Study on Visualization of Virtual City Model Based on Internet

With the rapid development of computer graphics, distributed-computing and Internet, it is possible to achieve Internet-based virtual city. This paper dwells on the method of the terrain and its feature modeling and complex entity modeling in the virtual city. Then, discusses the method for Internet-based virtual city 3D visualization and the design of the Browser/Server architecture of the system of virtual city in the network environment. Finally, Java and Java 3D are used to show an experiment example, and the related conclusion about Internet-based virtual city 3D displaying and the client-side interactive operation is given.

Enabling Digital Earth simulation models using cloud computing or grid computing-two approaches supporting high-performance GIS simulation frameworks

Geospatial simulation models can help us understand the dynamic aspects of Digital Earth.To implement high-performance simulation models for complex geospatial problems,grid computing and cloud computing are two promising computational frameworks.This research compares the benefits and drawbacks of both in Web-based frameworks by testing a parallel Geographic Information System(GIS)simulation model(Schelling’s residential segregation model).The parallel GIS simulation model was tested on XSEDE(a representative grid computing platform)and Amazon EC2(a representative cloud computing platform).The test results demonstrate that cloud computing platforms can provide almost the same parallel computing capability as high-end grid computing frameworks.However,cloud computing resources are more accessible to individual scientists,easier to request and set up,and have more scalable software architecture for on-demand and dedicated Web services.These advantages may attract more geospatial scientists to utilize cloud computing for the development of Digital Earth simulation models in the future.

DCCS:A General-Purpose Distributed Cryptographic Computing System

Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated.

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.

G-Phenomena as a Base of Scalable Distributed Computing—G-Phenomena in Moore’s Law

Today we witness the exponential growth of scientific research. This fast growth is possible thanks to the rapid development of computing systems since its first days in 1947 and the invention of transistor till the present days with high performance and scalable distributed computing systems. This fast growth of computing systems was first observed by Gordon E. Moore in 1965 and postulated as Moore’s Law. For the development of the scalable distributed computing systems, the year 2000 was a very special year. The first GHz speed processor, GB size memory and GB/s data transmission through network were achieved. Interestingly, in the same year the usable Grid computing systems emerged, which gave a strong impulse to a rapid development of distributed computing systems. This paper recognizes these facts that occurred in the year 2000, as the G-phenomena, a millennium cornerstone for the rapid development of scalable distributed systems evolved around the Grid and Cloud computing paradigms.

Parallel Computation of Fourier Transform on Distributed Memory Computer System

Multicomputer systems(distributed memory computer systems) are becoming more and more popular and will be wildly used in scientific researches. In this paper, we present a parallel algorithm of Fourier Transform of a vector of complex numbers on multicomputer system and give its computing times and its speedup in parallel environment supported by EXPRESS system on the multicomputer system which consists of four SGI workstations. Our analysis shows that the results is ideal and this scheme is suitable to multicomputer systems.

Grid Service Framework: Supporting Multi-Models Parallel Grid Programming

Web service is a grid computing technology that promises greater ease-of-use and interoperability than previous distributed computing technologies. This paper proposed Group Service Framework, a grid computing platform based on Microsoft. NET that use web service to: (1) locate and harness volunteer computing resources for different applications, and (2) support multi-models such as Master/Slave, Divide and Conquer, Phase Parallel and so forth parallel programming paradigms in Grid environment, (3) allocate data and balance load dynamically and transparently for grid computing application. The Grid Service Framework based on Microsoft. NET was used to implement several simple parallel computing applications. The results show that the proposed Group Service Framework is suitable for generic parallel numerical computing.

Edge Intelligence with Distributed Processing of DNNs:A Survey

Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also booming.As a result,the intolerable long time for models’training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually.Moreover,devices stay idle in the scenario of edge computing(EC),which presents a waste of resources since they can share the pressure of the busy devices but they do not.To address the problem,the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices,which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing.Compared with existing papers,this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing.Considering the practicalities,commonly used lightweight models in a distributed system are introduced as well.As the key technique,the parallel strategy will be described in detail.Then some typical applications of distributed processing will be analyzed.Finally,the challenges of distributed processing with edge computing will be described.

并行特征提取和渐进特征融合的计算机主板装配缺陷检测

针对计算机主板装配缺陷检测中的元器件位置分布复杂、缺陷目标不显著及多尺度等问题,本文提出了一种并行特征提取和互交叉渐进特征融合的端到端的缺陷检测算法。首先,结合部分卷积和视觉Transformer提出了一种并行残差特征提取网络,利用部分卷积的低计算复杂度的优势提取局部特征,同时利用视觉Transformer的长距离建模能力扩大模型的感受野,增强网络的特征提取能力。其次,引入注意力机制和特征渐进融合机制,提出了一种多尺度注意力互交叉的渐进特征融合网络,增强检测模型的特征融合能力。在公开数据集上的实验结果表明,该算法的平均精度均值(mAP)达到了94.63%,相较于基线模型YOLOv5提升了4.62%,并优于其他几种先进模型,检测速度达到了25 FPS。实现了较好的检测精度与速度的平衡,为实际工业环境下计算机主板表面装配缺陷检测自动化和智能化的实现提供了一种快速、有效的方法。

