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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing 3. Designing a GPU Computing Strategy 4. Setting Up a GPU Computing Platform with NVIDIA and AMD 5. Section 2: Hands-On Development with GPU Programming
6. Fundamentals of GPU Programming 7. Setting Up Your Environment for GPU Programming 8. Working with CUDA and PyCUDA 9. Working with ROCm and PyOpenCL 10. Working with Anaconda, CuPy, and Numba for GPUs 11. Section 3: Containerization and Machine Learning with GPU-Powered Python
12. Containerization on GPU-Enabled Platforms 13. Accelerated Machine Learning on GPUs 14. GPU Acceleration for Scientific Applications Using DeepChem 15. Other Books You May Enjoy Appendix A

Introducing GPU Computing

Many years ago, I used to think that a graphics processing unit (GPU), more commonly known as a graphics card, was just a device dedicated to playing video games on a computer at their maximum potential. But, one day, while going through a textbook (Advanced Computer Architecture by Kai Hwang), I realized that I was unaware of a world that goes way beyond PC gaming.

Without a doubt, most consumer GPUs are manufactured to achieve those amazing graphics and visuals to enable some spell-binding gameplay. But there's a world that explores its application a whole lot further, and that is the world of GPU computing.

In this chapter, we are going to learn the basic ideas behind GPU computing, a historical recap on computing, and the rise of GPU computing. We will also read about the simplicity of Python and the power of GPUs, and learn about the scope of...

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