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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

Executing threads on a device

We have seen that, while configuring kernel parameters, we can start multiple blocks and multiple threads in parallel. So, in which order do these blocks and threads start and finish their execution? It is important to know this if we want to use the output of one thread in other threads. To understand this, we have modified the kernel in the hello,CUDA! program we saw in the first chapter, by including a print statement in the kernel call, which prints the block number. The modified code is as follows:

#include <iostream>
#include <stdio.h>
__global__ void myfirstkernel(void)
{
//blockIdx.x gives the block number of current kernel
printf("Hello!!!I'm thread in block: %d\n", blockIdx.x);
}
int main(void)
{
//A kernel call with 16 blocks and 1 thread per block
myfirstkernel << <16,1>> >();

//Function...
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