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检索条件"任意字段=16th Euromicro International Conference on Parallel, Distributed and Network-Based Processing"

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Parareal with a Physics-Informed Neural network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

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

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. this suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： Parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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Communication-aware Quantization for Deep Learning Inference parallelization on Chiplet-based Accelerators 29

Communication-aware Quantization for Deep Learning Inference...

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

作者： Zou, Kaiwei Qu, Songyun Li, Wen Wang, Ying Li, Huawei Liu, Yongpan Tsinghua University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Shanxi University School of Computer and Information Technology Taiyuan China

ISBN: (纸本)9798350330717

It has recently become trendy for neural network accelerators to scale from single-core to chiplet-based multichip architecture, as the growth of neural network depth and complexity are calling for the promotion of computation and memory capabilities. However, the unintended extensive inter-chip communication of chiplet-based accelerator may bottleneck the parallelism of deep learning inference, which is undesirable for many real-time applications and energy-efficient devices. Although it is imperative for novel schemes to be devised to alleviate this problem, related works are scarce. In this work, we present CampQ, a fine-grained communication-aware mixed-precision quantization method to accelerate inference parallelization by reducing the major inter-chiplet communication overhead. By leveraging the AutoML technique, CampQ is capable of determining different bit-width to activation groups according to thier transmission distances in on-package network. the experimental results show 1.4×-2.6× performance benefits and 29%-60% energy reduction over the 16-bit models for various neural networks and parallelism approaches. © 2023 IEEE.

关键词： AutoML chiplet deep learning inference inter-chip communication quantization

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OWETC: Open world encrypted traffic classification based on semi-supervised class incremental learning 21

OWETC: Open world encrypted traffic classification based on ...

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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： Zhao, Meng Di, Zhu Xia, Zhe Tian, Jing Xiang, Jianwen Jun, Lin China Electronic Product Reliability and Environmental Testing Research Institute Guangdong China Wuhan University of Technology Hubei Key Laboratory of Transportation of Internet of Things School of Computer Science and Artifical Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China

ISBN: (纸本)9798350329223

To address the dynamic changes in network environments and effectively tackle the open-world traffic classification challenge, we introduce a novel approach. Conventional traffic classification methods are constrained by their reliance on closed traffic sets and offline training scenarios, limiting their ability to recognize a fixed number of traffic classes and identify unknown traffic types. Our solution involves a traffic classification method founded on a combination of semi-supervised class-incremental learning and open-set recognition. To mitigate the issue of catastrophic forgetting in class-incremental learning, we leverage a substantial amount of external unlabeled traffic data. Specifically, we employ a class anchor clustering loss classifier with a discriminator to select external unlabeled traffic data as replay samples. this approach effectively mitigates the problem of catastrophic forgetting in class-incremental learning. Furthermore, the class anchor clustering loss is employed for incremental model training, enabling the model to identify unknown traffic data. We validate the performance of the model on encrypted traffic classical datasets, and the experimental results show that our method significantly outperforms traffic classification models based on other incremental learning methods. In particular, OWETC has a 3.28% performance improvement in average accuracy over multiple incremental sessions compared to the best baseline. Additionally, our enhanced open-set sampler displays a notable 17% reduction in false alarm rates with only a minor 1% accuracy degradation, surpassing the capabilities of existing open-set recognition methods. © 2023 IEEE.

关键词： Classification (of information)

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Optimizing XR User Experiences through network-based Asset Bundles 16th

Optimizing XR User Experiences Through Network-Based Asset B...

