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11. Working with PyCUDA

4,103 views · Published 21 September 2018 · 8:08 · Indexed 2 October 2026

Channel: Packt · 2018 · Science & Technology

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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA is available from: 

Packt.com: http://bit.ly/2xRyGea

Amazon: https://amzn.to/2OPhcXj

This is the “Code in Action” video for chapter 11 of Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA by Bhaumik Vaidya, published by Packt. It includes the following topics:

00:17 Writing the first program in PyCUDA 
00:52 Accessing GPU device properties from PyCUDA program
1:41 Thread and block execution in PyCUDA
2:37 Adding two numbers in PyCUDA 
3:08 Simplifying the addition program using driver class
3:36 Measuring performance of PyCUDA using large array addition   
4:21 Simple kernel invocation with multidimensional threads
4:51 Using inout with the kernel invocation
5:27 Dot product using GPU array
5:36 Matrix multiplication
6:41 Element-wise kernel in PyCUDA
7:03 Reduction kernel 
7:36 Scan kernel 

This book is a guide to explore how accelerating of computer vision applications using GPUs will help you develop algorithms that work on complex image data in real time. It will solve the problems you face while deploying these algorithms on embedded platforms with the help of development boards from NVIDIA such as the Jetson TX1, Jetson TX2, and Jetson TK1.

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Video created by Bhaumik Vaidya

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