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Proceedings of the iasted international conference on parallel and distributed computing and Systems, PDCS 2010

Proceedings of the IASTED International Conference on Parall...

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iasted international conference on parallel and distributed computing and Systems, PDCS 2010

ISBN: (纸本)9780889868786

The proceedings contain 40 papers. The topics discussed include: selection of switching sites in all-optical network topology design;design of an all-optical WDM lightpath concentrator;a 3D solid texture feature computation method using multi-core CPU architectures;feedback-control based real-time divisible load scheduling;efficient implementation of large-scale workflows based on array contraction;speed-up of SAR image formation processing using graphics processing units;node-to-set disjoint paths routing in metacube;an agent execution control method for content-based information retrieval with time constraints;a software implementation of a cycle precision simulator of a multiple associative model;detecting game bots with encrypted game traffic;a framework for developing parallel optimization algorithms;and a proposal of thread virtualization environment for cell broadband engine.

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Execution Time Optimization for Pipeline Deep Network Training on Multiple GPUs

Execution Time Optimization for Pipeline Deep Network Traini...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Bing-Jou Wu Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput.

关键词： Training Directed acyclic graph Computational modeling Neural networks Pipelines Computer architecture parallel processing Throughput Dynamic programming Topology

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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

30th International Conference on Parallel and Distributed Pr...

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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

ISBN: (纸本)9783031856372

The proceedings contain 24 papers. The special focus in this conference is on parallel and distributed Processing Techniques. The topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication Algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional Neural networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted

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Attack Graph Generation on HPC Clusters 30th

Attack Graph Generation on HPC Clusters

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作者： Li, Ming Hale, John The University of Tulsa TulsaOK74104 United States

ISBN: (纸本)9783031856372

Attack graphs (AGs) are graphical tools to analyze the security of computer networks. By connecting the exploitation of individual vulnerabilities, AGs expose possible multi-step attacks against target networks, allowing system administrators to take preventive measures to enhance their network’s security. As powerful analytical tools, however, AGs are both time- and memory-consuming to be generated. As the numbers of network assets, interconnections between devices, as well as vulnerabilities increase, the size and volume of the resulting AGs grow at a much higher rate, leading to the well-known state-space explosion. In this paper, we propose the use of high performance computing (HPC) clusters to implement AG generators. We evaluate the performance through experiments and provide insights into how cluster environments can help resolve the issues of slow speed and high memory demands in AG generation in a balanced way. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attack Graph Cybersecurity High Performance computing

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

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international Communication Systems and networks and Workshops, COMSNETS

作者： Saumya Mathkar Shreya Aiyer Yashovardhan Bapat Pinki Pinki Arnab K. Paul Vinayak Naik Department of CSIS BITS Pilani Goa India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size.

关键词： Performance evaluation Computer aided instruction Machine learning algorithms Processor scheduling Distance learning Soft sensors Machine learning Integer linear programming Resource management Edge computing

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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On Developing Smart IoT-Based distributed System for Remote Detection of Slope Movements

On Developing Smart IoT-Based Distributed System for Remote ...

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international conference on computing, Networking and Communications (ICNC)

作者： Rahul Debnath Ahmed Abdelmoamen Ahmed Md Jobair Bin Alam Department of Computer Science Prairie View A&M University Prairie View TX USA Department of Civil Engineering Prairie View A&M University Prairie View TX USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Landslides or slope failures cause significant damage to the transportation infrastructure worldwide, resulting in billions of dollars of property damage. The initial slope movement of highway slopes, caused by environmental factors and man-made activities, must be detected early to prevent significant losses in infrastructure damages and human lives. However, traditional monitoring approaches for slope movement, such as periodic human inspection and surveying, are labor-intensive and time-consuming. With the advent of the Internet of Things (IoT) and high-speed internet, the potential to use such technologies for monitoring remote earth movement of highway embankments is becoming increasingly important. This paper presents an IoT-based distributed system for the early detection of slope movements to assist transportation authorities in immediately repairing the slope before a major failure occurs. We built a laboratory prototype embankment, equipped with various IoT sensors (e.g., tilt, moisture, and tensiometer sensors), that simulate a failure-prone slope to evaluate the effectiveness of our system. A user-friendly GUI is developed to enable users to utilize the system remotely. The collected sensory data for four months allowed us to analyze the soil hydrologic dynamics and the slope performance by detecting changes in the slope tilting both parallel (pitch) and perpendicular (roll). The experimental results showed that our system could detect negligible movement (1mm) and rotation (0.02°) of slopes.

关键词： Road transportation Working environment noise Transportation Artificial neural networks Sensor systems Vectors Internet of Things Remote monitoring Intelligent sensors Graphical user interfaces

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HPC and GPU solutions for radio interferometry using RICK

HPC and GPU solutions for radio interferometry using RICK

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Emanuele De Rubeis Claudio Gheller Giovanni Lacopo Luca Tornatore Pascal Jahan Elahi Maciej Cytowski Giuliano Taffoni Ugo Varetto Dipartimento di Fisica e Astronomia Università di Bologna Istituto di Radioastronomia INAF via Gobetti 93/2 Bologna Italy Istituto di Radioastronomia INAF Via P. Gobetti 101 Bologna Italy INAF Astronomical Observatory of Trieste Università degli studi di Trieste Trieste Italy INAF Astronomical Observatory of Trieste via GB Tiepolo 11 Trieste Italy Pawsey Supercomputing Centre 1 Bryce Avenue Kensington WA Australia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Since the last decade, radio astronomy has started a new era: the advent of the Square Kilometer Array (SKA), preceded by its pathfinders, will produce a huge amount of data that will be hard to process with a traditional approach. This means that the current state-of-the-art software for data reduction and imaging will have to be re-modeled to face such data challenge. In order to manage such an increase in data size and computational requirements, scientists need to exploit modern high-performance computing (HPC) architectures. In particular, heterogeneous systems, based on complex combinations of CPUs, accelerators, high-speed networks and composite storage devices need to be used in an efficient and effective way. In this paper, we present an overview on Radio Imaging Code Kernels (RICK; [1]; [2]; [3]), a code able to perform the most computationally demanding steps of w-stacking gridder algorithm exploiting distributed parallelism and GPU acceleration. GPU offloading is possible through CUDA, HIP, and OpenMP, aiming at the largest possible usability among multiple architectures. After detailing the (multi-)GPU approach to the problem and listing all the new code implementations, we analyze its performances considering both the computational and communication workload. We will show how the full, distributed GPU offload of the code, first of its kind and crucial to deal with increasingly large interferometric data, represents not only an extremely fast and optimized approach, but also the greenest one if compared to its parallel CPU counterpart. This code, now publicly available, has been tested with a wide variety of modern interferometers and SKA pathfinders. This represents, to date, the first example of radio imaging software fully enabled to GPUs, becoming a potential state-of-the-art approach for the upcoming SKA. Finally, we will also present the future perspectives about the code, planned to be converted into a library and possibly be used by any of the m

关键词： Performance evaluation Codes Radio astronomy High performance computing Graphics processing units Imaging Radio interferometry Computer architecture Software Usability

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The 18th iasted international conference on parallel and distributed computing and systems, PDCS 08: Chairman's foreword

IASTED International Symposium on Distributed Sensor Network...

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iasted international Symposium on distributed Sensor networks, DSN 2008 2008年

作者： Gonzalez, Teofilo F.

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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