检索结果-内蒙古大学图书馆

2023 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhao, Honglei Zhang, Peiyao Sun, Jingxiu Su, Jianping Ming, Meng Aostar Information Technologies Co. Ltd. Sichuan Chengdu610000 China State Grid Information and Communication Industry Group Co. Ltd. Beijing100000 China

ISBN: (纸本)9781510671881

This paper summarizes the development status, prediction methods and classification of wind power power prediction at home and abroad, and verifies three typical power prediction models through example comparison- -ARMA power prediction model, Kalman filter model and a distributed solar power generation area. Through the comprehensive analysis of the evaluation indicators of the prediction results, it is found that the wavelet neural network prediction model has the highest accuracy, and realizes the short term and super short term accurate prediction for the small range of distributed photovoltaic and wind power. To meet the actual operation needs of the power grid, it has a certain promotion and application value in the distributed power generation projects. © 2023 SPIE. All rights reserved.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Data transfer for STAR grid jobs 20

Data transfer for STAR grid jobs

引用

20th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Chakaberia, Irakli Lauret, Jerome Poat, Michael Porter, Jefferson Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Brookhaven Natl Lab Upton NY USA

The Solenoidal Tracker at RHIC (STAR) is a multipurpose experiment at the Relativistic Heavy Ion Collider (RHIC) with the primary goal to study the formation and properties of the quark-gluon plasma. STAR is an international collaboration of member institutions and laboratories from around the world. Yearly data-taking period produces PBytes of raw data collected by the experiment. STAR primarily uses its dedicated facility at BNL to process this data, but has routinely leveraged distributed systems, both high throughput (HTC) and high performance (HPC) computing clusters, to significantly augment the processing capacity available to the experiment. The ability to automate the efficient transfer of large data sets on reliable, scalable, and secure infrastructure is critical for any large-scale distributed processing campaign. For more than a decade, STAR computing has relied upon gridFTP with its x509-based authentication to build such data transfer systems and integrate them into its larger production workflow. The end of support by the community for both gridFTP and the x509 standard requires STAR to investigate other approaches to meet its distributed processing needs. In this study we investigate two multi-purpose data distribution systems, *** and XRootD, as alternatives to gridFTP. We compare both their performance and the ease by which each service is integrated into the type of secure and automated data transfer systems STAR has previously built using gridFTP. The presented approach and study may be applicable to other distributed data processing use cases beyond STAR.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Power System Simulation Platform Based on Cloud computing 4

Design and Implementation of Power System Simulation Platfor...

引用

4th international conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue National Energy Power Grid Technology R&d Centre Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

Power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

AJAS: A high performance direct solver for advancing high precision astrometry

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2025年 87卷

作者： Ryabinin, Konstantin Sarras, Gerasimos Loeffler, Wolfgang Erokhina, Olga Biermann, Michael Heidelberg Univ Ctr Astron Astron Rechen Inst Monchhofstr 12-14 D-69120 Heidelberg Germany

In astrometry, the determination of three-dimensional positions and velocities of stars based on observations from a space telescope suffers from the uncertainty of random and systematic errors. The systematic errors are introduced by imperfections of the telescope's optics and detectors as well as in the pointing accuracy of the satellite. The fine art of astrometry consists of heuristically finding the best possible calibration model that will account for and remove these systematic errors. Since this is a process based on trial and error, appropriate software is needed that is efficient enough to solve the system of astrometric equations and reveal the astrometric parameters of stars for the given calibration model within a reasonable time. This paper is an extended version of the conference paper published and discussed at the international conference on Computational Science 2024. In this work, we propose a novel software architecture and corresponding prototype of a direct solver optimized for running on supercomputers. The main advantages expected from this direct method over an iterative one are the numerical robustness, accuracy of the method, and the explicit calculation of the variance-covariance matrix for the estimation of the accuracy and correlation of the unknown parameters. This solver can handle astrometric systems with billions of equations within several hours. To reach the desired performance, we use state-of-the-art libraries and methods for hybrid parallel and vectorized computing. The calibration model based on Legendre polynomials is tested by generating synthetic observations on grid-shaped constellation with specified distortions. For these small-sized test data, the solver can recover perfectly the correct physical solution under the condition that the correct amount of eigenvalues is zeroed out. During the space mission, the calibration model should be carefully fine-tuned according to the real operating conditions. The developed solver

