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检索条件"任意字段=Distributed Memory Computing Conference"

共 8890 条记录，以下是81-90 订阅

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection Real-time systems Internet of Things Security Servers Long short term memory Edge computing

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A Review on Detection and Mitigation Strategies of DDoS Attack in SDN Environment using Machine Learning and Deep Learning

A Review on Detection and Mitigation Strategies of DDoS Atta...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Maram Smriti Boruah Satyajit Sarmah Department of Information Technology Gauhati University Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Software-Defined Networking (SDN) has a unique architecture, however it is highly susceptible to distributed Denial-Of-Service (DDoS) attacks targeting its control and data planes. This paper reviews the use of Machine Learning (ML) and Deep Learning (DL) techniques for the detection and mitigation of DDoS attacks in Software-Defined Networking (SDN). The review examines research published between 2019 and 2024, highlighting various approaches, such as Kernel Principal Component Analysis (KPCA), Random Forest (RF), Long Short-Term memory (LSTM), and Convolutional Neural Networks (CNN), among others. These techniques are evaluated based on their effectiveness in detecting high-rate, low-rate, and zero-day attacks, with the focus on accuracy, false positive rates, and real-time adaptability. While several methods show promising results in experimental environments, challenges remain in terms of scalability, generalization, and dynamic adaptation to evolving attack patterns in large-scale deployments.

关键词： Deep learning Reviews Prevention and mitigation Scalability Denial-of-service attack Real-time systems Convolutional neural networks Software defined networking Long short term memory Random forests

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Intel® SHMEM: GPU-initiated OpenSHMEM using SYCL 24

Intel® SHMEM: GPU-initiated OpenSHMEM using SYCL

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Proceedings of the SC '24 Workshops of the International conference on High Performance computing, Network, Storage, and Analysis

作者： Alex Brooks Philip Marshall David Ozog Md. Wasi-ur-Rahman Lawrence Stewart Rithwik Tom Intel Corporation

ISBN: (纸本)9798350355543

Modern high-end systems are increasingly becoming heterogeneous, providing users options to use general purpose Graphics Processing Units (GPU) and other accelerators for additional performance. High Performance computing (HPC) and Artificial Intelligence (AI) applications are often carefully arranged to overlap communications and computation for increased efficiency on such platforms. This has led to efforts to extend popular communication libraries to support GPU awareness and more recently, GPU-initiated operations. In this paper, we present Intel SHMEM, a library that enables users to write programs that are GPU aware, in that API calls support GPU memory, and also support GPU-initiated communication operations by embedding OpenSHMEM style calls within GPU kernels. We also propose thread-collaborative extensions to the OpenSHMEM standard that can enable users to better exploit the strengths of GPUs. Our implementation adapts to choose between direct load/store from GPU and the GPU copy engine based transfer to optimize performance on different configurations.

关键词： distributed Communications

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A Performance Model of In-Situ Techniques

A Performance Model of In-Situ Techniques

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Yi Ju Nicolas Vidal Adalberto Perez Ana Gainaru Fred Suter Stefano Markidis Philipp Schlatter Scott Klaskyll Erwin Laure Max Planck Computing and Data Facility Oak Ridge National Laboratory Friedrich-Alexander-Universität Erlangen-Nürnberg KTH Royal Institute of Technology

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The computational capacity of High-Performance computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

关键词： Lead acid batteries Data analysis Accuracy High performance computing Graphics processing units Data visualization Central Processing Unit Resource management

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COSMICA: a novel parallel GPU code for Cosmic Rays propagation in heliosphere

COSMICA: a novel parallel GPU code for Cosmic Rays propagati...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Giovanni Cavallotto Stefano Della Torre Giuseppe La Vacca Massimo Gervasi Division of Milano-Bicocca ICSC - Centro Nazionale di Ricerca in HPC Big Data and Quantum Computing National Institute for Nuclear Physics (INFN) Department of Physics “Giuseppe Occhialini” University of Milano-Bicocca

