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23 篇 会议

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708079

The proceedings contain 165 papers. The special focus in this conference is on algorithms and architectures for parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge Computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge Computing;Distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated Computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of Network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge Computing Networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite Network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center Network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708338

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819707973

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708581

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708000

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708611

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on algorithms and architectures for parallel processing, ica3pp 2023

ISBN: (纸本)9789819708109

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Performance Comparison of Distributed DNN Training on Optical Versus Electrical Interconnect Systems 23rd

Performance Comparison of Distributed DNN Training on Optica...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Dai, Fei Chen, Yawen Huang, Zhiyi Zhang, Haibo Tian, Hui Univ Otago Sch Comp Dunedin New Zealand Eastern Inst Technol Pukenga Sch Comp Napier New Zealand Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia

ISBN: (纸本)9789819708338;9789819708345

parallel and distributed Deep Neural Network (DNN) training have become integral in data centers, significantly reducing DNN training time. The interconnection type among nodes and the chosen all-reduce algorithm critically impact this speed-up. This paper examines the efficiency differences in distributed DNN training across optical and electrical interconnect systems using various all-reduce algorithms. We first explore the Ring and Recursive Doubling (rd) all-reduce algorithms in both systems, followed by formulating a communication cost model for these algorithms. Performance comparison is then carried out via extensive experiments. Our results reveal that, in 1024-node systems, the Ring algorithm outperforms the rd algorithm in optical and electrical interconnects when data transfer exceeds 64MB and 1024 MB, respectively. We also find that both Ring and rd algorithms in optical interconnect systems reduce average communication time by around 75% compared to electrical interconnect systems across four different DNNs. Interestingly, the communication time of the rd algorithm, but not the Ring algorithm, reduces as the number of wavelengths increase in optical interconnects. These findings provide valuable insights into DNN training optimization across various interconnect systems and lay the groundwork for future related research.

关键词： Performance comparison Distributed DNN training Electrical interconnect Optical interconnect Performance comparison parallel computation

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parallelized ADMM with General Objectives for Deep Learning 23rd

Parallelized ADMM with General Objectives for Deep Learning

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Shi, Yanqi Tang, Yu Zheng, Hao Kan, Zhigang Qiao, Linbo Natl Univ Def Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819707973;9789819707980

While considerable efforts have been dedicated to improving models that employ regularized functions, the direct solution of non-convex models using most stochastic gradient optimization algorithms poses significant challenges due to their inherent non-convex nature. The Alternating Direction Method of Multipliers (ADMM) has emerged as a promising approach for addressing both convex and non-convex problems, boasting rapid convergence and effective constraint-handling capabilities. However, ADMM has not yet achieved significant advancements in the realm of non-convex regularized deep learning, and the development of parallelized ADMM techniques for non-convex objectives remains lacking. To address these challenges, this paper proposes the implementation of ADMM as a solution for solving general (non-convex regularized) deep learning tasks and presents a comprehensive analysis of its convergence properties. Furthermore, a parallelized framework for ADMM is proposed to address the absence of such advancements for general objectives. Experimental results reveal the stable convergence properties of ADMM when applied to non-convex objectives, demonstrating superior performance compared to ADMM with convex objectives. Additionally, we evaluate the computational efficiency of our proposed parallelized framework for ADMM.

关键词： General objectives Stochastic ADMM parallel computing

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POWErdIS: Fine-Grained Power Monitoring Through Power Disaggregation Model 23rd

POWERDIS: Fine-Grained Power Monitoring Through Power Disagg...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Qi, Xinxin Chen, Juan Deng, Rongyu Li, Zekai Deng, Lin Yuan, Yuan Che, Yonggang Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9789819708581;9789819708598

In an era where power and energy are the first-class constraints of computing systems, precise power information is crucial for energy efficiency optimization in parallel computing systems. Current power monitoring techniques rely on either software-centric power models that suffer from poor accuracy or hardware measurement schemes that have coarse granularity. This paper introduces Powerdis, a new technique for accurately measuring and forecasting the power consumption of computing components, such as CPUs and memory, within the compute node on parallel computing systems. Powerdis combines coarse-grained node power readings collected through hardware measurement and a software power disaggregation model to improve monitoring granularity while achieving high accuracy. Additionally, it can be used to predict future power usage by mining spatiotemporal patterns with minimal modifications to dataset construction building on the existing Powerdis framework. We evaluate Powerdis on both ARM-based and X86-based platforms. Extensive results show that Powerdis has great accuracy for power estimation and forecasting, reducing the mean absolute percentage error (MAPE) by up to 20% compared to other power modeling methods.

关键词： power monitoring power disaggregation model parallel computing

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