考虑不确定性的地震灾害应急救援设备并行优化设计   总被引：1，自引：0，他引：1       下载免费PDF全文

刘航 《地震工程学报》2018,40(5):1118-1123

由于地震灾害的不确定性，使得应急救援设备运行速率及使用效率均受到影响，需要进行并行优化处理。对此，提出基于双向并行计算的地震灾害应急救援设备优化方法。以地震灾区灾情等级评估结果为基础，将地震等级及应急救援设备，设备及设备之间的关系进行标准化处理，转化为求解最优解问题；在考虑不确定性的情况下，通过通信时间与救援设备需求进行双向并行处理，优化地震灾害应急救援设备。实验结果表明，采用改进方法进行地震灾害应急救援设备并行优化，能够对地震灾害应急救援设备需求量进行准确预测，提高应急救援设备的运行速率，缩短通信时间，提高应急救援设备的使用效率，具有一定的优势。  相似文献   

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

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

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.  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   

Parallelization of the flow-path network model using a particle-set strategy     

Fangli Zhang 《International journal of geographical information science》2019,33(10):1984-2010

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).  相似文献   

基于GPU加速的Boussinesq类波浪传播变形数值模型     

孙家文  朱桐  房克照  刘忠波 《海洋工程》2020,38(2):111-119

Boussinesq波浪模型是一类相位解析模型,在时域内求解需要较高的空间和时间分辨率以保证计算精度。为提高计算效率,有必要针对该类模型开展并行算法的研究。与传统的中央处理器(CPU)相比,图形处理器(GPU)有大量的运算器,可显著提高计算效率。基于统一计算设备架构CUDA C语言和图形处理器,实现了Boussinesq模型的并行运算。将本模型的计算结果同CPU数值模拟结果和解析解相比较,发现得到的结果基本一致。同时也比较了CPU端与GPU端的计算效率,结果表明,GPU数值模型的计算效率有明显提升,并且伴随数值网格的增多,提升效果更为明显。  相似文献   

基于并行计算的海洋平台谱分析疲劳筛选技术研究     

李洛东  刘成名  梁园华 《海洋工程》2018,36(5):56-64

为了得到海洋平台疲劳失效风险最大的焊接区域,提出了以有限元网格单元应力为基础的谱分析筛选方法,并开发了程序。程序采用并行计算架构,单次完成数万个单元的累积损伤度/疲劳寿命计算,并通过有限元软件以云图方式显示疲劳寿命。采用该程序计算双浮筒半潜式平台的疲劳寿命。结果表明,该型平台疲劳失效风险较高的区域为立柱与上壳体下浮体连接对角线转角区域、浮筒中纵舱壁与立柱连接区域。并行计算方法的引入,大大缩减疲劳损伤度计算的分析时间,采用22核心的CPU,时间缩短为原来的1/18.5,17小时完成全平台外壳单元谱分析计算。  相似文献   

多视密集匹配并行传播GPU-PatchMatch算法     

邓非  颜青松  肖腾 《测绘学报》2020,49(2):181-190

针对多视密集匹配的效率较低的问题,提出了GPU-PatchMatch多视密集匹配算法。该算法使用GPU提高PatchMatch的计算效率;同时充分利用稀疏场景信息,对深度信息进行规则初始化;为提高传播效率,使用了金字塔红黑板并行传播深度信息。最后在DTU、Strecha和Vaihigen数据集上进行了试验,并与常用的多视密集匹配算法进行对比。试验结果表明,本文算法在重建效率上有较大提高,与CPU算法(PMVS、MVE、OpenMVS)相比有7倍以上提升,与GPU算法相比也有2.5倍以上提升,表明本文算法的有效性。  相似文献   

针铁矿对湖泊草、藻来源可溶有机质的非均质吸附     

章奇  居琪  李健欣  曹驰程  江和龙  张晖 《湖泊科学》2020,32(4):1041-1049

为探究富营养化湖泊中自生源可溶有机质(DOM)在泥水界面的吸附行为,以马来眼子菜(Potamogeton malaianus)和铜绿微囊藻(Microcystis aeruginosa)释放的DOM为代表,考察针铁矿对草、藻源DOM中不同组分的吸附特征.三维荧光平行因子分析表明类富里酸组分C1和类胡敏酸组分C4的含量很低,而类蛋白物质(类酪氨酸组分C2和类色氨酸组分C3)分别占草、藻源DOM荧光组成的70%和93%. 2种DOM均可被针铁矿吸附,吸附过程符合伪一级动力学.通过等温线拟合发现针铁矿吸附藻源DOM的非线性更强,饱和吸附量(23.77 mg/g)高于草源DOM(19.10 mg/g).特别地,类蛋白组分呈现非线性吸附,而类腐殖物质的吸附近似线性,且针铁矿对于DOM各荧光组分的吸附量顺序为:C3C2 C4 C1,此非均质吸附特征与DOM组分的初始含量、分子大小、芳香性及有效吸附位点有关.红外光谱证实氨基、羧基和羟基是吸附过程中的重要官能团.因此,草、藻源DOM显著改变了针铁矿表面的有机物质组成,影响湖泊沉积物的生物地球化学行为.  相似文献   

利用MPI实现多幅点云ICP并行配准     

崔家武  周波阳  张兴福  柴向浩  樊树春 《测绘通报》2021,(3):87-90

迭代最近点算法（ICP）是一种用于点云精确配准的经典算法。针对多幅点云进行ICP配准存在耗时多、效率低的问题,本文利用消息传递接口MPI对多幅点云进行分批并行配准。首先并行求解相邻两幅点云的相邻变换矩阵,然后计算每幅点云在当前批次的局部变换矩阵,最后获得每幅点云的全局变换矩阵。本文以DELL PowerEdge R730服务器为计算平台,对空间点总规模达四千多万的65幅点云进行了分批并行配准。试验结果表明：利用MPI对多幅点云进行分批处理可显著加快配准速度,最优进程数为计算机的核数时,加速比为5.3。  相似文献   

基于Web的地下管网溯源分析算法研究     

董万虎 《地理空间信息》2021,19(4):71-72,77

利用.NetCore的并行运算机制,实现基于Web的高性能地下管网的溯源分析功能。实践证明当某点管网数据监测出现异常时,能够快速分析所有可能流经该点源头管网信息,并在地图上动态展示分析结果,达到快速定位查询管网源头的区域管理目的。  相似文献