To summarize, in this chapter, you were introduced to programming concepts in CUDA C and how parallel computing can be done using CUDA. It was shown that CUDA programs can run on any NVIDIA GPU hardware efficiently and in parallel. So, CUDA is both efficient and scalable. The CUDA API functions over and above existing ANSI C functions needed for parallel data computations were discussed in detail. How to call device code from the host code via a kernel call, configuring of kernel parameters, and a passing of parameters to the kernel were also discussed by taking a simple two-variable addition example. It was also shown that CUDA does not guarantee the order in which the blocks or thread will run and which block is assigned to which multi-processor in hardware. Moreover, vector operations, which take advantage of parallel-processing capabilities of GPU and CUDA, were discussed...
-
Book Overview & Buying
-
Table Of Contents
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
By :
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
By:
Overview of this book
Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications.
To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples.
Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python.
By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach.
Table of Contents (15 chapters)
Preface
Free Chapter
Introducing CUDA and Getting Started with CUDA
Parallel Programming using CUDA C
Threads, Synchronization, and Memory
Advanced Concepts in CUDA
Getting Started with OpenCV with CUDA Support
Basic Computer Vision Operations Using OpenCV and CUDA
Object Detection and Tracking Using OpenCV and CUDA
Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1
Deploying Computer Vision Applications on Jetson TX1
Getting Started with PyCUDA
Working with PyCUDA
Basic Computer Vision Applications Using PyCUDA
Assessments
Other Books You May Enjoy
