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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

Summary

This chapter explained the launch of multiple blocks, with each having multiple threads from the kernel function. It showed the method for choosing the two parameters for a large value of threads. It also explained the hierarchical memory architecture that can be used by CUDA programs. The memory nearest to the thread being executed is fast, and as we move away from it, memories get slower. When multiple threads want to communicate with each other, then CUDA provides the flexibility of using shared memory, by which threads from the same blocks can communicate with each other. When multiple threads use the same memory location, then there should be synchronization between the memory access; otherwise, the final result will not be as expected. We also saw the use of an atomic operation to accomplish this synchronization. If some parameters remain constant throughout the...

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