Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

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.

Building Depth Maps Using an Active-Pulse Television Measuring System in Real Time Domain

“…The main function of the GPU is to work on a huge number of tasks that are independent of each other, which is why the GPU contains a huge number of execution units [25].…”

Accelerating Multiple Flow Accumulation Algorithm Using MPI on a Cluster of Computers

“…Parallel implementation of watershed analysis algorithms based on large-scale DEM processing has attracted attention in the scientific community in the last decade (Qin & Zhan, 2012;Stojanovic & Stojanovic, 2019;Rueda et al, 2016). (Quin & Zhan, 2012) perform parallelization of multiple-flow direction algorithm on GPU.…”

“…They compare and analyze the performance and programming effort of two proposed parallel implementations for different input datasets. In (Stojanovic & Stojanovic, 2019) multiple flow direction algorithm (MFD-md) for watershed analysis is implemented and evaluated on multicore CPU and many-core GPU. Different parallel implementations, native CUDA for GPU and two OpenACC implementations are proposed.…”