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Bandit-based Variable Fixing for Binary Optimization on GPU parallel computing 31

Bandit-based Variable Fixing for Binary Optimization on GPU ...

引用

31st Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2023

作者： Yasudo, Ryota Graduate School of Informatics Kyoto University Sakyo-ku Kyoto606-8501 Japan

ISBN: (纸本)9798350337631

This paper explores whether reinforcement learning is capable of enhancing metaheuristics for the quadratic unconstrained binary optimization (QUBO), which have recently attracted attention as a solver for a wide range of combinatorial optimization problems. In particular, we introduce a novel approach called the bandit-based variable fixing (BVF). The key idea behind BVF is to regard an execution of an arbitrary metaheuristic with a variable fixed as a play of a slot machine. Thus, BVF explores variables to fix with the maximum expected reward, and executes a metaheuristic at the same time. The bandit-based approach is then extended to fix multiple variables. To accelerate solving multi-armed bandit problem, we implement a parallel algorithm for BVF on a GPU. Our results suggest that our proposed BVF enhances original metaheuristics. © 2023 IEEE.

关键词： Decision making

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AIACC-Training: Optimizing distributed Deep Learning Training through Multi-streamed and Concurrent Gradient Communications 42

AIACC-Training: Optimizing Distributed Deep Learning Trainin...

引用

42nd IEEE international conference on distributed computing Systems (ICDCS)

作者： Lin, Lixiang Qiu, Shenghao Yu, Ziqi You, Liang Xin, Long Sun, Xiaoyang Xu, Jie Wang, Zheng Alibaba Grp Hangzhou Peoples R China Univ Leeds Leeds W Yorkshire England

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

There is a growing interest in training deep neural networks (DNNs) in a GPU cloud environment. This is typically achieved by running parallel training workers on multiple GPUs across computing nodes. Under such a setup, the communication overhead is often responsible for long training time and poor scalability. This paper presents AIACC-Training, a unified communication framework designed for the distributed training of DNNs in a GPU cloud environment. AIACC-Training permits a training worker to participate in multiple gradient communication operations simultaneously to improve network bandwidth utilization and reduce communication latency. It employs auto-tuning techniques to dynamically determine the right communication parameters based on the input DNN workloads and the underlying network infrastructure. AIACC-Training has been deployed to production at Alibaba GPU Cloud with 3000+ GPUs executing AIACC-Training optimized code at any time. Experiments performed on representative DNN workloads show that AIACC-Training outperforms existing solutions, improving the training throughput and scalability by a large margin.

关键词： distributed deep learning Model training Communication optimization

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The High Error Tolerance Characteristics of Hadoop HDFS and the Deployment Strategy of Hyperfusion Platform

The High Error Tolerance Characteristics of Hadoop HDFS and ...

引用

2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Liang, Yingwei Zhu, Taipeng Liu, Chenghui Chen, Yue Yang, Yufei Csg Guangdong Power Grid Co. Ltd. Information Center Guangdong Guangzhou510000 China

ISBN: (纸本)9798350369212

To meet the growing demand for data applications in the context of big data, storage systems are evolving from traditional centralized architectures to distributed architectures. Hadoop HDFS has high fault tolerance, reliability, scalability, and throughput, making it very suitable for applications on large-scale datasets. With the increasing virtualization of cloud computing and data services, building cloud computing using traditional deployment methods is no longer the current requirement. The emergence of hyper fusion technology precisely solves the problems of complex traditional architecture systems and resource expansion, and broadens the horizons for the growth of cloud computing technology. This article mainly explores the high error tolerance characteristics of Hadoop HDFS and the deployment strategy of hyper fusion platforms. Firstly, the high error tolerance mechanism of HDFS and the advantages of hyper fusion platform were introduced, and a hyper fusion platform structure based on Hadoop HDFS was designed. The experimental results show that the high error tolerance of Hadoop HDFS and the deployment strategy of the hyper fusion platform can complement each other, providing users with efficient, reliable, and flexible large-scale data storage and processing solutions. © 2024 IEEE.

