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.

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.

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.

P2P-Grid结构模型研究与设计

网格和P2P网络技术在当今分布式计算研究中有十分重要的意义和地位,这两种技术都是通过汇聚互联网中来自不同机构的数量庞大的资源,来满足用户之间的资源共享需求。但是它们在具体实现与应用中遇到一定困难。文中在分析了其各自的优点和不足的同时,比较了它们的相似性,提出了一种结合二者技术优势的新的分布式模型———P2P-Grid模型。它是由一些被称之为Super-Peer的小型的网格系统通过P2P技术互连构成。在充分比较该模型与银行运作模式的基础上,设计了整体结构模型,包括系统结构模型、功能模型和任务调度模型。

分布式CAD-Grid集成系统平台的设计与实现

从系统实施模式、体系结构和功能模块等方面讨论了构建分布式同步协同CAD-Grid系统的基本理论和方法,展现了一个新的基于网格技术的分布式协同CAD系统环境。利用软件代理实现网格计算中间件,解决了异构计算平台下多种并行编程环境的协同计算问题,为用户提供统一的服务接口。实例表明,该系统具有完全的跨平台性、可扩展性和开放性,足以支持人机环境下协同动态工作和资源共享。

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.

PARAMETRIC OPTIMIZING ANALYSIS OF UNSTEADY STRUCTURES AND VISUALIZATION OF MULTIDIMENSIONAL DATA

The paper presents an approach to fast approximate estimation of conditions for spacetime structures appearing in the flows.The approach is based on combination of optimization problem computation with methods of data visual presentation.The visual presentation methods are applied for analysis of multidimensional array containing discrete result data.Optimization problem solution is implemented by parallel computation in a multitask form.For some cases,the approach allows to obtain for control parameter of considered problem the sought-for approximate dependence on characteristic parameters in a quasi-analytical form.

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.

元电网MetaGrid:基于平行电网的新一代智能电网的体系与架构

随着新能源设备的大规模接入,大电网系统的脆弱性、开放性和不确定性显著上升,系统的管理与控制面临重大的挑战。构建基于平行系统与元宇宙思想的平行电网系统,形成虚实融合的智能大电网管理与控制理论方法,是解决上述问题的一种可行途径。介绍平行电网系统MetaGrid的基本体系架构,在此基础上分析平行电网系统的关键技术,并展望平行电网潜在的应用场景。基于300节点热稳案例,提供一个平行电网应用案例的原型。期望通过构建平行电网系统,对实际电网系统进行精准建模,借助大规模的计算实验和虚实闭环的平行执行,实现实际电网系统和人工电网系统的虚实互动,促进电力系统从依赖仿真的管控走向基于平行电网的智能管理与控制。

A distributed spatial computing prototype system in grid environment

Digital Earth has been a hot topic and research trend since it was proposed,and Digital China has drawn much attention in China.As a key technique to implement Digital China,grid is an excellent and promising concept to construct a dynamic,inter-domain and distributed computing environment.It is appropriate to process geographic information across dispersed computing resources in networks effectively and cooperatively.A distributed spatial computing prototype system is designed and implemented with the Globus Toolkit.Several important aspects are discussed in detail.The architecture is proposed according to the characteristics of grid firstly,and then the spatial resource query and access interfaces are designed for heterogeneous data sources.An open-up hierarchical architecture for resource discovery and management is represented to detect spatial and computing resources in grid.A standard spatial job management mechanism is implemented by grid service for convenient use.In addition,the control mechanism of spatial datasets access is developed based on GSI.The prototype system utilizes the Globus Toolkit to implement a common distributed spatial computing framework,and it reveals the spatial computing ability of grid to support Digital China.

Graph Computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid systems.In this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed.The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result.First,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data processing.Considering the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis.For each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system states.Furthermore,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson method.With the improved initial estimate,the sequential method can converge in fewer iterations.Consequently,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system.The performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

Multisite co-allocation scheduling algorithms for parallel jobs in computing grid environments

Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of multisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, compared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved.

Potential Off-Grid User Prediction System Based on Spark

With the increasingly fierce competition among communication operators,it is more and more important to make an accurate prediction of potential off grid users.To solve the above problem,it is inevitable to consider the effectiveness of learning algo rithms,the efficiency of data processing,and other factors.Therefore,in this paper,we,from the practical application point of view,propose a potential customer off grid predic tion system based on Spark,including data pre processing,feature selection,model build ing,and effective display.Furthermore,in the research of off grid system,we use the Spark parallel framework to improve the gcForest algorithm which is a novel decision tree ensemble approach.The new parallel gcForest algorithm can be used to solve practical problems,such as the off grid prediction problem.Experiments on two real world datasets demonstrate that the proposed prediction system can handle large scale data for the off grid user prediction problem and the proposed parallel gcForest can achieve satisfying per formance.

Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.

GridMol:基于网格的分子可视化建模应用系统

基于网格计算思想,提出集成远程高性能计算硬件、软件资源和计算前、后处理功能的高性能计算化学应用模型,并基于此模型开发了GridMol系统。GridMol采用Browser/Server结构设计,服务器端通过网格中间件提交高性能计算作业高效利用网格中的各种硬件、软件资源,浏览器端GridMol提供图形化的计算化学前、后处理功能,如蛋白质可视化和分子建模处理。GridMol基于Java和Java3DAPI设计实现,系统具有跨平台和网络运行等优点,目前GridMol已集成在中国国家网格(CNGrid)平台上。