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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

Memory architecture

The execution of code on a GPU is divided among streaming multiprocessors, blocks, and threads. The GPU has several different memory spaces, with each having particular features and uses and different speeds and scopes. This memory space is hierarchically divided into different chunks, like global memory, shared memory, local memory, constant memory, and texture memory, and each of them can be accessed from different points in the program. This memory architecture is shown in preceding diagram:

As shown in the diagram, each thread has its own local memory and a register file. Unlike processors, GPU cores have lots of registers to store local data. When the data of a thread does not fit in the register file, the local memory is used. Both of them are unique to each thread. The register file is the fastest memory. Threads in the same blocks have shared memory...

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