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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

You're reading from   Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA Effective techniques for processing complex image data in real time using GPUs

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789348293
Length 380 pages
Edition 1st Edition
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Author (1):
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Bhaumik Vaidya Bhaumik Vaidya
Author Profile Icon Bhaumik Vaidya
Bhaumik Vaidya
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Table of Contents (15) Chapters Close

Preface 1. Introducing CUDA and Getting Started with CUDA 2. Parallel Programming using CUDA C FREE CHAPTER 3. Threads, Synchronization, and Memory 4. Advanced Concepts in CUDA 5. Getting Started with OpenCV with CUDA Support 6. Basic Computer Vision Operations Using OpenCV and CUDA 7. Object Detection and Tracking Using OpenCV and CUDA 8. Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1 9. Deploying Computer Vision Applications on Jetson TX1 10. Getting Started with PyCUDA 11. Working with PyCUDA 12. Basic Computer Vision Applications Using PyCUDA 13. Assessments 14. Other Books You May Enjoy

Texture memory

Texture memory is another read-only memory that can accelerate the program and reduce memory bandwidth when data is read in a certain pattern. Like constant memory, it is also cached on a chip. This memory was originally designed for rendering graphics, but it can also be used for general purpose computing applications. It is very effective when applications have memory access that exhibits a great deal of spatial locality. The meaning of spatial locality is that each thread is likely to read from the nearby location what other nearby threads read. This is great in image processing applications where we work on 4-point connectivity and 8-point connectivity. A two-dimensional spatial locality for accessing memory location by threads may look something like this:

Thread 0 Thread 2
Thread 1

Thread 3

General global memory cache will not be able to capture...

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