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

共 4313 条记录，以下是101-110 订阅

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Proceedings - 10th euromicro Workshop on parallel, distributed and network-based processing, euromicro-PDP 2002

Proceedings - 10th Euromicro Workshop on Parallel, Distribut...

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10th euromicro Workshop on parallel, distributed and network-based processing, euromicro-PDP 2002

ISBN: (纸本)0769514448

The proceedings contain 57 papers. The topics discussed include: SICOSYS: an integrated framework for studying interconnection network performance in multiprocessor systems;performance analysis for teraflop computers: a distributed automatic approach;execution time prediction for parallel data processing tasks;model oriented profiling of parallel programs;ambient interfaces in a web-based theatre of work;routing to support communication in dependable networks;performance sensitivity of routing algorithms to failures in networks of workstations with regular and irregular topologies;eventually consistent failure detectors;on the impossibility of implementing perpetual failure detectors in partially synchronous systems;mobile agents in a distributed heterogeneous database system;scalable monitoring and configuration tools for grids and clusters;and improving induction decision trees with parallel genetic programming.

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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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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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Research on Big Data distributed Computing Model and Algorithm Optimization for Complex network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models distributed computing Detection algorithms Optimization

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Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster 20th

Efficient Implementation of the LOBPCG Algorithm on a CP...

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20th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2024

作者： Liu, Yang Zhao, Yonghua Wang, Zexin Huang, Rongfeng Zhang, Dingye Zhang, Xinyin Computer Network Information Center Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Technology Shijiazhuang Tiedao University Hebei Shijiazhuang China

ISBN: (纸本)9789819628292

The Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) algorithm is an effective algorithm for solving large-scale sparse eigenvalue problems in various scientific and engineering applications. based on a distributed CPU-GPU heterogeneous environment, this paper presents an optimized approach for the efficient solution of sparse eigenvalue problems utilizing the LOBPCG algorithm. To conceal intra-node data transfer and reduce the influence of the cross-node MPI communication overhead, we employ a sparse matrix regular two-dimensional partitioning scheme. To enhance GPU data throughput and data reuse, we consolidate multiple sparse matrix-vector multiplications (SPMV) into a single sparse matrix-matrix multiplication (SPMM). Furthermore, for the irregular dense matrix-matrix multiplication (GEMM) involved in the LOBPCG algorithm, we design an adaptive batched GEMM technique for GPU execution. Extensive experimental results demonstrate the computational efficiency and scalability of our approach. Our implementation achieves a significant speedup in solving typical matrices compared to the SLEPc library. The strong scalability performance exceeds 58%. © IFIP International Federation for Information processing 2025.

关键词： Scalability

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A Multi-holder Role and Strange Attractor-based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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LBoDSN: An In-network Load Balancing Mechanism for Lossless Data Center networks based on Direct Switch Notification 20th

LBoDSN: An In-Network Load Balancing Mechanism for Lossless...

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20th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2024

作者： Shi, Qingyu Jiang, Fangxue Huang, Huang Li, Xiaocui Li, Chuang Cao, Wenzhi Liu, Limei School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China

ISBN: (纸本)9789819628292

With the extensive deployment of Remote Direct Memory Access (RDMA) in lossless data center networks (DCNs), enhancing the RDMA load balancing performance for distributed AI training and HPC applications becomes particularly critical. However, existing load balancing schemes either suffer difficulty in responding to congestion in sub-RTT time, or cannot accurately detect and reroute flows that are on the verge of creating path congestion for Priority-based Flow Control (PFC) enabled lossless DCNs. In this paper, we propose LBoDSN, an in-network load balancing mechanism for lossless DCNs based on direct swtich notification, to address above challenges. LBoDSN monitors ingress queue length evolution at destination switches to predict the triggering time of PFC pause, and accurately identifies congested flows based on the congestion contribution level before PFC pause, then further proactively sends the flow congestion notification (FCN) to source switches for fast rerouting. The FCN contains a flow ID that identifies the congested flow, and a path ID that identifies the target path to which the congested flow will be rerouted. And after rerouting, the Congestion Notification Packet (CNP) of the old path is selectively discarded at source switches to improve transmission performance, while out-of-order packets are reordered at the destination switch. The experimental results show that under realistic workloads, LBoDSN achieves 9–64% and 20–80% better than CONGA for the average and tail Flow Completion Times (FCTs), respectively. Compared with ConWeave, LBoDSN achieves around 8% better performance for the average and tail FCTs while significantly reducing switch queue usage for reordering. © IFIP International Federation for Information processing 2025.

