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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

Questions

  1. Why aren't CPU timers used to measure the performance of kernel functions?
  2. Try to visualize the performance of the matrix multiplication code implemented in the previous chapter using the Nvidia Visual Profiler tool.
  3. Give different examples of semantic errors encountered in programs.
  4. What is the drawback of thread divergence in kernel functions? Explain with an example.
  5. What is the drawback of using the cudahostAlloc function to allocate memory on the host?
  6. Justify the following statement: the order of operations in CUDA streams is very important to improve the performance of a program.
  7. How many blocks and threads should be launched for a 1024 x 1024 image for good performance using CUDA?
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