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.

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.

基于远近视角驾驶员模型人机合作转向PDC/H_∞控制策略

基于远近视角驾驶员模型获得了非线性车路和人车路闭环T-S模型,运用状态反馈γ-次优H∞范数和线性矩阵不等式约束得到了反馈增益矩阵。应用模糊并行分布补偿控制设计了车路和人车路闭环T-S模型全局控制器。CarSim/Simulink仿真结果表明,基于人车路闭环模型的人机合作转向控制的车道保持能力和跨道时间均优于车路模型,从而减少了车道偏离的风险。通过合作转向评价准则得出的人车路闭环PDC/H_∞控制器的人机合作程度较高。

Parallel Implementation of Global Illumination Using PVM

In this paper an attempt of employing network resources to solve a complex and time-consuming problem is presented. The global illumination problem is selected as the study objective. An improved density estimation algorithm is first developed, in which the more inherent concurrency is explored. Then its parallel implementation by using a PVM mechanism and the running performance analysis are provided. The analysis results show the expected speed-up obtained and demonstrate that the PVM has good application prospects for parallel computation in a distributed network.

Parallel Extraction of Marine Targets Applying OIDA Architecture

Computing resources are one of the key factors restricting the extraction of marine targets by using deep learning.In order to increase computing speed and shorten the computing time,parallel distributed architecture is adopted to extract marine targets.The advantages of two distributed architectures,Parameter Server and Ring-allreduce architecture,are combined to design a parallel distributed architecture suitable for deep learning–Optimal Interleaved Distributed Architecture(OIDA).Three marine target extraction methods including OTD_StErf,OTD_Loglogistic and OTD_Sgmloglog are used to test OIDA,and a total of 18 experiments in 3categories are carried out.The results show that OIDA architecture can meet the timeliness requirements of marine target extraction.The average speed of target parallel extraction with single-machine 8-core CPU is 5.75 times faster than that of single-machine single-core CPU,and the average speed with 5-machine 40-core CPU is 20.75 times faster.

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.

Parallel Implementation of Yee Algorithm 2D Using MPI

The examination work clarifies a practical, superior registering stage for the parallel usage of the FDTD calculation on PC bunches utilizing the message-passing interface (MPI) library, which is a neighborhood framework comprising of various interconnected (PCs), and is now generally utilized for parallel figuring. In this paper, we describe the essential elements of a parallel algorithm for the FDTD method using the MPI (message passing interface). At present, the Internet of Things (IoT) has attracted more and more researchers’ attention. Parallel FDTD method is applied to analyze the electromagnetic problems of the electrically large targets. This paper presents the concept of “the optimum virtual topology” for MPI based parallel FDTD. Parallel FDTD method is applied to analyze the electromagnetic problems of the electrically large targets on super computer.

Spatial Management of Distributed Social Systems

The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be represented as dynamic,self-evolving and distributed social networks.The approach allows us to find important solutions on a holistic level by spatial navigation and parallel pattern matching of social networks with active self-propagating scenarios represented in a special recursive language.This approach effectively hides inside the distributed and networked language implementation traditional system management routines,often providing hundreds of times shorter and simpler high-level solution code.The paper highlights the demands to efficient simulation of social systems,briefs the technology used,and provides some programming examples for solutions of practical problems.

The Design and Implementation of a Distributed Data Acquisition、Monitoring & Processing System (DDAMAP)

This report presents the design and implementation of a Distributed Data Acquisition、 Monitoring and Processing System (DDAMAP)。It is assumed that operations of a factory are organized into two-levels: client machines at plant-level collect real-time raw data from sensors and measurement instrumentations and transfer them to a central processor over the Ethernets, and the central processor handles tasks of real-time data processing and monitoring. This system utilizes the computation power of Intel T2300 dual-core processor and parallel computations supported by multi-threading techniques. Our experiments show that these techniques can significantly improve the system performance and are viable solutions to real-time high-speed data processing.

