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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

Chapter 4

  1. CPU timers will include time overhead of thread latency in OS and scheduling in OS, among many other factors. The time measured using CPU will also depend on the availability of high precision CPU timer. The host is frequently performing asynchronous computation while GPU kernel is running, and hence CPU timers may not give correct time for kernel executions.
  2. Open Nvidia Visual profiler from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\libnvvp. Then, go to -> New Session and Select .exe file for matrix multiplication example. You can visualize the performance of your code.
  3. Divide by zero, incorrect variable types or sizes, nonexistent variables, subscripts out of range etc are examples of semantic errors.
  4. An example of thread divergence can be given as follows:
__global__ void gpuCube(float *d_in, float *d_out) 
{
int tid = threadIdx.x;
if(tid%2 =...
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