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Rethinking the distributed DNN Training Cluster Design from the Cost-effectiveness View 25

Rethinking the Distributed DNN Training Cluster Design from ...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Lai, Zhiquan Liu, Yujie Wang, Wei Hao, Yanqi Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

As deep learning grows rapidly, model training heavily relies on parallel methods and there exist numerous cluster configurations. However, current preferences for parallel training focus on data centers, overlooking the financial constraints faced by most researchers. To attain the best performance within the cost limitation, we introduce a throughput-cost metric to accurately characterize clusters' cost-effectiveness. Based on this metric, we design a cost-effective cluster featuring the 3090 with NVLink. the experiment results demonstrate that our cluster achieves remarkable cost-effectiveness in various distributed model training schemes. © 2023 ieee.

关键词： Cost effectiveness

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Development of a Visualization for Developers of a distributed System Including Idle Computers 25

Development of a Visualization for Developers of a Distribut...

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作者： Tsutsumi, Shosuke Higashinc, Masayuki Takahashi, Kenichi Kawamura, Takao Tottori University Department of Engineering Tottori Japan

ISBN: (纸本)9798350330014

this paper proposes a visualization function for developers in volunteer computing. In recent years, information systems such as e-Learning systems and groupware have been introduced in companies and educational institutions to promote DX, but the introduction and operation of these systems incur a large cost burden. therefore, we are developing a distributed information system that can be introduced and operated at low cost by utilizing surplus computers (idle computers) in an organization. Idle computers are unstable depending on the operations of their original users and have different performances. In order to use idle computers efficiently, it is necessary to improve the performance of the system and modify it to add new functions, taking into account the differences in behavior and performance of each machine. In this paper, visualization for developers to easily check the behavior and performance of a computer in an idle state is developed, and its usefulness is evaluated through experiments using a real system. © 2023 ieee.

关键词： Visualization

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Construction of a Load-testing Environment for distributed systems Including Idle Computers 25

Construction of a Load-testing Environment for Distributed S...

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作者： Nagashima, Yuya Higashino, Masayuki Kawamura, Takao Tottori University Tottori Japan

ISBN: (纸本)9798350330014

In recent years, many educational institutions have adopted information systems such as e-Learning platforms and groupware. However, these existing information systems often have significant installation, operation, and maintenance costs. Moreover, on-premise systems require expensive servers and cloud computing systems can accumulate high maintenance expenses. To address this issue, we have proposed that utilizes idle resources (idle computers) in an organization's information system. One of the characteristics of idle computers is that communication lines can be connected or disconnected at arbitrary times according to the operations of the original users. In general, the distributed system uses an idle computer as an unstable resource. However, our proposed system requires tolerating them as normal behaviors, and it requires a test environment for this problem. In this paper, we propose a load-testing environment to use idle computers having unstable behaviors in a distributed system. We also implemented and applied the proposed environment to a distributed e-Learning system. the result shows efficiency in showing behavior of unstable behaviors of idle computers to developers. © 2023 ieee.

关键词： Information systems

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Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

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作者： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. the efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 ieee.

关键词： Deep neural networks

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Spectral Clustering Based Resource Allocation Strategy for distributed Large-scale Network Emulation 25

Spectral Clustering Based Resource Allocation Strategy for D...

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作者： Chen, Jiyan Gu, Rentao Bai, Lin Sch. of Information and Communication Engineering Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

Nowadays, networks are increasingly reliant on software frameworks and virtualization. To obtain relevant pre-dictions of the behavior of their protocols, network emulation tools play a crucial role. However, as networks grow in size and complexity, the emulation task demands more physical resources such as CPU and memory. Consequently, a single physical machine is no longer sufficient to handle large-scale network emulation. Instead, distributed network emulation tools must be employed for resource-intensive network emulation tasks. In resource-intensive distributed network emulation, the physical resource allocation is NP-hard to perform appropriately. In this paper, we propose SCBG (spectral clustering based greedy node placement algorithm) as a means to enhance the bandwidth utilization between physical nodes in distributed emulation. SCBG aims to address the communication bottleneck in distributed network simulation, surpassing the algorithm proposed by Distrinet, while also improving the algorithm's execution speed. Additionally, SCBG incorporates optimization for time dilation when physical bandwidth resources are severely insufficient. this optimization leads to a reduction in the additional time required for network simulation after applying time dilation. Experimental results demonstrate that SCBG surpasses the algorithm in Distrinet across a diverse range of intricate large-scale networks. © 2023 ieee.

关键词： Optimization

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Solar Irradiance Forecasting Utilizing distributed and Federated Learning 25

Solar Irradiance Forecasting Utilizing Distributed and Feder...

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作者： Zhang, Hao Yan, Ke College of Information Engineering China Jiliang University Hangzhou China China Jiliang University National University of Singapore Singapore

ISBN: (纸本)9798350330014

Photovoltaic energy generation and prediction are crucial for the integration between solar plants and smart grid. Accurate solar radiation prediction is one of the most important research topics for solar energy generation. Existing deep learning methods require sufficient data for training for achieving the desired results. In traditional deep learning modeling, the data sets required for model training usually come from various data in different regions and organizations, and it is difficult to ensure data security and privacy protection. Federated learning can effectively utilize the data optimization model while ensuring data security and privacy, but there is still the risk of privacy leakage by analyzing the parameters uploaded by the client. To effectively utilize data optimization models while ensuring data security and privacy, we propose a method based on a federated learning framework and N-BEATS to achieve interpretable time series prediction of solar irradiance. the model builds the N-BEATS model on the solar irradiance datasets of multiple institutions in a distributed method, does not share local data to protect institutional data privacy, the concept of differential privacy (DP) is introduced, Gaussian noise is added to the parameters of the client before the server-side aggregation processing, the model parameters of each distributed node are collected through TCP communication for federated average, and the parameters are returned to each distributed node. the experimental results show that compared with other deep learning models, the model has better prediction accuracy under the premise of ensuring data security and privacy. © 2023 ieee.

关键词： Solar radiation

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IRS-Aided Caching with Channel Power-Delay-Profile via DDPG and Self-Supervision Learning 9

IRS-Aided Caching with Channel Power-Delay-Profile via DDPG ...

引用

9th international conference on Computer and Communication systems (ICCCS)

作者： Huang, Huang Zhou, Fasheng Guangzhou Univ Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangdong Hi Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

the 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line with the requirements of 5G green technology. therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. the numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.

关键词： Deep deterministic policy gradient (DDPG) edge caching intelligent reflecting surface average power-delay-profile(APDP) spectrum efficiency (SE) self-supervision learning

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism 25

Efficient Large Models Fine-tuning on Commodity Servers via ...

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作者： Liu, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. this solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance. © 2023 ieee.

关键词： Program processors

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

the development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 ieee.

关键词： Internet of things

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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location 25

indexPDT: A High Scalable Distributed Classification Approac...

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作者： Sun, Zhijie Li, Jing Xie, Jun Zheng, Binfan Zeng, Li Zhao, Rongqian Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. the geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. the cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively. © 2023 ieee.

关键词： Location

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