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HPC-in-Containers: A Containerized parallel Environment for parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

引用

2024 international conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Arisal, Andria Suhartanto, Heru National Research and Innovation Agency Research Center for Data and Information Sciences Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798331511470

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting. © 2024 IEEE.

关键词： Students

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Power Consumption Forecast for Load Management in Smart grid 6

Power Consumption Forecast for Load Management in Smart Grid

引用

6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Tyagi, Sarthak Kaitwade, Nidhishri Seraphim, B. Ida Srmist Dept. of Computing Technology Chennai India

ISBN: (纸本)9798350394702

Amidst the dynamic landscape of contemporary energy management, this research delves into the pivotal challenge of power consumption forecasting, a linchpin for efficacious load management within the ambit of smart grids. Conventional power grids suffer from many problems like grid instability which results in power outages, voltage fluctuations, and even grid failures, overloading of transmission and distributed system, energy wastage etc. which are solved by smart grid solutions like two-way communication between the utility company and the consumer, automation and control which provides overall grid resilience, energy efficiency and integration of renewable *** core objective of this research is to confront the limitations inherent in conventional grids through the harnessing of forecasting techniques within a time series framework to address the pressing load management issues through the development and a comparative analysis of three distinct time series forecasting models: ARIMA, SARIMA, and LSTM. Moreover, the project lays the foundation for future enhancements, like the integration of more advanced machine learning techniques and time series models, and the incorporation of additional data sources. This research contributes to the ongoing evolution of smart grid technology, with far-reaching implications for energy management, renewable energy sources and sustainability in the modern world. © 2023 IEEE.

关键词： Time series

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Enabling High-Throughput parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring

Enabling High-Throughput Parallel I/O in Particle-in-Cell Mo...

引用

2024 international conference on Cluster computing

作者： Williams, Jeremy J. Medeiros, Daniel Costea, Stefan Tskhakaya, David Poeschel, Franz Widera, Rene Huebl, Axel Klasky, Scott Podhorszki, Norbert Kos, Leon Podolnik, Ales Hromadka, Jakub Narwal, Tapish Steiniger, Klaus Bussmann, Michael Laurel, Erwin Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden Univ Ljubljana LeCAD Ljubljana Slovenia CAS Inst Plasma Phys Prague Czech Republic Helmholtz Zentrum Dresden Rossendorf Dresden Germany Lawrence Berkeley Natl Lab Berkeley CA USA Oak Ridge Natl Lab Oak Ridge TN USA Max Planck Comp & Data Facil Garching Germany

ISBN: (纸本)9798350383461;9798350383454

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massivelyparallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

关键词： openPMD Darshan ADIOS2 parallel I/O Efficient Data Processing distributed Storage Large-Scale PIC Simulations

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Research and implementation of a traceability algorithm for electric distribution data resources considering distributed new energy 6

Research and implementation of a traceability algorithm for ...

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6th international conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Qiao, Junfeng Zhou, Aihua Peng, Lin Shen, Xiaofeng Gu, Hua Huang, Chenhong State Grid Key Lab. of Info. and Netwk. Secty. of State Grid Smart Grid Research Institute Co. LTD. Jiangsu Nanjing210003 China Qingpu Power Supply Company State Grid Shanghai Electric Power Company Shanghai201799 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

The traceability and analysis of power distribution business data depend on the accurate description of the correlation network of power distribution data resources. In response to the above requirements, this paper first builds a multi-source heterogeneous power distribution and distributed new energy data flow topology network. Aiming at the complex relationship between power distribution and data resources and the flow characteristics of data, topology network construction technology is used to build bus type, star type, ring type, tree type, mesh and other topology networks according to the data association characteristics to achieve accurate description of the data association network. Then, based on the data association relationship network of power distribution and distributed energy, the data traceability must be calculated, and the traceability analysis can only be carried out if there is connectivity between the data. In response to the above requirements, the research on connectivity analysis algorithm based on power distribution and distributed new energy data network was carried out. By solving the connection path between the data nodes, the connectivity analysis requirements of the data nodes in the directed connected network and the directed connected network can be met. The data traceability of power distribution and distributed new energy is to trace the source of data in all business links, which plays an important role in the safe production, problem tracing, root cause analysis and other businesses of power distribution. In response to the above requirements, carry out research on tracing technology of allocated electricity and distributed new energy y based on path analysis. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Network topology

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parallel Algorithm Design and Optimization for Numerical Simulation Application of Ion Implantation in Silicon 29

Parallel Algorithm Design and Optimization for Numerical Sim...

