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共 543 条记录，以下是41-50 订阅

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JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection 20th

JediGAN: A Fully Decentralized Training of GAN with Adapti...

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Han, Xiaoming Liu, Boan Cheng, Dazhao Department of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (纸本)9789819628292

Existing decentralized learning of Generative Adversarial network (GAN) suffers from a slower convergence rate and training instability due to the changes in the gradient-sharing approach among workers. It requires more iterations to achieve convergence and deteriorates the GAN training, consequently leading to slow down training speed and accuracy degradation. We propose JediGAN, the novel distributed GAN system that achieves optimal benefits by balancing accuracy and communication overheads through adaptive scheduling strategies. JediGAN reduces overall consensus variance by incorporating adaptive global updates for the discriminators. Additionally, JediGAN effectively leverages the internal characteristics of the GAN to decouple the training processes of the discriminators and generators during the search. It employs the concept of evolutionary selection to update the generators, thereby improving training stability. Through extensive experiments, we show that JediGAN outperforms state-of-the-art decentralized GAN systems, with 13.1% accuracy and 36.6% training speed improvement. © ifip international Federation for Information Processing 2025.

关键词： Discriminators

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Optimizing Vo-Viso: A Modified Methodology to parallel computing with Isolating Data in Memristor Arrays 20th

Optimizing Vo-Viso: A Modified Methodology to Parallel Comp...

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Wang, Wenqing Chen, Yabo Xu, Nuo Hu, Yihong Huang, Chenglong Wei, Shaojun Fang, Liang National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9789819628292

The MAGIC NOR gate-based memristor array is a promising processing-in-memory (PIM) approach due to its massive parallelism, flexible logical operation, and non-volatile memory characteristics. The Vo-Viso MAGIC NOR gate operation mode is proposed to achieve high-throughput parallel computing and isolate other data within a memristor array. However, Viso has a cumulative effect on the node voltage of the active row, calling into question the feasibility of Vo-Viso operation mode. In this paper, circuit analysis defines the necessary circuit parameter conditions for enabling parallel computing and isolation units in the MAGIC NOR gate array. By algorithms in Python and CUDA C++ exploration, we determined that achieving the necessary conditions within the array parameters ranges—including the tolerance margin VΔ, array size, Vo, Viso and memristor parameters suitable for real-world circuit requirements—is unfeasible. In response to these challenges, we modified the memristor array architecture. The modified architecture employs transistors to activate compute units while safeguarding the rest selectively. The feasibility of this architecture is validated by SPICE with selected transistors fabricated using a 65 nm process, taking into account transistor resistance and the fluctuations in Vset and Vreset in the 8×8 modified memristor array. To accommodate the design requirement of the state-of-the-art memristor array, the feasibility of 64×64 modified memristor array, taking into account the impact of wire resistance, is further verified. This paper bridges the gap between reliable high-throughput parallel computing operation and MAGIC NOR PIM architecture. © ifip international Federation for Information Processing 2025.

关键词： SPICE

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FedDeSnowNet: Federated De-snowing network for LiDAR Point Clouds 20th

FedDeSnowNet: Federated De-snowing Network for LiDAR Point ...

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Fang, Zhiyu Li, Boyang Liao, Jiahui Ren, Siheng Huang, Kai School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9789819628636

Although Light Detection and Ranging (Lidar) is widely utilized in the intelligent transportation systems, the susceptibility to data degradation in snowy condition limits the application. Since rule-based methods cannot meet the requirements of real-time inference while central learning-based methods has the limitations of training data collection and privacy, this paper proposes the FedDeSnowNet, a federated de-snowing method for Lidar point clouds. In order to solve the problems arising from the feature shift as well as the client shift caused by the data heterogeneity and the communication constraints in parameter aggregation process, the FedDeSnowNet incorporates a distribution-guided aggregation mechanism as well as an ingeniously designed personalized module, both designed to enhance the training effects. FedDeSnowNet outperforms baseline methods by 14% in F1 score in experiments. © ifip international Federation for Information Processing 2025.

