共查询到20条相似文献，搜索用时 125 毫秒
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并行计算技术可以大幅度提高数据处理效率,快速响应用户需求。随着地理空间数据量增大、复杂性增加、时效性增强,采用并行计算技术和处理架构来进行地理空间信息大数据的处理是必由之路。本文研究了各种高性能计算以及互联网所采用的并行计算技术,分析了地理空间数据的特点和并行处理的难点,提出了GIS对并行计算的支持技术路线,探讨了云计算环境中部署并行系统的方法和策略,实现了SMPP并行架构,并基于该系统对并行处理的效能进行了研究和分析。最后,展望了并行GIS技术发展的前景和演进路线。 相似文献
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《测绘科学技术学报》2013,(5)
构建了多视匹配过程的总计算量模型,根据模拟参数赋值结果,分析得到了其中的密集计算任务,探讨了其GPU并行加速的必要性;针对单立体影像匹配技术细粒度GPU并行计算方案的不足,研究并设计了一种多视匹配密集计算任务的GPU粗粒度并行计算方案;利用专业级的GPU并行计算平台,对GPU粗粒度并行计算方案进行了实验验证,结果表明,该方案对于多视匹配过程中密集计算任务的并行加速效果十分显著。 相似文献
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针对现有侵蚀学坡长串行算法在处理大区域海量数据时计算能力的不足,基于消息传递接口(MPI)并行化工具,提出了一种格网DEM的侵蚀学坡长并行计算方法,采用缓冲区更新计算策略,解决了并行计算过程中的数据依赖性问题。选取陕北黄土高原的两组不同分辨率的DEM数据对并行算法进行了测试,结果表明,提出的并行计算方法可以有效降低侵蚀学坡长的计算时间,并取得了较好的并行效率。 相似文献
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多核处理器已成为当前通用计算机体系架构的主流,相应的多核并行计算技术及其应用引起了越来越多的重视,而传统的GNSS数据处理程序都是针对单处理器体系架构编写的。本文对当前多核环境下多时段或者多测站的GNSS数据处理所涉及的计算密集型任务并行算法进行研究,分析了GNSS数据处理涉及的热点计算任务,提出基于分块理论的矩阵乘法运算、矩阵分解运算等数值计算并行方法,对比了单核和多核环境下的计算时间。通过多个算例验证多核并行设计方法的有效性,利用.NET4.0框架下的Parallel Extensions实现相关并行设计。实验结果表明,GNSS数据处理的多核并行计算能充分发挥多核体系带来的性能优势,极大提高资源利用效率和GNSS数据处理效率。 相似文献
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本文提出了面向CPU+GPU异构环境的数据密集型矢量多边形地理大数据并行计算框架(PFGAP)。PFGAP将数据密集型矢量多边形地理大数据的并行计算分解为算子、数据、粒度、并行环境及任务调度5个模块,分别设计相应的负载均衡并行计算策略;通过封装并行计算实现细节及数据密集型多边形算子的快速并行化。试验采用多边形三角剖分、栅格化及投影变换作为测试算例,采用土地利用数据作为测试数据,在不同类型的并行环境中计算并行效率。结果表明,PFGAP能很好地适用于不同类型的数据集、算子及并行计算环境。利用PFGAP实现的并行算法显著地降低了串行执行时间,取得了40.03的最优并行加速比。试验还分别测试了各个模块涉及的并行策略,结果表明取得的并行效率优于现有并行策略。 相似文献
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通过对MASNUM海浪数值模式的串行程序的结构研究和对串行模式进行并行化分析,提取了可并行部分的程序计算模块。利用消息传递接口(MPI)和Fortran语言,将模式中可并行计算部分进行了划分,同时分配多个进程并行计算。测试结果表明,MASNUM并行模式的效率较高,满足业务化预报的要求。 相似文献
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为满足GNSS数据处理效率不断提升的需求,提出并开发了一套分布式并行计算框架,并基于该框架实现了全球电离层模型的分布式并行解算。采用2台服务器和4台台式机,对全球电离层建模分别测试了单机多线程、多机分布式等并行计算方案,并分析了不同方案建模的数据处理效率。结果表明,采用多线程并行计算可以大幅提高数据处理效率,且当开启线程数与计算机CPU核心数一致时效率提升最佳;采用多机分布式并行计算可进一步提高数据处理效率,使用4台台式机相对于单台台式机解算时间减少约60%,使用2台服务器相对于单台服务器解算时间减少约18%;采用分布式并行计算方案,可充分利用多台计算机资源来提高全球电离层建模效率,对电离层产品快速发布、建模算法的测试等具有重要的意义,对多系统GNSS精密定轨与定位、大网解算也具有很好的参考价值。 相似文献
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波形分解是机载激光雷达全波形数据处理的重要基础工作,通过求解波形函数模型的参数,将波形数据利用具体的函数模型拟合出来,实现对全波形及其中各个子波形函数表达。LM(Levenberg-Marquardt)算法及其改进的算法是波形分解中对参数进行拟合求解的常用方法。针对LM算法在参数拟合计算的过程中存在大量迭代和矩阵运算,提出了基于线程块组和线程两级并行粒度的并行计算方案。将串行多次循环迭代求解参数改为单次并行计算取最佳值实现对参数的选择,将矩阵运算进行线程块的协同并行计算,实现了LM算法在通用计算图形处理器上的并行计算。实验证明,在规定阈值条件下,并行LM降低了算法的迭代次数,提高了波形分解LM算法的计算效率,为提高波形分解的处理效率提供了研究思路。 相似文献
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ABSTRACTLight detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle ‘big’ LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework. 相似文献
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Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition 总被引：3，自引：0，他引：3
We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach
is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm
for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and
block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure.
Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the
algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation
of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime
results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming
satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month
of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations
and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion
strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development
stage, the LSQR method appears ready to deal with real-data applications. 相似文献
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球近似下地球外空间任意类型场元的地形影响 总被引：1，自引：0，他引：1
传统的重力归算方法只适用于地球表面上的重力异常,不能用于扰动重力、垂线偏差、重力梯度等其他类型扰动重力场元,不适合处理除地面外其他高度上场元的地形影响问题。当前,地球重力场探测的场元类型越来越丰富,探测的高度也逐渐转向航空和卫星高度,精确处理地球外空间各种类型重力场元的地形影响已成为地球重力场领域面临的重要课题。本文通过直接分解由地形生成的具有调和性质的引力场,从而导出地球外空间任意高度、任意类型扰动重力场元的地形影响,在此基础上给出在球近似下地形影响的严密算法和高精度快速算法。利用本文推荐的地形影响计算方案,可以方便地处理各种类型地面重力、海洋重力、航空重力、卫星重力、卫星测高数据的地形影响,从而丰富重力场数据处理的内涵,改善地球重力场算法的性能。 相似文献
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以拟合方差最小为准则,通过点质量法拟合船载重力测量数据,得到点质量大小、埋深等参数。回避点质量法数值求解的不稳定性问题,借鉴移去-恢复技术的思路,利用该参数计算船载重力测量点上的重力异常,并将其在测线上的重力异常中扣除,计算出船载重力残差值。以点质量大小、埋深等参数计算卫星测高重力格网点上重力异常,同样得到测高重力残差值。采用加权最小曲率格网化方法,将船载重力残差值与测高重力残差值格网化,进而恢复由点质量大小、埋深等参数计算格网点处的重力异常,实现卫星测高与船载重力测量数据融合。经国际船载重力测量数据检核,融合后的模型较国际船载重力测量数据的平均偏差在1~2 mGal（1 Gal=1×10-2 m/s2）,标准差约为4 mGal。本文的研究方法可为陆地、海岸带区域的多种重力数据的融合、航空重力及卫星重力的向下延拓等问题提供参考。 相似文献
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Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach 总被引：1，自引：0，他引：1
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63–84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10–30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters. 相似文献
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Xiaopeng Li 《Journal of Geodesy》2018,92(5):471-485
Radial basis functions (RBFs) have been used extensively in satellite geodetic applications. However, to the author’s knowledge, their role in processing and modeling airborne gravity data has not yet been fully advocated or extensively investigated in detail. Compared with satellite missions, the airborne data are more suitable for these kinds of localized basis functions especially considering the following facts: (1) Unlike the satellite missions that can provide global or near global data coverage, airborne gravity data are usually geographically limited. (2) It is also band limited in the frequency domain. (3) It is straightforward to formulate the RBF observation equations from an airborne gravimetric system. In this study, a set of band-limited RBF is developed to model and downward continue the airborne gravity data for local geoid improvement. First, EIGEN6c4 coefficients are used to simulate a harmonic field to test the performances of RBF on various sampling, noise, and flight height levels, in order to gain certain guidelines for processing the real data. Here, the RBF method not only successfully recovers the harmonic field but also presents filtering properties due to its particular design in the frequency domain. Next, the software was tested for the GSVS14 (Geoid Slope Validation Survey 2014) area in Iowa as well as for the area around Puerto Rico and the US Virgin Islands by use of the real airborne gravity data from the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project. By fully utilizing the three-dimensional correlation information among the flight tracks, the RBF can also be used as a data cleaning tool for airborne gravity data adjustment and cleaning. This property is further extended to surface gravity data cleaning, where conventional approaches have various limitations. All the related numerical results clearly show the importance and contribution of the use of the RBF for high- resolution local gravity field modeling. 相似文献
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利用最小二乘直接法反演卫星重力场模型的MPI并行算法 总被引：2，自引：0，他引：2
针对海量卫星重力数据反演高阶次地球重力场模型的密集型计算任务与高内存耗用问题,基于MPI实现了最小二乘直接法恢复高阶次位系数的并行算法。引入并行读写、分块存储与分块计算等方式完成了设计矩阵的构建、法方程的形成与求解等密集型计算任务的并行算法,数值计算结果表明三者的并行相对效率峰值可分别达到95%、68%、63%。利用GOCE轨道跟踪和径向扰动重力梯度数据(共518 400个历元)分别反演了120、240阶次地球重力场模型,计算时间仅为40 min、7 h,内存耗用峰值仅为290 MB、1.57 GB;采用与GOCE同等噪声水平的观测数据恢复的重力场模型精度与GOCE已发布模型的解算精度相一致,联合GRACE和GOCE的解算模型能够实现二者独立信息的频谱互补,表明本文方法可高效稳定地恢复高阶次地球重力场模型。 相似文献
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重力测量卫星性能不仅与轨道参数、载荷误差、数据分辨率等因素密切相关,也与反演算法有关。传统的分析方法如动力学法、短弧法等用于误差分析,不可避免将算法误差引入分析结果,使得分析结论确定性不足。为解决这一问题,提出了空域最小二乘分析法,用空域格网重力扰动数据替代重力卫星载荷数据反演地球重力场,有效避免了算法误差对于分析结果的影响。分析结果表明,重力卫星在500 km轨道高度、一次数据覆盖条件下,测量重力场最高阶数约为240阶,载荷误差为1×10-10 m·s-2·Hz-1/2水平时,测量重力场最高阶数为136阶,其累积重力异常误差为2.7 mGal,累积大地水准面误差为14 cm。要达到最优测量能力,轨道倾角通常不小于89°。为减小地球引力高频信号对于地球重力场低阶位系数估计值的影响,估计位系数最高阶数需大于240阶。 相似文献