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

作者： Qi, Ruixuan Yang, Wangdong Yan, Xiuwen Li, Honglu Zhang, Yifei Li, Kenli Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China The 48th Research Institute of China Electronic Technology Corporation Hunan Changsha China

ISBN: (纸本)9798350330717

We design a computational framework for ion implantation into silicon that can simulate the reaction process of cascade collisions in silicon and the subsequent annealing process. Since simulating materials requires large-scale particle data and high-precision simulation requirements, which often take up a lot of computing time, the conventional computing process needs to be accelerated in parallel to solve current problems. However, the current field of ion implantation focuses on the development of algorithms, often neglecting the design of parallel algorithms, and has not tested them on large-scale supercomputing clusters. Based on Tianhe new generation high-performance computers, this paper proposes a parallel computing framework for numerical simulation of ion implanted silicon based on multi-core processor architecture. By optimizing the data structure, the local continuity of the data is enhanced to improve the overall communication efficiency. Divide three-dimensional data for processes and adapt to MPI parallel computing strategies. Finally, a multi-level parallel computing strategy based on OpenMP is added to improve computing efficiency. After being deployed on the Tianhe new generation high-performance computer platform, experimental results show that on a single node, the multi-level parallel computing framework can increase the computing speed by 20.81X. After conducting tests at different scales, we can see that the parallel efficiency can be maintained at a high level. © 2023 IEEE.

关键词： data structure optimization ion implantation multi-level parallel strategy

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Deep Learning-Based Control Model for Electronic Evidence Access Rights 4

Deep Learning-Based Control Model for Electronic Evidence Ac...

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4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Chen, Peng Li, Yongliang Gao, Xianzhou State Grid Ningxia Electric Power Co.Ltd. Ningxia750001 China State Grid Ningxia Electric Power Co.Ltd. Electric Power Research Institute Ningxia750002 China State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9798350337181

With the rapid development of digitalization and intelligence in smart grids, how to identify untrusted users in the electric power system and implement user access control based on electronic evidence has become an important research topic. A credible assessment needs to be made on the subject users accessing electronic evidence. In this paper, we propose a deep learning-based user multi-dimensional attribute feature extraction access control model, which considers multi-dimensional attribute features such as user identity, behaviour, and device information, and determines whether the user is a legitimate access user by using CNN-Transformer deep learning framework to extract user features and determine their trust value. Through simulation experiments, it is proved that the access permission control model proposed in this paper has high accuracy and reliability and can meet the access requirements of different electronic evidence users. © 2023 IEEE.

关键词： Smart power grids

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Design and Optimization of Smart Contracts for Cross-Domain Sharing of Sensitive Data 30

Design and Optimization of Smart Contracts for Cross-Domain ...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Zhu, Manqing Wang, Lingxiao Li, Xiaohong Tianjin University College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798331515966

While studying the scenarios of sensitive data cross-domain sharing, we found there was no security assessment of smart contracts within existing data sharing schemes, which might lead to data leakage or unauthorized data access. To address this issue, we proposed a comprehensive security assessment of the smart contracts involved in existing data sharing solutions. In which, we extended the current smart contract tools to include detection capabilities for 7 additional types of vulnerabilities and implemented automatic repair functions for smart contracts. We also designed 3 types of smart contracts for the Hyperledger Fabric platform to facilitate sensitive data cross-domain sharing, specifically for data upload, data access, and data management functions to make sure that complex vulnerabilities could be automatically repaired. In our designation, the smart contracts for data upload and data access utilize the CP-ABE method and the data management smart contract includes various functions for secure management and tracing of data on the blockchain, which collectively ensure the security and comprehensive functionality of sensitive data cross-domain sharing scheme. © 2024 IEEE.

关键词： Information leakage

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Research on the distributed Secure Storage of Embedded Terminals Based on the Blockchain 26

Research on the Distributed Secure Storage of Embedded Termi...

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Yun-Xi, Li Zhong-Hua, Wang Cheng-Lie, Du Ya-Hui, Li Jian-Feng, Ma Wen-Sheng, Niu Northwestern Polytechnical University Department of Computer Science Xi'an China Technique Research Institute Avic Xi'an Aeronautics Computing Xi'an China Xidian University School of Cyber Engineering Xi'an China Avicas Generic Technology CO. Ltd Yang Zhou China

ISBN: (纸本)9798350345865

The blockchain is a distributed public ledger technology that originates from the digital crypto currency bitcoin. Blockchain's key advantages include decentralization, time-series data, collective maintenance, programmability and security. It breaks through the limitation of traditional center-based technology and has broad military development prospect. Nevertheless, with the distributed embedded devices in the battlefield in the future, the cooperative computing task needs to be performed effectively and rapidly among them. Furthermore, in order to achieve the aim of the cooperative computation on the multi-device, the urgent and primary problem is how to realize the effective share and secure storage about the critical data. Consequently, given the requirement of data sharing, the architecture design of data distributed secure storage based on the blockchain is proposed in this paper. Besides, the vivid descriptions of some key functional modules are given. © 2023 IEEE.

关键词： Blockchain

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Spark-KNN parallel Pattern Recognition Technique for Substation Secondary Cable Data 3

Spark-KNN Parallel Pattern Recognition Technique for Substat...

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3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Lu, Weizhou Huang, Haiying Luo, Huan Guangdong Power Grid Maoming Power Supply Bureau China

ISBN: (纸本)9798350382426

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data. © 2023 IEEE.

关键词： Online monitoring Secondary cable data Spark-KNN

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