关键词： Federated learning

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RFaaS: Function Scheduling Across Heterogeneous Clusters 20th

RFaaS: Function Scheduling Across Heterogeneous Clusters

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Tang, Zhihang Li, Yiming Mao, Zezheng Zhao, Laiping Li, Keqiu College of Intelligence and Computing Tianjin University Tianjin300072 China Intelligent Computing Infrastructure Innovation Center Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9789819628292

The rapid development of cloud computing has attracted a diverse migration of applications. Serverless computing, with its abstract resource management, on-demand billing, and dynamic scaling, has become a popular cloud computing paradigm. X86 signifies traditional computility, while RISC-V symbolizes new potential. Making the most of existing traditional computing resources while exploring the potential of new computility will be a key challenge. The x86 and RISC-V hybrid computility supply will continue for a long time. Task management under diverse instruction set architectures is a critical issue that needs addressing in this evolving landscape. Current research predominantly concentrates on homogeneous instruction set clusters. In this paper, we propose RFaaS, a function job scheduling methodology tailored for RISC-V + X86 heterogeneous instruction set clusters, leveraging the OpenFaaS serverless computing platform. We delve into the affinity traits of function jobs in RISC-V + X86 amalgamated instruction set clusters and devise an affinity classifier alongside an architecture-aware scheduling algorithm. Our methodology dissects scheduling decisions into resource fulfillment and affinity alignment, underpinned by a meticulously crafted update algorithm to uphold and refine job affinities. Experimental results show that RFaaS can provide at least 3x performance improvement and 2.4x throughput increase compared to existing solutions. © ifip international Federation for Information Processing 2025.

关键词： Scheduling algorithms

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Vickrey Auction Offloading for Edge-Assisted Video Analytics with Dynamic Gain Prediction 20th

Vickrey Auction Offloading for Edge-Assisted Video Analytic...

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Du, Mei Tan, Xiaobin Pan, Yaying Wang, Mingyang Wang, Shunyi Zheng, Quan University of Science and Technology of China Hefei China Institute of Artificial Intelligence HCNSC Hefei China

ISBN: (纸本)9789819628636

Mobile Edge computing (MEC) with offloading from local light models to edge heavy models offers a promising approach to meeting the high processing quality and low latency demands of video analytics while reducing overall system resource consumption. However, significant challenges remain in predicting offloading gains and ensuring effective multi-device collaboration under dynamic video content and network conditions, as existing methods struggle with accuracy prediction, timeliness, and resource allocation flexibility. In this paper, we present a Tile Confidence Gains-inspired Vickrey Auction-based Offloading System (TCGVA), designed to optimize latency-decayed offloading accuracy gains under network and computation constraints. To dynamically and efficiently predict offloading improvements, the system leverages historical gains thresholds feedback from edge batch processing, incorporating recent offloading intervals. A distributed Vickrey auction-based strategy is proposed to optimize resource allocation among multiple devices, enabling quick adaptation to content and network fluctuations. Our implemented system, tested on the VisDrone dataset with HSDPA network traces, achieves 93.28% of the ideal accuracy gain while maintaining prediction speeds close to the greedy method, with 62.36% less bandwidth occupancy and 16.49% fewer latency violations under fluctuating conditions. © ifip international Federation for Information Processing 2025.

关键词： Mobile edge computing

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parallel Construction of Independent Spanning Trees on 3-ary n-cube networks 20th

Parallel Construction of Independent Spanning Trees on 3-a...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Xu, Yuzhen Fan, Weibei Lv, Mengjie Sun, Xueli Han, Lei Xiao, Fu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9789819628292

As networks grow larger and more intricate, failures become unavoidable. Interconnection networks need continual and reliable functioning, as well as effective routing methods for transmitting data among processors. Ensuring fault tolerance in routing, especially through independent spanning trees (ISTs), is crucial. The 3-ary n-cube network (Qn3) boasts numerous properties such as low vertex degree, regularity, and straightforward implementation. In this paper, We have presented an effective parallel constructive approach for ISTs on Qn3 relying on the ascending sequence. Derived from the ISTs constructed before, we obtain a fault-tolerant routing system that utilizes those as its routing table. Following this, we evaluate the effectiveness of the fault-tolerant routing mechanism through simulated data. Simulated data reveals a progressive escalation in transmission success rates as dimensionality increases, approaching near-perfection at almost 100%. Its findings can provide a valuable benchmark and offer insights into the efficacy of fault-tolerant communication within a broad spectrum of cube-based systems. © ifip international Federation for Information Processing 2025.

关键词： Trees (mathematics)

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EAMTI: A Novel Method Toward Early and Accurate Malicious Traffic Identification 20th

EAMTI: A Novel Method Toward Early and Accurate Malicious Tr...

