Abstract
Watershed analysis, as a fundamental component of digital terrain analysis, is based on the Digital Elevation Model (DEM), which is a grid (raster) model of the Earth surface and topography. Watershed analysis consists of computationally and data intensive computing algorithms that need to be implemented by leveraging parallel and high-performance computing methods and techniques. In this paper, the Multiple Flow Direction (MFD) algorithm for watershed analysis is implemented and evaluated on multi-core Central Processing Units (CPU) and many-core Graphics Processing Units (GPU), which provides significant improvements in performance and energy usage. The implementation is based on NVIDIA CUDA (Compute Unified Device Architecture) implementation for GPU, as well as on OpenACC (Open ACCelerators), a parallel programming model, and a standard for parallel computing. Both phases of the MFD algorithm (i) iterative DEM preprocessing and (ii) iterative MFD algorithm, are parallelized and run over multi-core CPU and GPU. The evaluation of the proposed solutions is performed with respect to the execution time, energy consumption, and programming effort for algorithm parallelization for different sizes of input data. An experimental evaluation has shown not only the advantage of using OpenACC programming over CUDA programming in implementing the watershed analysis on a GPU in terms of performance, energy consumption, and programming effort, but also significant benefits in implementing it on the multi-core CPU.
Highlights
Geospatial processing and analysis of large amounts of geospatial data in the Geographic Information System (GIS) represents an application domain that obtains significant benefits from parallel and high-performance computing [1]
We have shown that the parallel implementation of OpenACC watershed analysis outperforms corresponding CUDA implementation for different Digital Elevation Model (DEM) sizes, while consuming less energy and requiring less programming efforts
The same directive has to be used before the parallel region in the GenerateFlowDirections function with wDEM [:size], RMFD [:height] [:width] as parameters, before the parallel region in GenerateFlowFractions function with wDEM[:size], RMFD[:height][:width], flowFractions [:size1] as parameters, and before the parallel region in FlowAccumulation function with wDEM [:length], flowFractions [:size1], RMFD [:height] [:width], flowAccumulation[:length] as parameters
Summary
Geospatial processing and analysis of large amounts of geospatial data in the Geographic Information System (GIS) represents an application domain that obtains significant benefits from parallel and high-performance computing [1]. There are several HPC approaches that can be applied to improve the performance of advanced computationally and data intensive applications, such as the watershed analysis Such approaches are based on a parallel computing paradigm applied through multi/many-core computer systems, or through distributed computing infrastructure, such as a cluster or cloud infrastructure. General-purpose computing on a Graphics Processing Unit (GPGPU) represents a new paradigm based on tightly coupled, massively parallel computing units [2] It represents a method and a technique for performing general purpose computations on a GPU by using an appropriate framework, an API, and a programming model, such as OpenCL, Microsoft’s DirectCompute, and NVIDIA CUDA. Experimental evaluation proves expected improvements in performance of watershed analysis with respect to a single-core CPU-based solution It shows feasibility in using CUDA and OpenACC programming frameworks on GPUs and multi-core CPUs, for digital terrain analysis and similar GIS algorithms.
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