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Data Communication Algorithm of HPDB parallel Database System Based on Computer Network

international Journal of High Speed Electronics and systems 2025年第2期34卷

作者： Ma, Zhiqiang School of Information Engineering Henan Agricultural Vocational College Henan Zhengzhou451450 China

High-Performance Database (HPDB) is designed to meet the demands of today's modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system's performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query's conditions and the data's placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis. © 2025 World Scientific Publishing Company.

关键词： Search engines

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Federated Learning Based Neural Machine Translation

Federated Learning Based Neural Machine Translation

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international symposium on Advanced Computing and Communication (ISACC)

作者： Parvez Aziz Boruah Hiren Kumar Deva Sarma Shikhar Kumar Sarma Department of Information Technology Gauhati University Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In this paper a work on Federated Learning based Machine Translation is performed using English-Assamese Language pair. Federated Learning is the concept of machine learning where data are distributed on different system called clients/nodes. Our objective was to performe a Machine Translation training using Federated Learning Architecture and observed the performance by varying the system parameters/hyperparameters. Also two different Federated Aggregation strategies have been used for comparison. It was observed that having a higher number of clients/nodes, there is a reduce in performance score.

关键词： Training Federated learning Neural machine translation distributed databases Computer architecture Intelligent systems

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Development of a Collaborative Design Platform Based on Bim (Building Information Modeling) and Cloud Computing

Development of a Collaborative Design Platform Based on Bim ...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE international Conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This study proposes a collaborative design platform based on BIM and cloud computing, combined with an innovative real-time synchronization algorithm, to solve the problems of delay, data consistency and synchronization conflict in cloud collaborative design. The proposed real-time synchronization algorithm is based on a distributed computing model. By introducing a distributed lock and timestamp mechanism, it effectively guarantees the data consistency and real-time performance in large-scale collaborative design, and can reduce data conflicts and improve collaborative efficiency when multiple design teams operate in parallel. In order to verify the effectiveness of the platform, this study conducted experiments in multiple architectural design projects, and the platform runs in mainstream cloud computing environments (such as AWS and Azure). The experimental results show that the collaborative design platform based on the innovative algorithm has significant advantages over traditional collaborative design tools in terms of synchronization delay, data transmission speed and number of concurrent users. When multiple design teams collaborate, the design cycle of the platform is shortened by 15 %, the overall work efficiency is improved by more than 20 %, and it can maintain stability and low response time in a high concurrent user environment. In addition, the platform's real-time rendering and version control mechanism also significantly improve the design quality and accuracy.

关键词： Cloud computing Visualization Computational modeling Buildings Collaboration distributed databases Real-time systems Delays Synchronization Security

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High Performance Visualization for Astrophysics and Cosmology

High Performance Visualization for Astrophysics and Cosmolog...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Nicola Tuccari Eva Sciacca Fabio Vitello Iacopo Colonnelli Yolanda Becerra Enric Sosa Cintero Guillermo Marin Milan Jaros Lubomir Riha Petr Strakos Sebastian Trujillo-Gomez Emiliano Tramontana Robert Wissing Istituto Nazionale di Astrofisica Catania Italy Università di Torino Torino Italy Barcelona Supercomputing Center Barcelona Spain IT4Innovations National Supercomputing Center Ostrava Czech Republic Heidelberg Institute for Theoretical Studies Heidelberg Germany Università degli Studi di Catania Italy University of Oslo Institute of Theoretical Astrophysics Oslo Norway

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modern Astrophysics and Cosmology (A&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems used in A&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.

关键词： Adaptation models Analytical models Astrophysics Computational modeling Scalability Data visualization distributed databases Data models Reproducibility of results Real-time systems

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Fault-Tolerant Architectures for distributed Big Data Analytics

Fault-Tolerant Architectures for Distributed Big Data Analyt...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international Conference on

作者： Sunil Sudhakaran Samarth Shah Manas Kumar Mishra Vignesh Natarajan Jay Bhatt Om Goel Mahatma Gandhi University Kottayam Kerala India University at Albany Albany NY USA Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Arizona State University AZ United States Northeastem University Boston MA USA ABES Engineering College Ghaziabad U.P. India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

It has created challenges in the processing of data and analysis, which need to go beyond the traditional centralized architecture to solve the current data volume, speed, and types. To address these challenges, fault-tolerant architectures have become one of the keys to meeting the needs of distributed big data analytics. Failure is a normal part of systems; these architectures are meant to accommodate failures of individual components so that the entire system does not need to stop working or, worse, give false results. They make use of several nodes to operate and retain data in a distributed fashion, which supplies excessive scalability and fault tolerance. This means that even if some nodes fail, a fault-tolerant architecture can still provide redundancy by replicating data across many nodes, giving the system redundancy. Fault-tolerant architectures also utilize efficient data processing techniques like partitioning and data parallel processing, to achieve high performance. This allows real-time and near-real-time analysis of streaming data.

关键词： Scalability Fault tolerant systems Redundancy distributed databases Computer architecture Big Data Data processing Real-time systems Complexity theory Physical design

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PECS 2024 - Proceedings of the 2024 Workshop on Performance and Energy Efficiency in Concurrent and distributed systems, Part of: HPDC 2024 - 33rd international symposium on High-Performance parallel and distributed Computing

PECS 2024 - Proceedings of the 2024 Workshop on Performance ...

