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Proceedings of the 20th iasted international conference on parallel and distributed computing and Systems, PDCS 2008

Proceedings of the 20th IASTED International Conference on P...

引用

20th iasted international conference on parallel and distributed computing and Systems, PDCS 2008

ISBN: (纸本)9780889867741

The proceedings contain 57 papers. The topics discussed include: logical topology design with traffic grooming for scheduled demands;the impact of multi-core architectures on task retrieval policies for volunteer computing;evaluation of CPU power control and scheduling technique on parallel computing environment;clustered assignment of CCQS in information monitoring system supporting conjunctive queries;dense skip graphs as an overlay for P2P networks;multi-connection and multi-core aware all-gather on infiniband clusters;an improved prioritization method for task scheduling in heterogeneous computing systems;thread pipelining with clustered data communication on commodity CMPS;a distributed algorithm for disjoint paths in star networks;parallel algorithms for a visual text mining platform;and a cache miss analysis for multithreaded architectures.

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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP international Federation for Information Processing 2025.

关键词： Data aggregation

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RACI: A Resource-Aware Cooperative Inference Framework on Heterogeneous Edge Devices 24

RACI: A Resource-Aware Cooperative Inference Framework on He...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Wang, Zhenyu Ren, Ao Liu, Duo Fang, Haining Shi, Jiaxing Tan, Yujuan Chen, Xianzhang Chongqing University China

ISBN: (纸本)9798400710773

Cooperative inference for deep neural networks (DNNs) across edge devices has received increasing attention, due to the benefits of low latency, low power consumption, and privacy preservation. Cooperative inference partitions a DNN model into multiple segments, which will then be allocated to distributed devices for parallel inference. Nonetheless, prior works fail to comprehensively study the impact of layer configurations, dynamic network bandwidths, and heterogeneous device capabilities on the inference speed, resulting in suboptimal inference performance. In this work, we conduct a comprehensive analysis of these key factors and figure out the limitations of conventional transfer-based and redundant computation-based methods. Based on the analysis, we first propose a latency prediction agent that accounts for the layer configurations, network bandwidths, and device computing capabilities, aiming to quickly evaluate the inference latency. Furthermore, we propose RACI, a resource-aware cooperative DNNs inference framework on heterogeneous edge devices. It co-trains a model agent for model partition and a workload agent for workload allocation to generate co-optimized model partition and workload allocation strategies, leading to high cooperation inference acceleration. Experimental results demonstrate that RACI outperforms the state-of-the-art approaches by 1.1 × -5.2× in terms of inference speedup for three representative DNN models. © 2024 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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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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Proceedings of the 18th iasted international conference on parallel and distributed computing and Systems, PDCS 2006

Proceedings of the 18th IASTED International Conference on P...

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18th iasted international conference on parallel and distributed computing and Systems, PDCS 2006

The proceedings contain 110 papers. The topics discussed include: an evaluation of parallel algorithms on current memory consistency models;vertex-disjoint paths in transposition graphs;implementation of surface-to-surface contact algorithms using iterative solvers on parallel computers;wavelength assignment for broadcasting in sparse conversion tree networks with fewest converters;a two-phase scheduling approach for grid computing;parallelization of multimedia applications on the multi-level computing architecture;co-operation of processes in parallel simulated annealing;model-based design of the communication system in an integrated architecture;improved asynchronous group mutual exclusion in token-passing networks;global static-priority scheduling of sporadic task systems on multiprocessor platforms;approximating the buffer allocation problem using epochs;and entropy properties in program behaviors and branch predictors.

关键词： parallel processing systems

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Problem Specific Communications in distributed Quantum computing

Problem Specific Communications in Distributed Quantum Compu...

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Quantum Communications, Networking, and computing (QCNC), international conference on

作者： Joyanta Basak Bing Wang Sanguthevar Rajasekaran School of Computing University of Connecticut USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributed computing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.

关键词： Quantum computing Protocols Source coding Qubit Search problems Complexity theory Quantum networks Quantum circuit parallel algorithms Sorting

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Proceedings of the 21st iasted international conference on parallel and distributed computing and Systems, PDCS 2009

Proceedings of the 21st IASTED International Conference on P...

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21st iasted international conference on parallel and distributed computing and Systems, PDCS 2009

ISBN: (纸本)9780889868113

The proceedings contain 52 papers. The topics discussed include: performance and reliability evaluations on stochastic activity networks;multi-path position-based routing in mobile ad-hoc networks;PARA-SNOR : a multi-thread snort on multi-core ia platform;on the surface area of the alternating group networks;a programming model for high-performance adaptive applications on pervasive mobile grids;dynamic P2P topology management for scalable H.264 multiple-description coded video streaming;a new protocol to optimize the degree of concurrency in object-oriented databases;highly parallel multi-dimensional fast fourier transform on fine- and coarse-grained many-core approaches;cache-based bounds checking for multi-threaded c programs;design and evaluation of a user-oriented availability benchmark for distributed file systems;and partitioning strategies: spatiotemporal patterns of program decomposition.

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Multi-scale TFT-Net Time-Frequency Representation for Multi-component Radar Signal Recognition 25th

Multi-scale TFT-Net Time-Frequency Representation for Multi...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Tang, Zeyu Shen, Hong Lam, Chan-Tong Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (纸本)9789819642069

Existing research on multi-component radar signal recognition widely adopts recognition frameworks based on time-frequency transformation (TFT) and convolutional neural networks (CNN). To address the issue of the vulnerability of traditional TFT-generated time-frequency representations (TFR) to noise under low signal-to-noise ratio (SNR) conditions, we propose a new TFT scheme, called Multi-Scale TFT Network (MTFT-Net). Specifically, MTFT-Net learns diverse and comprehensive basis functions to obtain various TF features of time-domain multi-component radar signals. It then uses subsequent aggregation modules to concentrate and reconstruct the energy of the TF features, ultimately outputting the TFR. Experimental results show that MTFT-Net generates better TFRs with superior noise resistance under low SNR conditions compared to traditional TFT methods. Moreover, MTFT-Net can mimic the styles of various traditional TFTs. Finally, we compare its performance with the advanced TFA-Net to demonstrate the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Proceedings of the iasted international conference on parallel and distributed computing and Systems, PDCS 2012

Proceedings of the IASTED International Conference on Parall...

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iasted international conference on parallel and distributed computing and Systems, PDCS 2012

The proceedings contain 30 papers. The topics discussed include: an efficient out-of-core implementation of Block Cholesky decomposition on a multi-GPU system;a methodology for characterizing applications for cloud computing environments;social welfare maximization oriented admissions control over delay-sensitive media streams;a software architecture for distributed volume rendering on HPC systems;duty-cycle scheduling for intruder detection in wireless sensor networks;many-core acceleration of vertical plane radio wave propagation prediction;an autonomic detection and protection system for denial of service attack;supporting dynamic data structures in a shared-memory based GPGPU programming framework;improving the performance of document classification by using GPU parallelism;folded Torus based power aware interconnection topology for high-performance multicore architecture;and a power-aware versatile victim cache to reduce average memory latency in parallel architectures.

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Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles 18th

Recommendation-Aware Collaborative Edge Caching Strategy in...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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