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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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Author (1):
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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 FREE CHAPTER
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

Understanding how Anaconda works with CuPy and Numba

You must be familiar by now with the free and open source Anaconda distribution, as we have been using it for all our code examples so far. Let's further explore Anaconda and learn more about its features, especially in terms of accelerated computing. Accelerated computing in Anaconda is extremely significant in the deployment for scientific computing with Python.

So far, we covered Python programming implementations inclusive of C/C++ syntax. But from now on, it is important that we focus more on a programming implementation only with pure Python syntax, a perspective that is highly significant for maintaining a seamless programming experience with Python, irrespective of the CPU or GPU platform. Adopting this approach makes it a lot easier for Python programmers to migrate towards a GPU-enabled experience. The more similar...

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