Digital image processing using parallel computing based on CUDA technology

This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU

Accelerating ant colony optimization-based edge detection on the GPU using CUDA

Ant Colony Optimization (ACO) is a nature-inspired metaheuristic that can be applied to a wide range of optimization problems. In this paper we present the first parallel implementation of an ACO-based (image processing) edge detection algorithm on the Graphics Processing Unit (GPU) using NVIDIA CUDA. We extend recent work so that we are able to implement a novel data-parallel approach that maps individual ants to thread warps. By exploiting the massively parallel nature of the GPU, we are able to execute significantly more ants per ACO-iteration allowing us to reduce the total number of iterations required to create an edge map. We hope that reducing the execution time of an ACO-based implementation of edge detection will increase its viability in image processing and computer vision

Generic Techniques in General Purpose GPU Programming with Applications to Ant Colony and Image Processing Algorithms

In 2006 NVIDIA introduced a new unified GPU architecture facilitating general-purpose computation on the GPU. The following year NVIDIA introduced CUDA, a parallel programming architecture for developing general purpose applications for direct execution on the new unified GPU. CUDA exposes the GPU's massively parallel architecture of the GPU so that parallel code can be written to execute much faster than its sequential counterpart. Although CUDA abstracts the underlying architecture, fully utilising and scheduling the GPU is non-trivial and has given rise to a new active area of research. Due to the inherent complexities pertaining to GPU development, in this thesis we explore and find efficient parallel mappings of existing and new parallel algorithms on the GPU using NVIDIA CUDA. We place particular emphasis on metaheuristics, image processing and designing reusable techniques and mappings that can be applied to other problems and domains. We begin by focusing on Ant Colony Optimisation (ACO), a nature inspired heuristic approach for solving optimisation problems. We present a versatile improved data-parallel approach for solving the Travelling Salesman Problem using ACO resulting in significant speedups. By extending our initial work, we show how existing mappings of ACO on the GPU are unable to compete against their sequential counterpart when common CPU optimisation strategies are employed and detail three distinct candidate set parallelisation strategies for execution on the GPU. By further extending our data-parallel approach we present the first implementation of an ACO-based edge detection algorithm on the GPU to reduce the execution time and improve the viability of ACO-based edge detection. We finish by presenting a new color edge detection technique using the volume of a pixel in the HSI color space along with a parallel GPU implementation that is able to withstand greater levels of noise than existing algorithms

ReS2tAC -- UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV

ReS²tAC—UAV-borne real-time SGM stereo optimized for embedded ARM and CUDA devices

With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV

SkelCL - A Portable Skeleton Library for High-Level GPU Programming

While CUDA and OpenCL made general-purpose programming for Graphics Processing Units (GPU) popular, using these programming approaches remains complex and error-prone because they lack high-level abstractions. The especially challenging systems with multiple GPU are not addressed at all by these low-level programming models. We propose SkelCL – a library providing so-called algorithmic skeletons that capture recurring patterns of parallel computation and communication, together with an abstract vector data type and constructs for specifying data distribution. We demonstrate that SkelCL greatly simplifies programming GPU systems. We report the competitive performance results of SkelCL using both a simple Mandelbrot set computation and an industrial-strength medical imaging application. Because the library is implemented using OpenCL, it is portable across GPU hardware of different vendors