引用

20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Shen, Yuyang Tao, Jun Yu, Linxiao Luo, Yuantu School of Cyber Science and Engineering Southeast University Nanjing210096 China Purple Mountain Laboratories for Network and Communication Security Nanjing211111 China

ISBN: (纸本)9789819628636

Malicious traffic constantly threatens the security and stability of networks. However, most existing identification methods based on traditional machine learning and deep learning leverage statistical features of an entire flow, which delays the identification. Although a few studies have started to focus on early identification, they do not make full use of flow information, resulting in issues with performance or timeliness. In this paper, we propose EAMTI, a novel method toward Early and Accurate Malicious Traffic Identification. We construct feature sequences based on byte frequency vectorization and directional information of bidirectional flows to represent network flows. Our representation method preserves rich packet-level information and flow-level sequential characteristics. A bidirectional Gated Recurrent Unit (GRU) model is constructed to process feature sequence samples and perform accurate identification. Our experimental results on the CICIDS2017 dataset show that EAMTI can construct representative feature sequences using only 6 initial packets, and it outperforms the existing methods in terms of accuracy, recall, precision, and F1-score. © ifip international Federation for Information Processing 2025.

关键词： network security

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CMS: A Computility Resource Status Management and Storage Framework 20th

CMS: A Computility Resource Status Management and Storage F...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Peng, Xiaohui Luo, Xudong Chang, Kuo Wang, Yifan Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Nanjing Institute of InforSuperBahn Nanjing China

ISBN: (纸本)9789819628292

With the increasing demand for computility, managing distributed computility resources is crucial for improving service quality and performance of the cross-region computility infrastructure. In order to ensure timely and accurate scheduling of computing tasks on such infrastructure, a distributed computility status storage and query system is needed to achieve millisecond-level performance in terms of multidimensional sort query and multidimensional range query for computility status. Thus, we propose CMS, a computility resource status management and storage framework, which leverages specialized multidimensional indexes to support millisecond-level resource status reporting and querying, as well as sort and range queries. In addition, we developed BCQL and a set of querying algorithms to enhance throughput and reduce query latency. Experimental results show that the average query latency of the multidimensional sorting queries and range queries of the CMS system is 2.499 ms and 2.014 ms, respectively. While, the latency of those queries in Redis-based framework reach 25.66 ms and 25.13 ms, which are 10× higher than the CMS. © ifip international Federation for Information Processing 2025.

关键词： Structured Query Language

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SpecInF: Exploiting Idle GPU Resources in Distributed DL Training via Speculative Inference Filling 20th

SpecInF: Exploiting Idle GPU Resources in Distributed DL Tr...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Lv, Cunchi Shi, Xiao Liang, Dong Tan, Wenting Zhao, Xiaofang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Zhongguancun Laboratory Haidian China Nanjing Institute of InforSuperbahn Nanjing China

ISBN: (纸本)9789819628292

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to exploit idle GPU resources. It collocates each primary training instance with additional inference instances on the same GPU, detects the training bubbles and adaptively fills with online or offline inference workloads. Our results show that SpecInF can effectively enhance GPU utilization under mainstream parallel training modes, delivering additional up to 14× offline inference throughputs than TGS and 67% reduction in online inference p95 latency than MPS, while guaranteeing collocated training throughput. © ifip international Federation for Information Processing 2025.

关键词： Resource allocation

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Task Offloading Optimization in Multi-layer LEO Satellite-Terrestrial Integrated networks with Hybrid Cloud and Edge computing 20th

Task Offloading Optimization in Multi-layer LEO Satellite-Te...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Luo, Juan Yin, Weiyu Qiao, Ying Teng, Shuyang Hunan University Changsha China

ISBN: (纸本)9789819628636

Low Earth Orbit (LEO) satellite networks have gained significant attention due to their extensive coverage and low latency. Unlike terrestrial computing systems, satellite networks are extremely complex, and conventional methods find it challenging to adapt to such a dynamic and intricate environment. Meanwhile as the demand for real-time applications grows, current satellite architectures struggle with efficiently managing the increased need for on-orbit processing. This paper addresses the challenge by proposing an innovative multi-layer LEO satellite edge computing architecture, comprising two tiers of LEO satellites with distinct roles and computational capabilities. Reinforcement learning is leveraged to optimize task offloading, with each satellite acting as an agent capable of consulting a higher-tier agent for improved decision-making in complex scenarios. During implementing our algorithm, we faced a challenge: rewards from task offloading actions are delayed due to extended waiting and execution times. To tackle the issue of delayed feedback, we propose a experience replay buffer algorithm to adapt reinforcement learning. Experimental results demonstrate a 52% reduction in average latency and a 5% decrease in energy consumption per satellite, highlighting the efficiency and practicality of the proposed architecture. © ifip international Federation for Information Processing 2025.

关键词： Computation offloading

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