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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

Device properties of Jetson TX1 GPU

CUDA provides a simple interface to determine the capabilities of a GPU device, which is Tegra X1 present on a Jetson TX1 board. It is important to find out the properties of the device that will help in writing optimal programs for it. The program to find the properties of the device is available in the CUDA sample programs installed with JetPack in the home folder. You can also run the program we developed in the second chapter to find out the device properties.

The output of the program on an Nvidia Tegra X1 GPU is as follows:

The JetPack 3.3 installs the CUDA 9.0 runtime version. The global memory for the GPU device is around 4 GB, with a GPU clock speed of around 1 GHz. This clock speed is slower than the GeForce 940 GPU mentioned earlier in this book. The memory clock speed is only 13 MHz compared to 2.505 GHz on GeForce 940, which...

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