关键词： Cloud platforms

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distributed Generative Adversarial Networks for Fuzzy Portfolio Optimization 1

引用

23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Yang, Xueying Li, Chen Han, Zidong Lu, Zhonghua Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Financial time series is one of the most important data in the field of economics and finance, and it is important to forecast and simulate such data effectively based on historical patterns and trends. Existing forecasting models mainly forecasting one-step ahead, and cannot retain the complex characteristics of financial time series data such as serial correlation and the long-term time-dependent relationship. On the other hand, the large-scale data makes the training of the deep learning models a time-consuming process. Therefore, how to forecast financial time series multi-step ahead efficiently has become a key point to improve the asset management capability. At the same time, constructing a fuzzy portfolio optimization for different distributions is also an important direction to improve the robustness of a portfolio model. This paper proposes a distributed financial time series simulating model AssetGANs that simulating multi-step ahead based on GANs, and apply GANs as a parameter simulation method to fuzzy portfolio optimization to provide users with better strategy choices. The paper carries on numerical experiments on real market stock data, compares the results with LSTM and achieves a training speedup of over 573 with 8 GPUs compared to the CPU version.

关键词： Generative Adversarial Networks Fuzzy Simulation Portfolio Optimization parallel computing

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Construction of Digital Distribution Network Optimization and Scheduling System Based on Improved Genetic Algorithm

Construction of Digital Distribution Network Optimization an...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Li, Mengyuan He, Lianjie Wang, Gang State Grid Corporation of China Beijing100032 China State Grid Shanghai Energy Interconnection Institute Co. Ltd. Shanghai201203 China

ISBN: (纸本)9798350375275

This study aims to construct a digital distribution network optimization and scheduling system based on IGA (Improved Genetic Algorithm) to improve the efficiency and reliability of the power system. In order to achieve our research objectives, we have designed an IGA based optimization scheduling system that can handle complex problems such as load balancing, minimizing power losses, and maintaining voltage and current stability in digital distribution networks. Our research will validate the performance of the algorithm through experimental data and scenario settings, and analyze the experimental results to evaluate the optimization effect of the system. The research results show that the power loss of using IGA algorithm to optimize digital distribution network is the lowest, maintaining an average of around 15KW. The power loss of GA (Genetic Algorithm) is around 24KW, and the power loss of SA (Simulated Annealing) is around 27KW. It can be seen that SA loss is the largest, and this algorithm is not suitable for optimization of distribution network. The algorithm in this paper is more suitable for optimization of distribution network. In summary, this study provides a comprehensive solution for the optimization and scheduling of digital distribution networks, emphasizing the potential application of IGA. © 2023 IEEE.

关键词： Genetic algorithms

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Assessment of Voltage and Frequency in Islanded Microgrids by Varying Load Parameters Using Modified Gauss-Seidel Power Flow

Assessment of Voltage and Frequency in Islanded Microgrids b...

引用

2023 IEEE international conference on Power Electronics, Smart grid, and Renewable Energy, PESGRE 2023

作者： Lone, Ashiq Hussain Gupta, Neeraj National Institute of Technology Department of Electrical Engineering Srinagar India

ISBN: (纸本)9798350310573

Power flow studies play a crucial role in the planning and expansion of power systems, particularly with the increasing integration of distributed generation (DG) and the emergence of micro-grids. Micro-grids, which are localised power systems with their own generation and load, have become an attractive option for enhancing the resilience and efficiency of electric supply. The effect of load parameters in conjugation with the Modified Newton Raphson (MNR) power flow has been investigated in this paper. To validate the effectiveness of the proposed method, the MNR algorithm has been implemented and tested on a 6-bus islanded microgrid test bus system. This test system serves as a representative model for evaluating the performance and capabilities of the MNR method in solving power flow analysis for islanded micro-grids. © 2023 IEEE.

关键词： distributed generation Microgrid modified Newton Raphson Power flow

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BALANCING THE RELIABILITY AND COST OF ENERGY SYSTEMS UNDER grid AND COMPONENT FAILURES THROUGH PARETO-OPTIMAL STORAGE OPERATION 37

BALANCING THE RELIABILITY AND COST OF ENERGY SYSTEMS UNDER G...

