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Hands-On GPU Computing with Python

You're reading from   Hands-On GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Length 452 pages
Edition 1st Edition
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Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Author Profile Icon Avimanyu Bandyopadhyay
Avimanyu Bandyopadhyay
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing FREE CHAPTER 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

Basic CUDA concepts

Since we have discussed various points of Compute Unified Device Architecture (CUDA) in our earlier chapters, let's now focus on its technical aspects and GPU programmability.

Apart from the installation procedure that's unique to CUDA, the remaining concepts that will be discussed here in brief will be useful for getting started with GPU programming on all platforms.

Installing and testing

Before we discuss some fundamental GPU programming concepts, it is essential that we revisit the CUDA installation and testing procedure that we covered earlier in Chapter 2, Designing a GPU Computing Strategy, while concluding the DIY section. Step-by-step screenshots of a fresh Ubuntu 18.04 Linux installation...

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