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检索条件"任意字段=8th IFIP International Conference on Network and Parallel Computing"

共 1177 条记录，以下是21-30 订阅

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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...

引用

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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RFaaS: Function Scheduling Across Heterogeneous Clusters 20th

RFaaS: Function Scheduling Across Heterogeneous Clusters

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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...

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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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DeepSight: In-network Packet Loss Management for TCP Applications 20th

DeepSight: In-Network Packet Loss Management for TCP Applica...

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

作者： Liu, Jiuyi Wan, Anbang Zhang, Dongzhan Xiamen University Xiamen China

ISBN: (纸本)9789819628636

Packet loss can seriously degrade the performance of TCP flows, especially that of small latency-sensitive flows. Managing packet loss efficiently is critical for maintaining good network performance. However, the loss recovery mechanisms of TCP by nature incur high latency due to (i) long loss detection time, (ii) inaccurate loss notification, and (iii) one-size-fits-all loss reaction. It is desirable and essential to design a fast, accurate, and fine-grained packet loss management mechanism to reduce the latency introduced by packet loss. We present DeepSight, an in-network packet loss management mechanism for TCP applications in cloud networks. DeepSight addresses the problems of TCP by exploring the packet processing capabilities of existing programmable switches. It adopts a switch-end collaboration design which offloads the loss detection and recovery from the end to the switch data plane. We implement DeepSight on both the software simulator and hardware testbed. Extensive experiments show that DeepSight achieves faster loss detection time and smaller latency when managing packet loss compared with TCP’s loss recovery mechanisms. © ifip international Federation for Information Processing 2025.

关键词： Packet loss

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An Efficient Method for Evaluating the Two-Terminal Reliability with a parallel Algorithm on the Multi-core Processor Architecture 8th

An Efficient Method for Evaluating the Two-Terminal Reliabil...

引用

8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Chuyen, Nguyen Anh Minh, Le Quang University of Information and Communication Technology—Thainguyen University Thai Nguyen Viet Nam Information Technology Institute Vietnam National University—Hanoi Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819994854

the theory of network reliability has a lot of application in complex network structures, communication networks, cloud computing, traffic networks, and so on. this theory plays a crucial role in evaluating how efficiently networks and depicted as probabilistic graphs. Despite the challenge of network reliability evaluation being NP-hard, there exists a wealth of proposed solutions. However, a predominant number of these solutions have focused on the sequential computing, which is failing to fully leverage the advantages offered by multi-core processor architecture. this paper overcomes this limitation by proposing an efficient strategy that calculates the two-terminal reliability relied on parallel computing. the paper initially delves into a thorough analysis of existing methodologies, followed by the proposal of an efficient technique for computing terminal-pair reliability utilizing Logical-Probabilistic Calculus (LPC). Finally, the paper presents a parallelized iteration of the proposed algorithm designed for a multi-core processor architecture. Results obtained from experimentation confirm the superiority of our proposed algorithm in parallel version compared with other methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

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

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. the integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. the experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © ifip international Federation for Information Processing 2025.

关键词： Scalability

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AnaNET: Anatomical network for Aggregated Time Series Forecasting in Multi-layered Architecture 20th

AnaNET: Anatomical Network for Aggregated Time Series Forec...

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

作者： Zhang, Tiancheng Zhang, Cheng Liu, Shuren Wang, Xiaofei Huang, Shaoyuan Tianjin University Tianjin China Tianjin University of Finance and Economics Tianjin China

ISBN: (纸本)9789819628292

Data centers have become crucial infrastructure, continuously providing services for large-scale distributed computing, computational networks, and large model training. Accurate prediction of future power consumption in data centers is essential for ensuring system efficiency and energy management. However, data centers often employ multi-layered architectures and consist of heterogeneous devices, leading to highly complex and chaotic time-series information, making overall power consumption prediction challenging. To address this issue, this paper analyzes the complexity of aggregated time series from both horizontal and vertical perspectives, tackling the challenges posed by data aggregation in multi-layered architectures. We introduce AnaNET, an innovative analytical network designed specifically to dissect and model the complex and chaotic nature of power consumption. AnaNET utilizes a novel frequency decomposition and inverse progressive decomposition method, isolating and analyzing different periodic patterns within the aggregated data. AnaNET demonstrated approximately 22% relative improvement across six real-world time-series datasets. Code is available at: https://***/hihiztc1/AnaNET_NPC. © ifip international Federation for Information Processing 2025.

关键词： Information management

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RV-CVP: A Flexible Variable Precision RISC-V ISA Extension for Convolutional Neural network 20th

RV-CVP: A Flexible Variable Precision RISC-V ISA Extension f...

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

作者： Zhu, Jingyi Zhao, Di Zhang, Weifeng Jia, Zhijie Liu, Boran Chen, Guokai Alibaba Group Hangzhou China Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Peking University Beijing China

ISBN: (纸本)9789819628292

Convolutional neural network (CNN) performs well in several research fields and achieves high accuracy. With the development of CNNs, various hardware acceleration methods have been proposed. Instruction Set Architecture (ISA) based acceleration methods are gaining attention by providing flexibility and efficiency. Meanwhile, for mobile or embedded environments, there is a preference for variable precision quantization data to meet different accuracy or performance requirements. In this paper, we design a new RISC-V ISA extension, named RV-CVP, which supports variable precision specifically for CNNs. It has 3 computational instructions for CNNs and 3 memory access instructions that closely match the computation. We implement the RV-CVP extension on the open-source processor NutShell. then a CNN programming library is developed, which allows flexible use of the RV-CVP instructions and building CNN models. Finally, we implement our design in simulation and FPGA platforms and evaluate the code density, performance, hardware resource usage, and power consumption. the results show that the RV-CVP extension improves instruction density by up to 1.678x and program performance by up to 42.06x at the single operator level. While on real CNN models, the RV-CVP extension improves performance by 3.8x–4.4x. the code is available at https://***/OpenBPU/RV-CVP. © ifip international Federation for Information Processing 2025.

关键词： Convolutional neural networks

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DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating 20th

DTuner: A Construction-Based Optimization Method for Dynam...

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

作者： Chen, Wenxin Diao, Boyu Liu, Hangda Wang, RuiSheng Xu, Yongjun Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628292

Auto-optimization of tensor programs is a crucial technique in deep learning compilers. Traditional tensor program optimization methods rely on the known fixed shape of the tensor, but when inputs of tensor program are only determined at runtime, these traditional methods cannot optimize in advance. To overcome this challenge, we introduce DTuner, a dynamic shape tensor program optimization method based on a constructive algorithmic. By optimizing for a broad variety of potential input forms, DTuner leverages highly focused and quick optimization methods to increase the efficiency of dynamic shape tensor programs, resulting in notable gains in average performance. Specifically, modern advanced GPUs are composed of general-purpose computing cores and specialized computing cores. Since different input shapes can yield varying performance outcomes across different cores, we incorporate a sampling and prediction method in the optimizer to guide the selection of computing cores. Evaluation results demonstrate that our method is 2 to 3 orders of magnitude faster in tuning time compared to the existing state-of-the-art dynamic tensor program optimization methods (Dietcode). Additionally, the optimization performance of the algorithm is comparable to that of state-of-the-art methods. On the Nvidia A100 and Orin platforms, DTuner can enhance the inference performance of commonly used NLP models by up to 27.5%, with average performance improvements of 6% to 7% for dynamic inputs. For typical single dynamic tensor programs, our method can also achieve a maximum performance improvement of 25.7% and an average improvement of 9%. © ifip international Federation for Information Processing 2025.

关键词： Tensors

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