关键词： Astrometry Instrument calibration High-performance computing Least Squares Method Eigenproblem Pseudo-inverse matrix

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-enabled Secure Access Management Method in Edge computing 29

A Blockchain-enabled Secure Access Management Method in Edge...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Jiang, Xutong Zhang, Guoming Tian, Hao Xiang, Haolong Zhang, Xuyun Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Macquarie University School of Computing Sydney Australia

ISBN: (纸本)9798350330717

Edge computing has emerged as a transformative paradigm with applications ranging from IoT to smart homes and transportation systems. However, its decentralized nature presents significant challenges in securing sensitive data, particularly in scenarios involving data sharing. To address these issues, this paper introduces a secure data access control system for edge computing by leveraging blockchain to foster mutual trust among edge servers and employing attribute-based encryption for secure data sharing. Our proposed solution seeks to enhance data security in distributed environments. We validate our method through practical experiments conducted on multiple edge servers, confirming its effectiveness in safeguarding data integrity and privacy. © 2023 IEEE.

关键词： blockchain edge computing encryption security

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Edge Assistance for Solving Matrix Eigenvalue Problem 33

Privacy-Preserving Edge Assistance for Solving Matrix Eigenv...

引用

33rd IEEE international conference on Computer Communications and Networks (ICCCN)

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Kong, Fanyu Yu, Leyun Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China JIC IOT CO LTD Nanchang Peoples R China

ISBN: (纸本)9798350348439;9798350384611

The large-scale matrix eigenvalue computation, as a basic mathematical tool, has been widely used in many fields such as face recognition and data analysis. However, local terminal devices lack sufficient resources to undertake heavy computational tasks, which poses a challenge to the applications of eigenvalue computation. In this paper, we propose the first privacy-preserving edge-assisted computation scheme for solving the largest eigenvalue and corresponding eigenvector. We propose a privacy-preserving transformation method to protect data privacy and prevent edge servers from retrieving sensitive information. Meanwhile, we design a verification scheme to ensure the correctness of the results returned by the edge servers. In addition, we design a distributed parallel computing scheme to ensure the efficiency of edge computation. Through theoretical analysis and simulation experiments, we verify the feasibility and efficiency of our proposed scheme.

关键词： Eigenvalue problem edge computing privacypreserving parallel computing

来源：评论

学校读者我要写书评

暂无评论

FP16 Acceleration in Structured Multigrid Preconditioner for Real-World Applications 24

FP16 Acceleration in Structured Multigrid Preconditioner for...

引用

53rd international conference on parallel Processing (ICPP)

作者： Zong, Yi Huang, Haopeng Yu, Peinan Xue, Wei Tsinghua Univ Beijing Peoples R China Qinghai Univ Qinghai Prov Lab Intelligent Comp & Applicat Xining Qinghai Peoples R China

ISBN: (纸本)9798400717932

Half-precision hardware support is now almost ubiquitous. In contrast to its active use in AI, half-precision is less commonly employed in scientific and engineering computing. The valuable proposition of accelerating scientific computing applications using half-precision prompted this study. Focusing on solving sparse linear systems in scientific computing, we explore the technique of utilizing FP16 in multigrid preconditioners. Based on observations of sparse matrix formats, numerical features of scientific applications, and the performance characteristics of multigrid, this study formulates four guidelines for FP16 utilization in multigrid. The proposed algorithm demonstrates how to avoid FP16 overflow through scaling. A setup-then-scale strategy prevents FP16's limited accuracy and narrow range from interfering with the multigrid's numerical properties. Another strategy, recover-and-rescale on the fly, reduces the memory footprint of hotspot kernels. The extra precision-conversion overhead in mix-precision kernels is addressed by the transformation of storage formats and SIMD implementation. Two ablation experiments validate the effectiveness of our algorithm and parallel kernel implementation on ARM and X86 architectures. We further evaluate three idealized and five real-world problems to demonstrate the advantage of utilizing FP16 in a multigrid precon-ditioner. The average speedups are approximately 2.75x and 1.95x in preconditioner and end-to-end workflow, respectively.