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2024 Workshop on Performance and Energy Efficiency in Concurrent and distributed systems, PECS 2024, Part of HPDC 2024 - 33rd international symposium on High-Performance parallel and distributed Computing

ISBN: (纸本)9798400706448

The proceedings contain 3 papers. The topics discussed include: mapping parallel matrix multiplication in GotoBLAS2 to the AMD versal ACAP for deep learning;exploring post quantum cryptography with quantum key distribution for sustainable mobile network architecture design;and energy efficiency: a lattice Boltzmann study.

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Proceedings - international symposium on Software Engineering for parallel and distributed systems, PDSE 1999

Proceedings - International Symposium on Software Engineerin...

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1999 international symposium on Software Engineering for parallel and distributed systems, PDSE 1999

ISBN: (纸本)0769501915

The proceedings contain 22 papers. The topics discussed include: Symphony: a Java-based composition and manipulation framework for distributed legacy resources;multithreaded dependence graphs for concurrent Java programs;state-based reproducible testing for CORBA applications;specifying and analyzing distributed object-oriented systems;channel dependence analysis for slicing Promela;deadlock analysis of synchronous message-passing programs;testing of concurrent programs based on message sequence charts;systematic debugging of parallel programs based on collective breakpoints;automated test sequence generation using sequencing constraints for concurrent programs;specification and modeling of flexible manufacturing systems using behaviors and Petri nets building blocks;evaluation of a methodology for the reverse engineering and parallelization of sequential code;component-based development and verification of safety critical software for a brake-by-wire system with synchronous software components;parallel object server as a data repository for CASE tools;web-based tool support for dynamic management of distribution and parallelism in integrating architecture design and performance evaluation;distributed airport simulation using CORBA and DIS;an architecture for a distributed system for product engineering lifecycle support;data interoperability via surrogate API libraries;and specification and simulation of a concurrent real-time system.

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Proceedings of the 1st international symposium on parallel Computing and distributed systems, PCDS 2024

Proceedings of the 1st International Symposium on Parallel C...

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1st international symposium on parallel Computing and distributed systems, PCDS 2024

ISBN: (纸本)9798350349658

The proceedings contain 35 papers. The topics discussed include: affordable HPC: leveraging small clusters for big data and graph computing;systematic literature review of VANET simulators: comparative analysis, technological advancements, and research challenges;parallel swarm propagation for neural networks;implementing censorship-resistant trusted email using blockchain technology;new parallel order maintenance data structure;an optimization method for national cryptography algorithm;interactive data visualization to optimize decision-making process;the influence of implementing enterprise resource planning (ERP), human capital management (HCM), and supply chain management (SCM) to enhance company’s performance effectiveness;and research on user identity authentication method based on edge computing.

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Mithril: A Scalable System for Deep GNN Training

Mithril: A Scalable System for Deep GNN Training

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IEEE symposium on High-Performance Computer Architecture

作者： Jingji Chen Zhuoming Chen Xuehai Qian Purdue University Carnegie Mellon University Tsinghua University

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Communication is a key bottleneck for distributed graph neural network (GNN) training. Existing GNN training systems fail to scale to deep GNNs because of the tremendous amount of inter-GPU communication. This paper proposes Mithril, a new approach that significantly scales the distributed full-graph deep GNN training. Being the first to use layer-level model parallelism for GNN training, Mithril partitions GNN layers among GPUs, each device performs the computation for a disjoint subset of consecutive GNN layers on the whole graph. Compared to graph parallelism with each GPU handling a graph partition, Mithril reduces the communication volume by a factor of the number of GNN layers to scale to deep models. Mithril overcomes the unique challenges for pipelined layer-level model parallelism on the whole graph by partitioning it into dependent chunks, breaking the dependencies with embedding speculation, and applying specific training techniques to ensure convergence. We also propose a hybrid approach by combining Mithril with graph parallelism to handle large graphs, achieve better computer resource utilization and ensure model convergence. We build a general GNN training system supporting all three parallelism settings. Extensive experiments show that Mithril reduces the perepoch communication volume by up to $22.89 \times$ (on average $6.78 \times$). It achieves a maximum training time speedup of $2.34 \times$ (on average $1.49 \times$) on a GPU cluster with a high-performance InfiniBand network. On another cluster with a commodity Ethernet, Mithril outperforms the baseline by up to $10.21 \times$ (on average $7.16 \times$). Mithril also achieves a comparable level of model accuracy and convergence speed compared to graph parallelism.

关键词： Training Performance evaluation Solid modeling Accuracy Computational modeling Graphics processing units parallel processing Graph neural networks Resource management Convergence

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SNTA 2024 - Proceedings of the 2024 7th international Workshop on systems and Network Telemetry and Analytics, Part of: HPDC 2024 - 33rd international symposium on High-Performance parallel and distributed Computing

SNTA 2024 - Proceedings of the 2024 7th International Worksh...

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7th international Workshop on systems and Network Telemetry and Analytics, SNTA 2024

ISBN: (纸本)9798400706486

The proceedings contain 5 papers. The topics discussed include: parallel and distributed frugal tracking of a quantile;defining the boundaries for endpoint congestion management in networks for high-performance computing;accelerating application bulk synchronous writes in HPC environments;flying base station channel capacity;and eGossip: optimizing resource utilization in gossip-based clusters through eBPF.

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