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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 FREE CHAPTER 2. Parallel Programming using CUDA C 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. Which programming language is used to define the kernel function using the SourceModule class in PyCUDA? Which compiler will be used to compile this kernel function?
  2. Write a kernel call function for the myfirst_kernel function used in this chapter, with the number of blocks equal to 1024 x 1024 and threads per block equal to 512 x 512.
  3. State true or false: The block execution inside PyCUDA program is in sequential order.
  4. What is the advantage of using the In, Out ,and inout driver class primitives in PyCUDA programs?
  5. Write a PyCUDA program to add two to every element of a vector with an arbitrary size using the gpuarray class.
  6. What is the advantage of using CUDA events to measure the time for a kernel execution?
  7. State true or false: The gpuarray class is the GPU device version of the numpy library in Python.
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