引用

37th international conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Brosius, Benedict Frings, Dustin Nilges, Benedikt von der Assen, Niklas Institute of Technical Thermodynamics RWTH Aachen University Aachen Germany

ISBN: (纸本)9798331307660

In distributed multi-energy systems, the reliability of energy supply is threatened by unplanned failures of grid utilities and energy system components. To improve reliability, thermal energy storages can be integrated. In addition to improving reliability, thermal energy storages reduce operating costs, e.g., by capitalizing on electricity price fluctuations. However, existing storage operation methods neglect the inherent trade-off between reliability and costs. In contrast, our study presents a method to balance both objectives. Our method extends a cost-optimization problem with an approximation of the Expected Energy Not Served, a widely used reliability metric. To solve the resulting bi-objective problem, we employ a self-adjusting weighted sum algorithm that generates Pareto-optimal operating schedules. Finally, we validate the reliability of these operating schedules in a Monte Carlo reliability assessment. In an industrial case study, we benchmark our method against a state-of-the-art operation method. In comparison, our method reduces undersupply by 19.2 % and operating costs by 1.5 %. Consequently, our method can improve both the reliability and costs of distributed multi-energy systems with storages. © 2024 37th international conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024. All rights reserved.

关键词： Clean energy

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Economic Analysis on Transmission System of Offshore Oil Platform Based on LCC 2

Economic Analysis on Transmission System of Offshore Oil Pla...

引用

2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Wang, Xuebin Wang, Lei Song, Wenle Hao, Xiangyu Zhang, Xiaofan State Grid Cangzhou Electric Supply Company Cangzhou China North China Electric Power University School of Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9798350347456

Offshore platform power system is a key link in the development and utilization of offshore oil resources, and its power transmission method directly affects the safe and stable operation of offshore oil platform. To study the economics of different power transmission modes on offshore oil platforms, in this paper, the characteristics and applicability of conventional AC, flexible DC, and low-frequency transmission modes were compared, and considering the offshore distance, voltage level, transmission capacity, and other factors, a comprehensive evaluation model of offshore oil platform based on life cycle cost (LCC) was established. Then, the selection principles of offshore oil platform-based transmission mode suitable for different operation scenarios were proposed, and the best transmission methods for offshore oil platform were obtained, the DC transmission is the most economical for long-distance offshore oil platforms, which can provide reference and basis for the development of shore-based power supply scheme for offshore oil platform. © 2023 IEEE.

关键词： Life cycle

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Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India 7

Comparison of Machine Learning Models for predicting Covid-1...

引用

7th international conference on parallel, distributed and grid computing, PDGC 2022

作者： Taneja, Prashansa Sharma, Aman Singh, Mrityunjay Jaypee University of Information Technology Department of Cse Solan India Una School of Computing India

ISBN: (纸本)9781665454018

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

关键词： Coronavirus

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Application analysis of edge computing in power infrastructure construction business 2

Application analysis of edge computing in power infrastructu...

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2nd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Jiang, Li Ba, Lin Zhang, Qi Liu, Jinhui Hou, Yongliang Tong, Junda State Grid Liaoning Electric Power Co. Ltd. Economic Research Institute Shenyang China State Grid Liaoning Electric Power Supply Co. Ltd Shenyang China

ISBN: (纸本)9781510664968

As the cornerstone of economic development, power infrastructure business is of great significance for improving the quality of economic development. Compared with the traditional power infrastructure, the new power infrastructure is more technical and professional, and more intelligent. Taking the application of abnormal object detection in edge computing in line infrastructure construction site as an example, this paper expounds the important role of front-end intelligent sensing devices in new infrastructure construction. Compared with the traditional cloud platform processing method, edge computing technology deploys computing power on the physical equipment and data source side of the near power distribution terminal, conducts data analysis, system operation status situational awareness, and makes independent and rapid decisions on the control execution unit side, effectively making up for the shortcomings of cloud computing, responding more timely, and having higher reliability, so it is more suitable for deployment in business scenarios with unstable network environment and high response requirements. In this paper, a new method of edge computing node deployment is proposed to meet the requirements of distributed edge computing in distribution information physics system, considering information stability and power stability The calculation model of computing information stability is constructed by quantifying the real-time degree, accuracy and integrity of information. The power stability scale is established by observing small signal voltage changes. The system information power mixed entropy is calculated based on the mixed entropy theory, and the optimal deployment of network edge computing nodes is solved Finally, based on the IEEE 39 bus power system standard example simulation, the system power stability, information transmission stability and power grid frequency control performance are numerically analyzed. The experimental simulation results verify t

关键词： Economic and social effects

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