CYS-300型全液压地热水井钻机的研制

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。     

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

基于Python的“地理处理”并行方案

在传统方式下,ArcGIS地理处理工具的执行过程不能充分利用高性能多核计算机的全部运算能力处理日益增加的地理数据,导致数据处理效率低下。本文在分析地理处理工具特点的基础上,充分利用Python语言的并行编程特点,构建具有通用性的地理处理任务并行运行解决方案。结合ArcGIS软件自身的特质,有效解决了并行运行所带来的数据竞争、数据共享与进程通讯等问题,达到了一定硬件环境条件下ArcGIS工具执行效率最大化的目的。通过典型地理处理任务中不同运行方式效率的对比测试与分析,证明了并行运行的有效性。     

三江并流区地貌与环境演化研究

滇西北三江并流区地理环境演化既受青藏高原隆升的影响,同时又具有自己形成演化的特点,还以纵向岭谷地貌、水系形成演化等影响本区及周边的生态环境.三江并流区经历了古新世中新世地表夷平准平原阶段,中新世末第四纪初的岭盆谷形成发展阶段(高原发育期),更新世晚期以来的现代水系峡谷、纵向岭谷和垂向气候形成等几个时期的发展,形成了现今西南纵向岭谷区的主要组成部分,导致了生态环境的通道阻隔效应,形成了独具特色的自然地理区域.     

A parallel algorithm for coverage optimization on multi-core architectures

Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.     

滑坡变形预测灰色神经网络耦合模型的构建及适用性分析

结合灰色模型和神经网络的数据处理特点,提出串联、并联和混联式3种结构的灰色神经网络滑坡变形预测模型。串联式将滑坡变形位移时序分解为趋势项和随机项,采用灰色模型提取滑坡位移时序趋势,利用神经网络逼近随机波动;并联式以灰色模型和神经网络分别对滑坡预测,采用智能非线性组合,按照预测目标精度动态调整权重,从而获取最终组合预测结果;混联式通过增加灰白化层及灰模型群,对神经网络拓扑结构进行优化,达到弱化滑坡原始监测数据随机性、提高预测模型稳健性的目的。将3种模型应用于古树屋滑坡变形预测,并对其适用性进行讨论。结果表明,3种结构的灰色神经网络耦合模型均提高了预测精度,适用于复杂状况下滑坡体的变形预测。     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

曙光4000A上三维叠前深度偏移并行计算的应用设计

波动方程叠前深度偏移在地震勘探成像处理方面起着不可替代的作用。随着高性能大规模并行计算机技术的发展,波动方程叠前深度偏移计算在地震勘探中的应用有了很大进步。在波动方程叠前深度偏移处理中,庞大的数据规模与海量计算对计算性能提出了很高的要求。曙光4000A超级计算机系统是我国目前峰值速度最快的商用超级计算机系统,无论是硬件平台建设还是应用软件的配置方面,都具有良好的应用性能。基于该系统设计的三维波动方程叠前深度偏移(炮域)PSDM软件,采用动态负载平衡并行计算模式,具有较高的计算效率,高度的可扩展性和可靠性。