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The complex structure of interplanetary magnetic fields and their variability, due to solar activity, make it necessary to compute the Cosmic Ray (CR) modulation with numerical simulations. COde for a Speedy Monte Carlo (MC) Involving Cuda Architecture (COSMICA) is a MC code, solving backward-in-time the system of Stochastic Differential Equations (SDE) equivalent to the Parker Transport Equation (PTE). The Graphics Processing Unit (GPU) parallelization of COSMICA code is a game changer in this field because it reduces the computational time of a standard simulation from the order of hundred of minutes to few of them. Furthermore, the code is capable of distributing the computations on clusters of machines with multiple GPUs, opening the way for scaling. In COSMICA we implemented the synchronous broadcasting of memory access for evolving variable samples, the rounding of virtual particle set numbers, to fulfil the GPU blocks, and the exploitation of shared memory to free registers. Furthermore, we compactify the mathematical computations and pass to the lighter momentum formulation of SDE. The first porting of the code on GPU architecture brings it to a speed-up of 40 X. The successful optimizations bring 1.5X speed-up.

关键词： Technological innovation Codes Systematics Computational modeling Graphics processing units Stochastic processes Computer architecture Optimization Cosmic rays Standards

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Optimizing Large Scale Ontology Alignment to Establish Interoperability for Efficient Retrieval

Optimizing Large Scale Ontology Alignment to Establish Inter...

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Disruptive Technologies (ICDT), International conference on

作者： Usha Yadav Gyanendra Kumar National Institute of Fashion Technology Bengaluru Karnataka Manipal University Jaipur Jaipur Rajasthan

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The Semantic Web is aiming to add meaning and structure to the vast amount of data available online. It aims to make information machine-readable and interpret, unlike traditional web pages that are designed for human consumption. The increase in adoption of semantic web technologies resulted in the generation of vast number of ontologies with varying size and heterogeneity. The efficient ontology matching system to align the large number of ontologies is extremely required to allow interoperability in cross domain. Although there are various ontology matching systems available which could handle matching small ontologies but when it comes to matching large scale ontologies, high computational and space requirements is always a challenge. In this paper, an ontologies matching system is proposed which partition the large-scale healthcare ontologies in parallel using base level ontology partitioning method and the similarity between the entities are computed using Adjustment similarity computation method. However, the alignment discovery process demands high computational requirement, therefore in this proposed system, the distributed and parallel approach of Map Reduce technique is used to make system more scalable and efficient.

关键词： Semantic Web Accuracy Scalability memory management Web pages Medical services Ontologies distributed computing Disruptive technologies Interoperability

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and Parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

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20th IFIP WG 10.3 International conference on Network and Parallel computing, NPC 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. The project is open-sourced at https://***/gmfff12334/DaCP. © IFIP International Federation for Information Processing 2025.

关键词： Graphics processing unit

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11th Latin American High Performance computing conference, CARLA 2024

11th Latin American High Performance Computing Conference, C...

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11th Latin American High Performance computing conference, CARLA 2024

ISBN: (纸本)9783031800832

The proceedings contain 21 papers. The special focus in this conference is on Latin American High Performance computing. The topics include: High Performance computing for Auto Supervised Machine Learning Training: Parallel-distributed Implementation of the Word2Vec Algorithm for Training Word Embeddings;A Comprehensive Analysis of Process Energy Consumption on Multi-socket Systems with GPUs;Enhancing Reverse Time Migration Simulations in HPC Systems Through I/O and Computation Overlapping;Evaluation of Computational and Power Performance in Matrix Multiplication Libraries - MKL Vs cuBLAS;a User-Centric Evaluation Methodology for Informed Provisioning of High Performance computing Resources in Academic Institutions;efiMon: A Process Analyser for Granular Power Consumption Prediction;Leveraging CPU-FPGA Co-design for Matrix Profile Computation;Web System for Recognizing Actions of Physical Violence in Urban Spaces Using CNN with Transfer Learning;Quantized SG-MCMC for Bayesian Deep Posterior Compression;no Plankton Left Behind: Preliminary Results on Massive Plankton Image Recognition;Machine Learning Regression-Based Prediction for Improving Performance and Energy Consumption in HPC Platforms;a Computational Framework for Crop Yield Estimation and Phenological Monitoring;Histopathology Image Augmentation Through StyleGAN2-ADA;strategies to Reduce memory Consumption in Software Quantum computing Simulators;Adaptive Edge-Based AIoT Architecture for Efficient Retraining and Sustainable Monitoring of Ephemeral Streams;Multi-GPU Tomographic Reconstructions of Large Volumes in the Frequency Domain;Accelerating Tomographic Artifact Removal Using a Multi-GPU System;a Parallel Multi-threading Global Energy Balance for a Room Thermal Analysis in an Unsteady State;Parallel computing Strategies in WRF: The Role of MPI, OpenMP, & NUMA Affinity.

