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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

Installing PyOpenCL for Python (AMD and NVIDIA)

Through our documented source code on OpenCL, we now know the basic ways of OpenCL implementation compared to CUDA syntax. So, let's get started with our PyOpenCL installation procedure for Python. Once again, note that you need not install Python, as it is already available (both 2.x and 3.x) with a freshly installed version of an Ubuntu 18.04 Linux operating system.

Setting up PyOpenCL will enable implementing OpenCL kernels within your existing Python setup of choice and then compute them on your AMD or NVIDIA GPU.

Once again, we will re-examine our two primary ways of installation, as we illustrated previously for PyCUDA. Note that these steps are independent of the previous chapter and can be used as a standalone reference for installing PyOpenCL.

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