International Journal of Geographical Information Science ( IF 5.7 ) Pub Date : 2020-12-04 , DOI: 10.1080/13658816.2020.1844207 A. J. Sanchez-Fernandez 1 , L. F. Romero 1 , G. Bandera 1 , S. Tabik 2
ABSTRACT
Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers of memory accesses to 2D arrays which, despite being regular, result in poor data locality in memory. Here, we propose a new methodology called skewed Digital Elevation Model (sDEM), which substantially improves the locality of memory accesses and increases the inherent parallelism involved in the computation of rotational sweep-based algorithms. In particular, sDEM applies a data restructuring technique before accessing the memory and performing the computation. To demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study considering different implementations for single-core, multi-core, single-GPU and multi-GPU platforms. We conducted two experiments to compare sDEM with (i) the most commonly used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm. In the first experiment, sDEM is on average 8.8x faster than current GIS software despite being able to consider only few points because of their limitations. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm in the best case.
中文翻译:
多GPU系统地形表面分析的数据重定位方法:以全视域问题为例
摘要
数字高程模型 (DEM) 是用于建模视线的重要数据集,例如无线电信号、声波和人类视觉。这些通常使用旋转扫描算法进行分析。然而,此类算法需要对二维数组进行大量内存访问,尽管是有规律的,但会导致内存中的数据局部性较差。在这里,我们提出了一种称为偏斜数字高程模型 (sDEM) 的新方法,该方法显着改善了内存访问的局部性并增加了基于旋转扫描的算法计算中涉及的固有并行性。特别是,sDEM 在访问内存和执行计算之前应用了数据重组技术。为了证明 sDEM 的高效率,我们使用总视域计算问题作为案例研究,考虑单核、多核、单 GPU 和多 GPU 平台的不同实现。我们进行了两个实验,将 sDEM 与 (i) 最常用的地理信息系统 (GIS) 软件和 (ii) 最先进的算法进行比较。在第一个实验中,sDEM 平均比当前的 GIS 软件快 8.8 倍,尽管由于它们的局限性只能考虑很少的点。在第二个实验中,sDEM 在最佳情况下比最先进的算法快 827.3 倍。sDEM 平均比当前的 GIS 软件快 8.8 倍,尽管由于它们的局限性只能考虑很少的点。在第二个实验中,sDEM 在最佳情况下比最先进的算法快 827.3 倍。sDEM 平均比当前的 GIS 软件快 8.8 倍,尽管由于它们的局限性只能考虑很少的点。在第二个实验中,sDEM 在最佳情况下比最先进的算法快 827.3 倍。