodes;905K edges), our partitioning-based shared memory implementation yields 4610x speedup, reducing the runtime from 9h 36m to 7 seconds on 128 threads. Furthermore, our distributed memory implementation enhances problem size reach to graph inputs with x10(6) nodes and x10(8) edges and enables sub-minute computation of IM solutions.

关键词： community detection graph partitioning greedy hill climbing Influence maximization parallel seed selection

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PIOD: An Efficient parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai 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 over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA parallel computing

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Content-driven Closeness Centrality based Caching in Softwarized Edge networks

Content-driven Closeness Centrality based Caching in Softwar...

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IEEE international conference on Communications (IEEE ICC)

作者： Amadeo, Marica Ruggeri, Giuseppe Campolo, Claudia Molinaro, Antonella Univ Mediterranea Reggio Calabria Reggio Di Calabria Italy

ISBN: (纸本)9781538674628

The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy distributed caching services at the network edge, close to the end users, in order to speed up the data retrieval and reduce the bandwidth demands. In parallel, centralized paradigms like Software Defined Networking (SDN) are considered to optimize network management while supporting a variety of network applications like routing, load balancing and caching. In this paper, we extend the SDN control plane to support a novel content caching strategy. We consider a softwarised edge network domain where SDN nodes, augmented with storage capabilities, cache incoming data with the twofold target of limiting the retrieval delay and the inter-domain traffic. The caching decision is taken in a centralized mode by the SDN Controller, according to a newly defined content-driven closeness centrality metric, which identifies the importance of the SDN nodes as cachers based on their proximity to the majority of the clients requesting the most popular contents. Simulation results show the superiority of the solution in terms of higher cache hits and reduced latency, when compared against benchmark caching strategies.

关键词： Caching Edge computing SDN closeness centrality

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A Hash-based Multidimensional Graph Neural Network Approach for Zero Trust Oriented Access Control Security 29

A Hash-based Multidimensional Graph Neural Network Approach ...

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

作者： Shi, Fengfan Cao, Sheng Zhang, Xiaosong School of Computer Science and Engineering China Yibin Park University of Electronic Science and Technology of China China

ISBN: (纸本)9798350330717

With the continuous development of technologies like cloud computing, the network edge is gradually fading away. Zero Trust, as a novel concept in network security, provides new insights into access control. However, the features advocated by Zero Trust Architecture (ZTA), such as continuous verification and dynamic access control, significantly increase computational and management overhead. Existing methods often lack comprehensive analysis of user behavior patterns and overlook the semantic information of the data, resulting in inaccurate access control decisions. To realize dynamic and accurate access control in ZTA, we propose a hash-based multidimensional graph neural network approach for access control, which models user behavior patterns, combining feature hashing and multidimensional graph neural networks to achieve deep feature extraction and accurate access decisions. Experimental results demonstrate that our approach can effectively improve the performance of access decisions, meeting the security requirements of ZTA. © 2023 IEEE.

关键词： Access Control Feature Hashing Multidimensional Graph Neural networks Zero Trust Architecture

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DistSim: A performance model of large-scale hybrid distributed DNN training 23

DistSim: A performance model of large-scale hybrid distribut...

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20th ACM international conference on computing Frontiers (CF)

作者： Lu, Guandong Chen, Runzhe Wang, Yakai Zhou, Yangjie Zhang, Rui Hu, Zheng Miao, Yanming Cai, Zhifang Li, Li Leng, Jingwen Guo, Minyi Shanghai Jiao Tong Univ Shanghai Qi Zhi Inst Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400701405

With the ever-increasing computational demand of DNN training workloads, distributed training has been widely adopted. A combination of data, model and pipeline parallelism strategy, called hybrid parallelism distributed training, is imported to tackle the problem of deploying large-scale models. However, how to evaluate the hybrid strategy and the utilization of each device remains a challenge since existing works either profile on a real large-scale cluster with high time and money costs or only analyze a specific type of parallelism without considering the hybrid parallelism. In this work, we proposed DistSim, an event-based performance model to accurately analyze each device's computation and communication activities with low profiling costs. DistDim breaks down the model into events according to the given distributed strategy, which can be profiled on two nodes. Then DistSim leverages the hierarchy of different parallel strategies to generate the computation and communication event-flow from layer level to model level and finally the activity timeline of each device participating in training. Experiment shows that DistSim can reach <4% errors when predicting distributing training batch time and <5% errors when predicting a single device's activity time in various hybrid strategy settings. We also provide a use-case of DistSim, automatically evaluate and search the best distributed training strategy, and find a hybrid strategy with at most 7.37x throughput improvement.

关键词： distributed DNN training performance model

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