CATCH: a FORTRAN program for measuring catchment area from digital elevation models
Computers & Geosciences
Calculating catchment area with divergent flow based on a regular grid
Computers & Geosciences
Efficient Flow Computation on Massive Grid Terrain Datasets
Geoinformatica
An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm
International Journal of Geographical Information Science
The Scalable Heterogeneous Computing (SHOC) benchmark suite
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
Parallel Viewshed Analysis on GPU Using CUDA
CSO '10 Proceedings of the 2010 Third International Joint Conference on Computational Science and Optimization - Volume 01
Extraction of hydrological proximity measures from DEMs using parallel processing
Environmental Modelling & Software
Parallel contributing area calculation with granularity control on massive grid terrain datasets
Computers & Geosciences
A layered approach to parallel computing for spatially distributed hydrological modeling
Environmental Modelling & Software
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As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.