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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

Measuring performance of PyCUDA programs using CUDA events

So far, we have not determined the performance of the PyCUDA programs explicitly. In this section, we will see how to measure the performance of the programs using CUDA events. This is a very important concept in PyCUDA because it will allow you to choose the best performing algorithms for a particular application from many options.

CUDA events

We can use Python time measuring options for measuring the performance of CUDA programs, but it will not give accurate results. It will include the time overhead of thread latency in the OS and scheduling in the OS among many other factors. The time measured using the CPU will also depend on the availability of a high-precision...

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