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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. Suppose you want to launch 100,000 threads in parallel. What is the best choice of the number of blocks in a grid and number of threads in a block and why?
  2. Write a CUDA program to find out the cube of every element in an array when the number of elements in the array is 100,000.
  3. State whether the following statement is true or false and give a reason: An assignment operator between local variables will be faster than an assignment operator between global variables.
  4. What is register spilling? How it can harm the performance of your CUDA program?
  5. State whether the following line of code will give the required output or not: d_out[i] = d_out[i-1].
  1. State whether the following statement is true or false and give a reason: Atomic operations increase the execution time for CUDA programs.
  2. Which kinds of communication patterns are ideal for using texture memory in your CUDA...
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