GPU-based parallel computation for discontinuous deformation analysis (DDA) method and its application to modelling earthquake-induced landslide

Graphic Processing Unit (GPU), as a computing device, has upgraded from single-subject graphical processors to multi-core processors with tremendous computational horsepower. This paper proposes to accelerate the DDA using parallel Jacobi Preconditioned Conjugate Gradient (JPCG) technique on GPUs. Based on the results of two numerical examples, the calculation accuracies of the DDA with serial and parallel solvers are validated, and we found that the DDA with parallel solvers exhibits a much higher execution efficiency. The movement process of Daguangbao landslide triggered by the Wenchuan earthquake is replicated and the modeled deposit pattern coincides well with the actual topography after earthquake.     

ONSET数据流水线

随着天文大科学设备的投入使用,传统的开发模式面临程序重复开发,环境依赖冲突等问题。另外,集群是一个高度耦合的计算资源,严重的环境冲突可能导致整个集群不可用。为了解决这个问题,采用微服务的概念开发新的流水线框架,这种框架可以实现短期内开发和部署新的流水线。介绍了通过这种框架开发的ONSET数据流水线,为了实现准实时数据处理,采用MPI和GPU技术对核心程序做了优化,并对最后的性能做了评估。结果表明,这种开发模式可以在短期内搭建满足需求的流水线,这种开发模式对未来多波段多终端的天文数据处理有借鉴意义。     

An IO-efficient parallel implementation of an R2 viewshed algorithm for large terrain maps on a CUDA GPU

A rapid and flexible parallel approach for viewshed computation on large digital elevation models is presented. Our work is focused on the implementation of a derivate of the R2 viewshed algorithm. Emphasis has been placed on input/output (IO) efficiency that can be achieved by memory segmentation and coalesced memory access. An implementation of the parallel viewshed algorithm on the Compute Unified Device Architecture (CUDA), which exploits the high parallelism of the graphics processing unit, is presented. This version is referred to as r.cuda.visibility. The accuracy of our algorithm is compared to the r.los R3 algorithm (integrated into the open-source Geographic Resources Analysis Support System geographic information system environment) and other IO-efficient algorithms. Our results demonstrate that the proposed implementation of the R2 algorithm is faster and more IO efficient than previously presented IO-efficient algorithms, and that it achieves moderate calculation precision compared to the R3 algorithm. Thus, to the best of our knowledge, the algorithm presented here is the most efficient viewshed approach, in terms of computational speed, for large data sets.     

A strategy for raster-based geocomputation under different parallel computing platforms

The demand for parallel geocomputation based on raster data is constantly increasing with the increase of the volume of raster data for applications and the complexity of geocomputation processing. The difficulty of parallel programming and the poor portability of parallel programs between different parallel computing platforms greatly limit the development and application of parallel raster-based geocomputation algorithms. A strategy that hides the parallel details from the developer of raster-based geocomputation algorithms provides a promising way towards solving this problem. However, existing parallel raster-based libraries cannot solve the problem of the poor portability of parallel programs. This paper presents such a strategy to overcome the poor portability, along with a set of parallel raster-based geocomputation operators (PaRGO) designed and implemented under this strategy. The developed operators are compatible with three popular types of parallel computing platforms: graphics processing unit supported by compute unified device architecture, Beowulf cluster supported by message passing interface (MPI), and symmetrical multiprocessing cluster supported by MPI and open multiprocessing, which make the details of the parallel programming and the parallel hardware architecture transparent to users. By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputation algorithms compatible with three popular parallel computing platforms. Practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of PaRGO.     

半全局匹配算法的多基线扩展及GPU并行处理方法

文章分析了半全局匹配算法基本原理,将其扩展应用于遥感影像的多基线匹配,在进一步提高匹配可靠性的同时,保留了算法的规则结构;并研究其图形图像处理器(GPU)细粒度并行处理技术,重点探讨匹配代价立方体生成与聚合过程的核函数优化策略与线程组织方案,最后利用Tesla C2050 GPU并行加速卡对3幅UCD航空影像进行MVLL多基线匹配半全局优化GPU并行处理实验证明了该算法的有效性和高效性.     

Review of the algebraic linear methods and parallel implementation in numerical simulation of groundwater flow

The desire to increase spatial and temporal resolution in modeling groundwater system has led to the requirement for intensive computational ability and large memory space. In the course of satisfying such requirement, parallel computing has played a core role over the past several decades. This paper reviews the parallel algebraic linear solution methods and the parallel implementation technologies for groundwater simulation. This work is carried out to provide guidance to enable modelers of groundwater systems to make sensible choices when developing solution methods based upon the current state of knowledge in parallel computing.     

一种基于CUDA的大数据量地理加权回归并行加速算法

针对传统地理加权回归（GWR）在大数据量计算中存在的计算效率低、内存占用大、数据规模受限等问题,本文提出了快速并行地理加权回归（FPGWR）算法,基于英伟达CUDA架构实现了GWR的并行加速,将串行过程分解为并行的独立回归计算模块,同时优化了内存使用模型,提高了算法的运行速度。对比FPGWR和传统GWR在不同数量级模拟数据上和真实数据上的运行速度,结果显示,FPGWR能够支持更大规模的样本量计算并有效提升运行效率,数据量越大加速效果越显著。