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16th international conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Vergari, Maurizio Kojic, Tanja Warsinke, Maximilian Moeller, Sebastian Voigt-Antons, Jan-Niklas Abboud, Osama Xiao, Xun TU Berlin Qual & Usabil Lab Berlin Germany Huawei Technol Duesseldorf GmbH Munich Res Ctr Adv Wireless Technol Lab Munich Germany Hamm Lippstadt Univ Appl Sci Immers Real Lab Lippstadt Germany German Res Ctr Artificial Intelligence DFKI Berlin Germany

ISBN: (纸本)9783031610431;9783031610448

Even though XR apps are becoming more widely used, one common issue is the need for more processing power on hardware to handle the complexity of the objects that need to be rendered. this study tests a system that shifts the computational load to the network and transfers information from a server to an XR device to maximize different aspects of user experience (e.g., innovativeness, consistency and control). the technical implementation utilizes the Unity 3D Engine to create and construct a Virtual Reality (VR) environment that embeds a communication mechanism between the server and the XR device. It uses distributed assets kept on a server as Asset Bundles. Asset Bundles provide a technique that enables dynamic loading at runtime. this allows for the minimization of local processing demands. the chosen application exemplifies this methodology in a "Pirate Game", where players incrementally render ships. the "***" was selected as the remote server in this process due to its seamless integration with the Unity editor, facilitating the storage and retrieval of 3D models. the study's core contribution lies in testing the proposed solution and reporting loading times for 3D models from the network. Empirical results show the solution's effectiveness, revealing initial loading times of approximately 800ms, followed by subsequent loading times experiencing a notable 60 to 80% reduction. this demonstrates the practicality of the suggested technique by giving an interesting method for displaying 3D models from a network, hence increasing the XR user experience.

关键词： Virtual Reality User Experience network Communication System

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Routing Attack and Detection Methods in the RPL-based Internet of things 16

Routing Attack and Detection Methods in the RPL-based Intern...

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16th international conference on Cyber-Enabled distributed Computing and Knowledge Discover, CyberC 2024

作者： Wang, Xinlong Yang, Wei Hou, Chengqi Luo, Hongtao Jiangxi Normal University School of Software Nanchang China

ISBN: (纸本)9798331506896

Routing Protocol for Low-power and Lossy networks (RPL) was developed by the Internet Engineering Task Force (IETF) to address the specific needs of IoT networks characterized by low power, limited processing capabilities communication links. RPL aims to provide a scalable, efficient routing solution tailored to these constrained environments by optimizing energy usage and ensuring reliable communication among numerous IoT devices. Given its critical role in enabling effective communication within IoT networks, RPL is fundamental to the functionality and resilience of these systems. However, the design of RPL makes it susceptible to a range of sophisticated network attacks that can compromise network performance and security. Current datasets are unable to comprehensively cover the various attack types that RPL might be subjected to, and their data characteristics do not provide adequate support for anomaly detection algorithms. To bridge this gap, we have developed a specialized IoT dataset focusing on RPL, providing a comprehensive resource for training machine learning models to detect and analyze attacks targeting this protocol. Our study details the dataset's simulation framework and feature extraction process, highlighting the impacts of three significant routing attacks: Black Hole attack, Hello Flooding attack, and Version Number attack. We evaluate five different machine learning models for anomaly detection using this dataset and find that all models effectively detect anomalous network traffic, with the Random Forest model achieving the highest accuracy. © 2024 IEEE.

关键词： Constrained optimization

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parallelizing Multipacting Simulation for the Design of Particle Accelerator Components

Parallelizing Multipacting Simulation for the Design of Part...

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euromicro conference on parallel, distributed and network-based processing

作者： J. Galarza J. Navaridas JA. Pascual T. Romero JL. Muñoz I. Bustinduy University of the Basque Country Donostia Spain Donostia International Physics Center Donostia Spain ESS Bilbao Bilbao Spain

Particle trajectory and collision simulation is a critical step of the design and construction of novel particle accelerator components. However it requires a huge computational effort which can slow down the design process. We started from a sequential simulation program which is used to study an event called “Multipacting”. Our work explains the physical problem that is simulated and the implications it can have on the behavior of the components. then we analyze the original program's operation to find the best options for parallelization. We first developed a parallel version of the Multipacting simulation and were able to accelerate the execution up to ~ 35× with 48 or 56 cores. In the best cases, parallelization efficiency was maintained up to 16 cores (~ 95%) and the speed-up plateaus at around 40 to 48 cores. When this first parallelization effort was tried for multi-power simulations, we found that parallelism was severely limited with a maximum of 20× speed-up. For this reason, we introduced a new method to improve the parallelization efficiency for this second use case. this method uses a shared processor pool for all simulations of electrons (OnePool). OnePool improved scalability by pushing the speed-up to over 32×.

