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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

Error handling in CUDA

We have not checked the availability of GPU devices or memory for our CUDA programs. It may happen that, when you run your CUDA program, the GPU device is not available or is out of memory. In that case, you may find it difficult to understand the reason for the termination of your program. Therefore, it is a good practice to add error handling code in CUDA programs. In this section, we will try to understand how we can add this error handling code to CUDA functions. When the code is not giving the intended output, it is useful to check the functionality of the code line-by-line or by adding a breakpoint in the program. This is called debugging. CUDA provides debugging tools that can help. So, in the following section, we will see some debugging tools that are provided by Nvidia with CUDA.

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