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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

Virtualization

Now that we know about the basic differences between open and closed environments, along with their advantages and disadvantages, let's proceed further into the virtualization concept. This is essential before we move on to our primary discussion—containerization, which is the main theme of this chapter.

As you might be well aware now, virtualization is a way to run applications and operating systems in an isolated location, allocated on a physical hard disk and RAM. Physical hard disk space and RAM can be use to allocate resources and create multiple virtual environments. The physical space allocation is referred to as the host, whereas the virtual space allocations are referred to as guests.

In the earlier chapters, we discussed installation and configuration steps for different Python modules with Conda. All of those steps were in fact, ways to virtualize...

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