基于正序瞬时功率算法的宽频振荡检测技术

现代电力系统表现出含高比例可再生能源和高比例电力电子设备的“双高”特征,电力系统中不同频率的谐波、间谐波与工频量相互作用,将可能导致宽频振荡,威胁电力系统安全稳定运行。针对无法在三相不平衡状态下实现宽频振荡准确检测的问题,提出了基于FPGA(现场可编程门阵列)和正序瞬时功率算法的宽频振荡检测技术。利用三相正序瞬时功率能滤除三相不平衡分量的原理,确保宽频振荡的检测结果不受三相不平衡的影响。利用FPGA并行计算的特性,大幅提升了宽频振荡检测算法的性能。测试结果表明,该技术能够在发生宽频振荡时准确检测出振荡分量,解决了现有技术存在误判和并行计算能力不足的问题。

基于GPU加速的分布式水文模型并行计算性能

针对具有物理机制的分布式水文模型对大流域、长序列模拟计算时间长、模拟速度慢的问题,引入基于GPU的并行计算技术,实现分布式水文模型WEP-L(water and energy transfer processes in large river basins)产流过程的并行化。选择鄱阳湖流域为实验区,采用计算能力为8.6的NVIDIA RTX A4000对算法性能进行测试。研究表明:提出的基于GPU的分布式水文模型并行算法具有良好的加速效果,当线程总数越接近划分的子流域个数(计算任务量)时,并行性能越好,在实验流域WEP-L模型子流域单元为8712个时,加速比最大达到2.5左右;随着计算任务量的增加,加速比逐渐增大,当实验流域WEP-L模型子流域单元增加到24897个时,加速比能达到3.5,表明GPU并行算法在大尺度流域分布式水文模型计算中具有良好的发展潜力。

基于Rucio的高能物理网格数据管理的研究和应用

【目的】近年来,高能物理网格数据规模和用户需求产生重大变革,需要研究和应用新兴网格数据管理技术以应对需求变化。【方法】基于新型网格数据管理系统Rucio,利用其高伸缩性、模块化和可扩展性的软件特点,发挥其分布式数据恢复、自适应的数据复制的功能特性,为多个国内主导的国际合作实验设计了面向实验需求的网格数据管理解决方案。【结果】实现了分布式数据统一命名、数据增删改查等基础管理功能、多站点数据副本管理、原始数据分发管理、实验软件数据管理接口嵌入等多种功能,并先后进入了测试应用阶段。【结论】本研究为未来国内主导的国际合作的高能物理实验网格数据管理方案的设计和开发进行了探索和尝试,希望进一步在网格架构上开展深入研究,实现国内实验通用标准的网格数据管理方案。

用于VSLAM系统的CNN在FPGA平台上的加速

为实现视觉同步定位与建图系统中卷积神经网络在FPGA上的加速,基于SuperPoint模型设计一种低功耗高效CNN加速器及相应的SoC系统。采用循环分块、数据复用、计算单元展开和双缓冲策略充分利用加速器的片上资源;为提高突发传输效率,预先对权重参数重排;提出Pack模块和Unpack模块,设计多通道数据传输,用于提高传输带宽。在Ultra96-V2 FPGA平台上部署整个SoC系统,在仅3 W左右的功耗下实现25.63 GOPS的吞吐量,其BRAM效率、DSP效率、性能密度和功耗效率相比之前的文献有明显优势。

面向结构化篇级科技文献数据治理的高性能分布式计算框架研究

[研究目的]为解决MapReduce、Spark等主流分布式计算框架存在的研发周期长、技术门槛高等问题,提出了一种高灵活、低门槛的高性能计算框架ArticleCF。[研究方法]ArticleCF框架吸收了主流分布式技术的优点,同时深度结合科技文献数据治理的特性,设计了Master/Slave的软件架构,在功能上针对科技文献数据特点进行多个维度的设计,重点设计了分布式任务分发策略、并行计算策略以及故障转移机制。[研究结论]通过21个指标将ArticleCF与MapReduce、Spark、Storm进行对比实验,有效验证所提方法的可行性、有效性,ArticleCF能够满足海量结构化科技文献数据的多样化处理需求。

基于云计算的优化配网调度执行系统研究

为了优化配网调度执行工作,对配网调度进行了研究。设计了基于云计算的优化配网调度执行系统。在硬件设计中采用分布式结构执行配网调度。在系统软件部分,使用云计算技术,设计了主动配网云边协同计算体系。该体系实现了配电调度边缘计算节点的远程操作,提高了配网调度执行优化的能力。采用客户端/服务端(C/S)、浏览器/服务器(B/S)混合架构,实现了配网调度的人机可视化执行。利用两点估计法以提高配网调度模型的优化计算能力,进而实现配网调度的控制,提升了配网调度运行的稳定性与经济性。试验结果表明,在对配网调度执行的可靠性进行测试时,该系统的可靠性达到了96%。该系统可使配网始终处于安全、可靠、优质、经济、高效的运行状态,为配网调度执行提供了一种可行的方案。

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Fast Cycle Structure Detection for Power Grids Based on Graph Computing

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid networks.However,detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive.In addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real time.In traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible task.Graph computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state estimation.In this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing platform.Two cases are used to test its performance in an actual power grid topology scenario.The results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.