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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 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

Installing CuPy and Numba for Python within an existing Anaconda environment

Here, we will learn how to integrate CuPy and Numba into our code development environment so that we can communicate with GPU devices in a simpler way than we saw for PyCUDA and PyOpenCL.

Coupling Python with CuPy

Since we have already learned about CUDA's installation and implementation, it will now be easier for us to get started with our CuPy installation procedure for Python, as it was originally developed to implement CUDA. As we have also learned about Anaconda and its setup, we can make use of Python 2.x or 3.x, which are readily available with an existing Anaconda configuration. Setting up CuPy will enable implementing CUDA kernels within...

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