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Deploying AI-based Environmental Monitoring Applications at the Edge: Two Case Studies 15th

Deploying AI-Based Environmental Monitoring Applications at...

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15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： de Lucia, Gianluca Laccetti, Giuliano Lapegna, Marco Montella, Raffaele Romano, Diego Rome Italy Department of Mathematics and Applications Univ. of Naples Federico II Naples Italy Department of Science and Technolgy University of Naples Parthenope Naples Italy Naples Italy

ISBN: (纸本)9783031856990

the Edge Computing environments enable the development of pervasive applications distributed across extensive geographical areas, overcoming specific issues associated with centralized information processing, such as network bandwidth saturation and the need for large computing infrastructures. this work presents two case studies of deploying applications for environmental monitoring on low-energy and high-performance edge computing devices, employing accelerated artificial intelligence techniques based on GPUs. the first problem entails the classification of various materials within hyperspectral images, while the second problem focuses on identifying floating plastic debris. the applications were validated on a Nvidia Jetson Nano sensor board, demonstrating good accuracy, effectiveness, and energy consumption results. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental monitoring

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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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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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Fine Time Granularity Allocation Optimization of Multiple networks Industrial Chains in Task processing Systems 24th

Fine Time Granularity Allocation Optimization of Multiple Ne...

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24th international conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2023

作者： Li, Pan Di, Kai Bai, Xinlei Jiang, Yuanshuang Chen, Fulin School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China

ISBN: (纸本)9789819982103

As the industrial division of labor becomes increasingly specialized, various collaborative relationships between industrial chains develop, forming complex multi-networks. In the task processing system of multiple network industrial chains, there are dynamic online tasks. the arrival and deadline of these tasks cannot be accurately predicted. therefore, it is necessary to divide the scheduling into finer time granularity to improve the response speed, efficiency, and timeliness, while ensuring the task completion rate and minimizing the task cost. In this paper, we study the characteristics of online tasks in multiple networks industrial chains and design a corresponding online scheduling framework. We analyze the arrival of online tasks in real-world scenarios and propose a passive scheduling algorithm based on the characteristics of different scenarios. the algorithm is tested on several sets of simulated data. Compared with previous heuristic algorithms, our algorithm can achieve better results in terms of task completion time, energy cost, and task completion rate in scenarios with fine time granularity. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Predicate-based push-pull communication for distributed CEP 22

Predicate-based push-pull communication for distributed CEP

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16th ACM international conference on distributed and Event-based Systems, DEBS 2022

作者： Purtzel, Steven Akili, Samira Weidlich, Matthias Humboldt-Universität zu Berlin Berlin Germany

ISBN: (纸本)9781450393089

Complex event processing (CEP) enables the detection of situations of interest by evaluating queries over event streams. When applied in a networked application, events generated by distributed nodes are sent over the network to evaluate CEP queries. To reduce the transmission of events, push-based communication that sends each event immediately upon generation may be complemented with a pull-based model that buffers events locally until they are requested for query evaluation. Existing approaches that leverage push-pull communication to reduce transmission costs for distributed CEP, however, exploit solely temporal constraints imposed by a query. As such, they are not applicable in scenarios, in which relevant events may occur in each time window defined by a query. In this paper, we propose predicate-based push-pull (PrePP) plans for CEP queries to overcome the above limitations. Our idea is to construct pull requests that enable fine-granular filtering at event sources based on query predicates, thereby reducing event transmission. Since the construction of optimal PrePP plans is NP-hard, we introduce a set of algorithms to speed up the plan construction by up to five orders of magnitude compared to a brute-force approach, while producing near-optimal results. In extensive experiments, we demonstrate that PrePP plans reduce event transmission by up to three orders of magnitude over baseline techniques. © 2022 Owner/Author.

关键词： Complex networks

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