关键词： multigrid sparse matrix structured grid preconditioner

来源：评论

学校读者我要写书评

暂无评论

Improving Input-Step Performance

Improving Input-Step Performance

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Adams, Joel C. Calvin University Department of Computer Science Grand RapidsMI United States

ISBN: (纸本)9798350355543

In many "Big Data"problems, the data to be analyzed are stored in files;to solve such problems, an input step reads the data from a file into an array for processing. This input step has traditionally been performed sequentially, causing the time to perform that step to grow linearly with N, the number of values in the file. This paper explores different ways to reduce the time consumed by the input step, including the use of different file formats, as well as parallel I/O via MPI-IO. To make parallel I/O easier for students to use, we have created OO_MPI_IO, a new set of C++ abstractions that hide the complexity of MPI-IO. We also demonstrate how these OO_MPI_IO abstractions can (i) improve the scalability of data-intensive problem solutions, and (ii) provide a means of helping students understand Amdahl's and Gustafson's Laws. © 2024 IEEE.

关键词： Amdahl's Law computing distributed Gustafson's Law I/O input MPI output parallel sequential speedup

来源：评论

学校读者我要写书评

暂无评论

Scalable Random Forest with Data-parallel computing 29th

Scalable Random Forest with Data-Parallel Computing

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Vazquez-Novoa, Fernando Conejero, Javier Tatu, Cristian Badia, Rosa M. Barcelona Supercomp Ctr BSC CNS Barcelona Spain

ISBN: (纸本)9783031396977;9783031396984

In the last years, there has been a significant increment in the quantity of data available and computational resources. This leads scientific and industry communities to pursue more accurate and efficient Machine Learning (ML) models. Random Forest is a well-known algorithm in the ML field due to the good results obtained in a wide range of problems. Our objective is to create a parallel version of the algorithm that can generate a model using data distributed across different processors that computationally scales on available resources. This paper presents two novel proposals for this algorithm with a data-parallel approach. The first version is implemented using the PyCOMPSs framework and its failure management mechanism, while the second variant uses the new PyCOMPSs nesting paradigm where the parallel tasks can generate other tasks within them. Both approaches are compared between them and against MLlib Apache Spark Random Forest with strong and weak scaling tests. Our findings indicate that while the MLlib implementation is faster when executed in a small number of nodes, the scalability of both new variants is superior. We conclude that the proposed data-parallel approaches to the Random Forest algorithm can effectively generate accurate and efficient models in a distributed computing environment and offer improved scalability over existing methods.

关键词： Random Forest PyCOMPSs COMPSs parallelism distributed computing Dislib Machine Learning HPC

来源：评论

学校读者我要写书评

暂无评论

parallel Physics-Informed Neural Networks & Giza Pyramid Construction Optimization Algorithm for FDIAs Detection in Industrial IoT:ML based Techniques 2

Parallel Physics-Informed Neural Networks & Giza Pyramid Con...

引用

2nd international conference on Sustainable computing and Smart Systems (ICSCSS)

作者： Reddy, Basi A. Rajkumar, R. Yogesh Bharath Inst Higher Educ & Res Dept CSE Chennai Tamil Nadu India Bharath Inst Higher Educ & Res Dept IT Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The rapid evolution of IIoT (Industrial Internet of Things) in computing has brought about numerous security concerns, among which is the looming threat of False Data Injection (FDI) attacks. To address these attacks, a study introduces a novel approach called MLBT-FDIA-IIoT (Fault Data Injection Attack Detection in IIoT using parallel Physics-Informed Neural Networks with Giza Pyramid Construction Optimization algorithm). This method makes use of real-time sensor data for attack detection. The data is preprocessed using distributed Set-Membership Fusion Filtering (DSMFF) to remove noise. Then, it is fed into a neural network for classification. Specifically, parallel Physics-Informed Neural Networks (PPINN) are used to distinguish between normal operations and False Data Injection Attacks (FDIAs). However, PPINN lacks optimization methods for accurate detection. To address this, the study proposes the Giza Pyramid Construction Optimization algorithm (GPCOA). This algorithm optimizes the PPINN classifier to detect attacks with more precision. The proposed MLBT-FDIA-IIoT method is implemented using MATLAB and evaluates various metrics such as accuracy, recall, and precision. The results demonstrate significant improvements compared to existing techniques such as MLT-FDI-IIoT, FDIA-FDAS-IIoT, and DCDD-IIoT-FDIA.

关键词： MLBT-FDIA-IIoT DSMFF FDI attacks GPCOA Industrial Internet of Things (IIoT) PPINN

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：