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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34218 条记录，以下是141-150 订阅

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection Graph structure learning Multivariate time series Window size selection

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Optimizing Transitive Closure Computation for High Performance computing and Security

Optimizing Transitive Closure Computation for High Performan...

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Elio Masciari Enea Vincenzo Napolitano DIETI University of Naples Federico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.

关键词： Heuristic algorithms High performance computing Scalability Memory management Data structures Real-time systems Computational efficiency Security Sparse matrices Optimization

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SPARE: Self-adaptive Platform for Allocating Resources in Emergencies for Urgent Edge computing

SPARE: Self-adaptive Platform for Allocating Resources in Em...

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Valerio Besozzi Marco Danelutto Patrizio Dazzi Emanuele Carlini Matteo Mordacchini Department of Computer Science University of Pisa Pisa Italy National Research Council Pisa Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.

关键词： Performance evaluation Serverless computing Computer architecture Time factors Virtualization Edge computing Resilience

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A Multi-Level parallel Algorithm for Detection of Single Scatterers in SAR Tomography

A Multi-Level Parallel Algorithm for Detection of Single Sca...

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Massimiliano Russo Mehwish Nisar Antonio Pauciullo Pasquale Imperatore Marco Lapegna Diego Romano Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Institute for the Electromagnetic Sensing of the Environment (IREA) National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

关键词： High performance computing Scalability Graphics processing units Tomography Performance analysis parallel algorithms Synthetic aperture radar Surface treatment Remote sensing Optimization

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Adaptive Protection Scheme Using Optimal Coordination of Directional Overcurrent Relays in grid-Connected Mode by Applying Micro-Genetic Algorithm

Adaptive Protection Scheme Using Optimal Coordination of Dir...

引用

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

作者： Sukrit Das Shubhrata Gupta Department of Electrical Engineering National Institute of Technology Raipur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The integration of distributed Generation (DG) in grid-connected systems presents challenges in maintaining reliable protection schemes, particularly in coordinating Directional Overcurrent Relays (DOCR). This paper proposes an Adaptive Protection scheme that optimizes the coordination of DOCRs using a Micro-genetic Algorithm ($\mu \mathbf{G A}$). The proposed method enhances the resilience and adaptability of protection mechanisms against variable fault conditions and DG contributions. Through simulation studies, the performance of the $\mu$ GA-based approach is evaluated against traditional coordination methods, demonstrating improved fault detection and minimized relay operating times. Results indicate significant reductions in coordination time intervals, ensuring reliable operation of protection systems in the presence of DG. This research offers a framework for enhancing the operational reliability of power systems while accommodating the increasing prevalence of distributed energy resources.

关键词： Adaptive systems Heuristic algorithms Power system dynamics Real-time systems Power system reliability distributed power generation Protection Relays Optimization Genetic algorithms

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HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs 51st

HyperBlocker: Accelerating Rule-based Blocking in Entity Res...

引用

51st international conference on Very Large Data Bases, VLDB 2025

作者： Zhu, Xiaoke Xie, Min Deng, Ting Zhang, Qi Beihang University China Shenzhen Institute of Computing Sciences China Meta Platforms United States

This paper studies rule-based blocking in Entity Resolution (ER). We propose Hyper Blocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, Hyper Blocker employs a pipelined architecture to overlap data transfer and GPU operations. It generates a data-aware and rule-aware execution plan on CPUs, for specifying how rules are evaluated, and develops a number of hardware-aware optimizations to achieve massive parallelism on GPUs. Using real-life datasets, we show that Hyper Blocker is at least 6.8× and 9.1× faster than prior CPU-powered distributed systems and GPU-based ER solvers, respectively. Better still, by combining Hyper Blocker with the state-of-the-art ER matcher, we can speed up the overall ER process by at least 30% with comparable accuracy. © 2025, VLDB Endowment. All rights reserved.

关键词： Data transfer

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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...

引用

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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Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT Systems 24th

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Dee...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Tang, Fengjie Xu, Jia Liu, Xiao Yao, Aiting Fang, Mingyan Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9789819615414

Artificial Intelligence of Things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have different task priorities and heterogeneous resource requirements. However, these factors will affect the QoS of the solution and the task response time. Therefore, motivated by a typical smart warehouse system, a Multi-access Edge computing (MEC) based distributed task offloading framework is proposed. Afterward, a delay-sensitive mixed-integer programming model is defined to address the imbalanced utilization of computing resources between edge and cloud servers during the offloading process. Finally, based on the attention mechanism, a Priority-Driven Multi-Agent Deep Reinforcement Learning algorithm (PDMA-DRL) is proposed to fulfill real-time resource requirements while reducing the task response time. The PDMA-DRL algorithm consists of two agents, and each agent corresponds to a sub-problem. Comprehensive experimental results demonstrate that our proposed PDMA-DRL algorithm can outperform representative existing methods in generating delay-sensitive task offloading plans and effectively reducing the task response time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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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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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yizhou Wang, Yijie Xu, Hongzuo Zhou, Xiaohui National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100091 China

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： Local Temporal Pattern Self-supervised Learning Time Series Anomaly Detection

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Research on the Design and Implementation of Financial ERP System Based on grid Algorithm 25

Research on the Design and Implementation of Financial ERP S...

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Proceedings of the 2025 4th international conference on Big Data, Information and Computer Network

作者： Xinyue Zhang Zhe Yang Shuai Zhang Beiing China-Power Information Technology co. LTD Beijing China

ISBN: (纸本)9798400712425

Finance informatization is an important method that helps managers in companies to enhance the decision-making quality along with their efficiency. The finance ERP system enriched with the help of the grid algorithms facilitates data analytics and the following of funds, with the assistance of such computational resources, the status of which is that the deployment of computational resources is done effectively and the task scheduling model is efficient. During looking at these grid algorithms on a more detailed level, these are results that were obtained when classification methods such as dynamic scheduling algorithms and load balancing algorithms were performed, the ones of which can particularly be used for parallel and distributed computing along with a multitude of services or enhancing the financial system performance. An experiment's design included explicit environmental setups that were outlined and a myriad of test datasets, which facilitated the assessment of comparative efficiency. The response time and processing efficiency the ERP system achieves using grid algorithms are the main causes of enhanced system performance metrics (far beyond the traditional thresholds). Besides the smoothness and cost-effectiveness of the system, the new system will result in an economic advantage in the market for a design concept that also will provide enterprises with the outline of and help in the implementation of the system that will solve a variety of financial issues they face. The study result indicates refreshed views and suggestions for the future economic management, the result of which will be efficient financial decisions and better use of the resources.

关键词： grid algorithm

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