DEM与地形分析的并行计算     

宋效东  刘学军  汤国安  王永君  田剑  窦万峰 《地理与地理信息科学》2012,28(4):1-7

总结了数字高程模型构建、特征提取等并行算法的研究进展,概述了不同并行算法的主要内容;探讨了DTA并行技术在海量地形数据可视化和高性能地学计算的应用,随着DEM的需求日益增大,高精度、高分辨率DEM产品及其附加服务也逐步产品化。最后,通过分析并行计算发展的关键问题,提出DTA并行技术的研究趋势及研究意义,合适的数据划分和结果融合策略、通用并行算法、容错机制和负载均衡策略的设计是今后研究的重要内容,尤其是如何在多种计算模式共同发展的背景下利用并行计算解决地学难题,从而得到更接近现实世界地理环境的模拟,并扩大数字地形分析的应用范围。  相似文献   

浅议基于像素工厂的ADS 80影像处理技术     

周晓敏  杨爱玲  孙丽梅  曹广强 《东北测绘》2012,(5):146-148,153

摄影测量进入数字时代以来,ADS80因其独特的优势逐步成为航摄影像获取的主流平台,对ADS80数据后续快速处理也成为目前数据处理软件研究的重点。本文介绍了基于像素工厂系统快速处理ADS80数据的技术流程和数据处理的主要技术环节,总结了像素工厂系统数据处理的优劣势。通过实际项目实践,验证了基于像素工厂系统处理ADS80数据的可靠性与高效性。  相似文献   

多作业管理方式在风能资源数值模拟中的应用          下载免费PDF全文

张小伟  李正泉  杨忠恩  姚益平  蔡菊珍 《应用气象学报》2010,21(6):747-753

通过对MM5和CALMET风能资源数值模拟耦合模式的计算流程分析,基于并行运算思想,设计了MM5和CALMET耦合模式模拟运算的多作业管理方式。在浙江省风能资源高分辨率数值模拟试验中,完成浙江区域1个月时间段的风能资源参数模拟运算,MM5和CALMET耦合模式在实施多作业管理方式前后,CALMET模式的运算时间由原来的1501.2 min缩短为149.5 min,运算时效提高了9倍;整个耦合模式的运算时间由原来的1709.9 min缩短为358.2 min,运算时效提高了4倍。数值模拟试验证实了多作业管理方式可在现有计算资源的基础上,大幅提高数值模式的运算时效,且随着数值模式模拟时间段的加长和模拟区域范围的扩大,多作业管理方式对数值模式运算功效的增强越加明显。  相似文献   

基于GPGPU的全波形并行分解算法     

王宗跃  马洪超  明洋 《遥感学报》2014,18(6):1217-1222

针对EM(Expectation Maximization)波形分解算法具有多次迭代和大量乘、除、累加等高密集运算的特点,提出一套将EM算法在通用计算图形处理器GPGPU上并行化的方案。针对通用并行计算架构CUDA的存储层次特点,设计总体的并行方案,充分挖掘共享存储器、纹理存储器的高速访存的潜能;根据波形采样值采用字节存储的特征,利用波形采样值的直方图求取中位数,从而降低求噪音阈值的计算复杂度;最后,采用求和规约的并行策略提高EM算法迭代过程中大量累加的计算效率。实验结果表明,当设置合理的并行参数、EM迭代次数大于16次、数据量大于64 M时,与单核CPU处理相比,GPU的加速比达到了8,能够显著地提高全波形分解的效率。  相似文献   

CYS-300型全液压地热水井钻机的研制          下载免费PDF全文

刘志  彭儒金  朱慧  陈敏  邱华 《探矿工程》2019,46(7):83-88

本文介绍了CYS300型全液压地热水井钻机的性能参数、关键机械部件结构及液压系统的设计。钻机选用齿轮泵作为液压系统动力元件,配合液压多路阀实现对动力头双液压马达和给进油缸等执行元件液压油的合流,通过液控二速阀实现动力头双液压马达的串并联工作,输出4种转速和扭矩,在满足钻机性能参数设计要求的同时,简化液压系统的设计,减少液压系统能量损耗,降低维护和使用成本。钻机采用油缸三链条倍速机构实现动力头的给进提升,具有结构简单、性能可靠、抗冲击能力强、提升平稳等优点。现场工程施工应用表明：CYS300型全液压地热水井钻机性能稳定可靠,工艺和复杂地层适用能力强,操作安全舒适,钻进效率高。  相似文献   

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities     

Jayakrishnan Ajayakumar  Eric Shook 《International journal of geographical information science》2020,34(9):1683-1707