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Precise Fault Distance Prediction in distributed Generators-Integrated Transmission Lines Using CNN-LSTM Ensemble Networks

Precise Fault Distance Prediction in Distributed Generators-...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Pavan Kumar Bais Narendra D. Londhe Electrical Engineering Department National Institute of Technology Raipur Raipur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Precise fault distance estimation is critical for effective transmission system protection, especially in modern power systems with integrated distributed generators (DGs). As the global demand for energy continues to rise, the adoption of renewable energy sources and their integration into the grid has significantly increased. However, the presence of various DGs complicates fault distance estimation due to the diverse power flows and interactions between DGs and existing grid infrastructure. The integration of large-scale DGs introduces additional challenges in fault detection and location, as the dynamic behavior of these generators affects the fault characteristics. This paper focuses on precise fault distance estimation within the IEEE-9 bus system, which includes two distinct DGs. The proposed solution leverages an ensemble deep learning approach, combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN-LSTM model takes advantage of CNN’s ability to extract spatial features and LSTM’s strength in capturing temporal dependencies, making it well-suited for fault distance prediction. The model’s performance is evaluated using mean absolute error (MAE), giving 0.6387 average MAE for the studied system. Comparative analysis with existing deep learning models and validated through OPAL-RT 4510 real-time simulator shows that the proposed model delivers superior performance, demonstrating its effectiveness in handling the complexities introduced by DG integration.

关键词： Deep learning Power system dynamics Estimation Predictive models Power system stability Feature extraction Generators Real-time systems Power system reliability Convolutional neural networks

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A Methodical Approach to Parallel IO Analysis in distributed Deep Learning Applications 30th

A Methodical Approach to Parallel IO Analysis in Distribut...

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30th International conference on Parallel and distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

作者： Parraga, Edixon Leon, Betzabeth Mendez, Sandra Rexachs, Dolores Suppi, Remo Luque, Emilio Bellaterra Barcelona Spain Barcelona Spain

ISBN: (纸本)9783031856372

Deep learning applications have become crucially important for the analysis and prediction of massive volumes of data. However, these applications impose substantial input/output (I/O) loads on computing systems. Specifically, when running on distributed memory systems, they manage large amounts of data that must be accessed from parallel file systems during the training stage using the available I/O software stack. These accesses are inherently intensive and highly concurrent, which can saturate systems and adversely impact application performance. Consequently, the challenge lies in efficiently utilizing the I/O system to allow these applications to scale. When the volume of data increases, access can generate high training latency and add overhead significantly when data exceeds the main memory capacity. Therefore, it is essential to analyze the behavior of the I/O patterns generated during the training stage by reading the data set to analyze the behavior when the application scales and what amount of resources it will need. The paper presents a methodology to analyze parallel I/O patterns in Deep Learning applications in this context. Our methodological approach mainly aims at providing users with complete and accurate information. This involves a thorough understanding of how the application, the dataset, and the system parameters can significantly influence the parallel I/O of their deep learning application. We seek to empower users to make informed decisions through a structured methodology that allows them to identify and modify configurable elements effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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