Accelerating geospatial analysis on GPUs using CUDA

GPU enabled XDraw viewshed analysis

“…Each thread in a GPU block, represents a single LoS calculation; therefore 16,000 GPU threads for example would represent 16,000 individual parallel LoS calculations, disregarding certain GPU bandwidth limits. This volume of threads fits well with the GPGPU paradigm, which argues for a very high number of independent threaded operations being executed simultaneously over a sustained [10,14]. There has been a concerted effort to discover the potential performance benefits of using the GPU as a viewshed processor [15,12,3], which aims to either modify existing CPU algorithms, or design new algorithms specifically for CUDA hardware; [6] presents a novel algorithm for 'combing' the DEM via thread directions.…”

“…It can be then stated that XDraw-O is the most efficient algorithm, across the test cases for generating viewsheds in the general case. Further analysis will be required to determine if this performance difference is maintained when memory optimization techniques are applied to the LoS algorithms, such as those proposed by Zhao, Padmanabhan and Wang [16] for the DDA algorithm or the work of Xia, Yang and Xingmin [14] for creating more optimized ray traversals across a DEM. It can be assumed that the number of GPU cores would significantly affect the performance of the algorithm.…”

“…The performance results detailed correlate well with the performance increases of many previous GPU viewshed studies. Xia, Yang and Xingmin [14] and Zhao, Padmanabhan and Wang [16] demonstrated that significant performance improvements can be made with careful consideration to data operations. Narasiman et al [9] also demonstrated that spatial independency is a key factor when generating viewsheds on a GPU.…”

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

“…A basic octree in the three-dimensional space is an 8-way branching tree, wherein at each level a cubic domain is decomposed into eight equal-size cubes. By traversing all the leaf nodes of the octree, the generated sub-domains are represented by a specific data structure which is linked and stored in a single directional list [33]. Big traffic data in the computational domain can be decomposed into data pieces in sub-domains by the octree structure.…”

Speeding up the high-accuracy surface modelling method with GPU

“…Beutel et al (2010) realized a GPU-based natural neighbour interpolation algorithm to generate DEM and accomplished a 10 times speedup ratio. Xia et al (2011) implemented IDW algorithm on a GPU and made a range of speedups from 12 to 33 depending on the scale and resolution of dataset. Hence, fully harvesting the computational power of a GPU has the potential to shorten the computing time of HASM.…”