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.  相似文献   

矢量瓦片并行构建与分布式存储模型研究     

聂沛  陈广胜  景维鹏 《地球信息科学学报》2020,22(7):1487-1496

矢量瓦片体积小、生成效率高、支持动态交互,较传统栅格瓦片有诸多优势,是下一代互联网地图服务研究的重点。为了解决当前矢量瓦片研究中处理速度慢,扩展性差等问题,本文利用并行计算框架Spark进行矢量瓦片快速构建,通过自定义转换函数,将原始矢量数据GeoJson转换成mvt瓦片集;对于生成的矢量瓦片集,本文基于分布式内存文件系统Alluxio设计一个瓦片存储模型-VectorTileStore,模型以键值对进行数据存储,瓦片元数据占据前八个键值对,单个瓦片占据一个键值对,在数据写入的同时,基于键构建一个哈希索引,用于快速访问,模型兼容海量瓦片的组织存储,具有很强的扩展性。通过实验结果表明,本文提出的矢量瓦片并行构建算法较单机构建算法运行时间平均减少49.6%,分布式存储模型VectorTileStore较传统方案更适合海量矢量瓦片存储,存取时间效率更高。  相似文献   

GIS典型几何计算算法的并行化及优化研究     

赵银利  范俊甫  王崇倡 《测绘与空间地理信息》2017,40(7)

空间数据规模的快速增长对传统矢量数据分析方法提出了更高的计算效率和处理规模要求。随着计算机硬件和软件技术的进步,并行计算为提高GIS中典型几何计算算法的计算效率、扩大问题处理规模提供了有效手段。本文在Visual Studio 2010中,使用标准C++编程语言,基于GDAL(Geospatial Data Abstraction Library)库实现空间数据的读写操作,针对线简化算法的并行化问题,在高性能计算环境下对并行任务调度策略、并行计算粒度、数据分解方法等多个核心内容开展研究。在完成相关串行算法的基础上,实现了该算法的并行化和优化设计,为相关的矢量数据空间分析方法的多核并行优化提供了思路和参考。  相似文献   

遥感影像并行分割的无缝拼接算法     

丁翠  杨云涛  张建 《测绘科学》2017,42(4)

针对影像数据分块并行处理造成的接边问题,该文基于构建缓冲区的方法提出了一种新的拼接算法。该算法只在每一个数据分块的右侧和下侧构建缓冲区,消除拼接线的原则是保证分割边界的准确性和拓扑关系的正确性。数据分块完成后采用并行的策略对第一个块进行分割,然后将每一块的分割结果与相邻的块进行拼接,在缓冲区内按一定规则判断各块分割对象是否保留并对拓扑关系进行检查,拼接完各块后并行输出对象矢量化后的结果。通过实验验证,该算法不仅保证了分割边界的准确性,而且能够处理大影像,提高了工作效率。  相似文献   

A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology   总被引：1，自引：0，他引：1  

Chang Xia  Anqi Zhang  Wenting Zhang 《International journal of geographical information science》2018,32(2):399-424

Cellular automata (CA) models can simulate complex urban systems through simple rules and have become important tools for studying the spatio-temporal evolution of urban land use. However, the multiple and large-volume data layers, massive geospatial processing and complicated algorithms for automatic calibration in the urban CA models require a high level of computational capability. Unfortunately, the limited performance of sequential computation on a single computing unit (i.e. a central processing unit (CPU) or a graphics processing unit (GPU)) and the high cost of parallel design and programming make it difficult to establish a high-performance urban CA model. As a result of its powerful computational ability and scalability, the vectorization paradigm is becoming increasingly important and has received wide attention with regard to this kind of computational problem. This paper presents a high-performance CA model using vectorization and parallel computing technology for the computation-intensive and data-intensive geospatial processing in urban simulation. To transfer the original algorithm to a vectorized algorithm, we define the neighborhood set of the cell space and improve the operation paradigm of neighborhood computation, transition probability calculation, and cell state transition. The experiments undertaken in this study demonstrate that the vectorized algorithm can greatly reduce the computation time, especially in the environment of a vector programming language, and it is possible to parallelize the algorithm as the data volume increases. The execution time for the simulation of 5-m resolution and 3 × 3 neighborhood decreased from 38,220.43 s to 803.36 s with the vectorized algorithm and was further shortened to 476.54 s by dividing the domain into four computing units. The experiments also indicated that the computational efficiency of the vectorized algorithm is closely related to the neighborhood size and configuration, as well as the shape of the research domain. We can conclude that the combination of vectorization and parallel computing technology can provide scalable solutions to significantly improve the applicability of urban CA.  相似文献