基于FPGA的新能源低压直流配电系统暂态实时仿真研究

对新能源低压直流配电系统开展暂态实时仿真研究对优化其运行控制具有重要作用。由于现场可编程门阵列(FPGA)内部集成了大量具有不同功能的电路,FPGA正成为电力系统暂态实时仿真领域主要的计算载体之一。该文面向新能源低压直流配电系统的暂态实时仿真需求,开发了一种基于FPGA的包含小型分布式风力发电、光伏发电以及蓄电池储能单元的新能源低压直流配电系统暂态实时仿真器。首先,研究构建了分布式发电单元和典型控制回路的计算模块,利用FPGA的并行计算特性并结合“算法-结构-有效匹配(AAA)”理念建立了底层模块串并联混合求解结构;然后,在节点分析法的框架下,建立了一种结合矩阵LDU分解和有向无环图(DAG)的电气系统节点电导矩阵并行求解方法;最后,在建立电气系统与控制系统并行求解架构的基础上,开发了一种基于FPGA的新能源低压直流配电系统暂态实时仿真器,通过将其仿真结果与PSCAD/EMTDC离线仿真平台的计算结果进行对比,验证了所开发暂态实时仿真器的有效性和准确性。

高分三号数据分布式自适应负载均衡并行转换算法研究

针对PolSARpro软件自带高分三号数据转换模块无法自适应分布式差异化硬件环境问题,提出了一种按照该软件的数据格式要求进行分布式自适应负载均衡并行转换算法,将高分三号多极化数据快速、精确地进行定标与格式转换,对不同硬件配置环境自适应,能够抗数据偏斜,并且通过KingMap V10.5平台实现了算法并在实际数据中进行测试,试验结果表明性能提升了约12.7倍,验证了算法的可行性、高效性和正确性。

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.

基于UREP的有源中压配电网电磁暂态建模方法

在能源转型的影响日益扩大的背景下,高比例的可再生能源和电力电子设备的接入给新型电力系统的研究带来了难题,传统电力系统开始朝着电网、电源、负荷与储能的综合动态平衡改变。因此,新型电力系统的模型仿真需要进行更加精确、快速的动态模拟来验证。而大规模的新型电力系统电磁暂态仿真目前缺乏高效的仿真方案及有效的仿真工具,基于国产实时仿真器UREP、以中压配电网的Simulink模型为例提出了“全拓扑、全电磁暂态、全配置”概念,运用理想变压器模型(ideal transformer model,ITM)将系统模型进行降阶处理,采取多核并行计算技术进行分核处理,仿真结果显示所提建模方法不仅提高了新型电力系统模型的仿真精确度,还极大地缩短了模型仿真时间,从而验证了大规模新型电力系统仿真的可行性。

一种基于HDFS的分布式文件系统MPIFS

传统的MPI(Message Passing Interface)计算特点是数据向计算迁移,对于数据量庞大的计算任务具有先天的不足。文中提出一种支持MPI的分布式文件系统MPIFS的架构及实现。该文件系统基于HDFS(Hadoop Distributed File System),使得MPI在MPIFS上能同时支持计算密集型和数据密集型计算,设置两个类型的批处理词频统计实验,所需数据都分布式存储在MPIFS分布式文件系统中,通过调用系统提供的统一数据接口实现数据访问。1个计算节点在本地计算大小为m的文件,n个计算节点分布式并行计算大小为n×m的文件,两者计算时间相同,MPIFS中文件总量不变,计算节点数量减少,计算时间t变长,可得出MPIFS文件系统架构可行,能够支持MPI实现计算向数据迁移的并行计算。

一种基于Yarn云平台的基因启发式多序列比对算法

提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划分错误率较高的长序列,得到多个较短的基因片段。对不同片段实施定位,将其中的变长种子生成,进行骨架构建和孔隙填补,可以实现基因启发式多序列比对。结果表明,设计的算法在不同数据集下处理时间缩短,多序列比对SP(Sum of Pairs)的分值较高,实验验证了该多序列比对方法具有很好的应用价值。

Parallel Computing Based Solution for Reliability-constrained Distribution Network Planning

The main goal of distribution network(DN)expansion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcDNP)problem can be cast as an instance of mixed-integer linear programming(MILP)which involves ultra-heavy computation burden especially for large-scale DNs.In this paper,we propose a parallel computing based solution method for the RcDNP problem.The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination.Then,a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems.The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem.Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition.Under mild conditions,the optimality and convergence of the proposed method are verified.Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-worldproblems.