关键词： Time switches

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QDPformer: Quantum-Driven Workload Prediction Model based on Transformer 20th

QDPformer: Quantum-Driven Workload Prediction Model Based on...

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20th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2024

作者： Cui, Zixuan Huang, Shaoyuan Zhang, Cheng Li, Xudong Wang, Xiaofei Qiu, Chao Niyato, Dusit Tianjin University Tianjin China Tianjin University of Finance and Economics Tianjin China Software Technology Co. Ltd. Suzhou China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Workload prediction is critical to ensuring quality of service in distributed edge cloud platforms (ECP). However, this task faces multiple challenges due to the inherent uncertainties within ECP. On the one hand, ECP aggregates heterogeneous infrastructure resources, whose static attributes are difficult to effectively represent and utilize. On the other hand, the dynamic switching of applications deployed on edge servers leads to diverse workload patterns. These uncertainties make server workload prediction in ECP challenging. Inspired by quantum theory in modeling uncertainty, we make an important attempt to design a Quantum-Driven workload Prediction model based on Transformer (QDPformer). Specifically, we introduce fundamental concepts such as quantum states and density matrices to model various uncertain features in ECP. Additionally, we present an improved quantum attention mechanism based on quantum measurement and quantum evolution that enables QDPformer to simulate dynamic changes in servers, significantly enhancing the model’s predictive performance. Experimental results on real ECP datasets show that QDPformer achieves up to a 24.52% reduction in mean squared error (MSE) compared with the baseline models. © IFIP International Federation for Information processing 2025.

关键词： Quality of service

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Feature Augmented Meta-Learning on Domain Generalization for Evolving Malware Classification 24th

Feature Augmented Meta-Learning on Domain Generalization fo...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Wang, Fangwei Chen, Yinhe Song, Ruixin Li, Qingru Wang, Changguang Key Laboratory of Network and Information Security of Hebei Province College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819615278

The proliferation of malware has resulted in substantial harm to various sectors and economies. Various deep learning-based malware classification methods have been suggested as a means of mitigating malware threats. These methods typically operate under the assumption of independent and identically distributed training and test data. However, this assumption becomes invalid with the evolving malware family. While domain adaptation models offer a potential solution to this issue, their implementation is hindered by the difficulty of collecting new malware variants. In order to address the previously mentioned problem, we suggest an image-based technique for categorizing malware families utilizing domain generalization. Initially, malware is transformed into gray-scale images that depict byte patterns of the malware. Subsequently, these gray-scale images are fed into a model incorporating convolutional block attention to extract features. Furthermore, data augmentation is implemented at the feature level to broaden the distribution of the source domain and enhance the model’s generalization capabilities. Finally, meta-learning is utilized as a training approach to effectively extract domain-invariant representations. A series of experiments are conducted on the BIG2015 and BenchMFC-G1P1P2. The proposed method demonstrates a higher accuracy rate of 88.66% on the BIG2015 and 80.25% on the BenchMFC-G1P1P2, which is better than the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain Generalization Domain Shift Feature Argumentation Malware Classification

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Proceedings of the Twelvth euromicro Workshop on parallel, distributed and network-based processing

Proceedings of the Twelvth Euromicro Workshop on Parallel, D...

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euromicro conference on parallel, distributed and network-based processing

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