Subscription

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Learning Hub

Conferences

Free Learning

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

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789341072

Length 452 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Data Processing

Author (1):

Avimanyu Bandyopadhyay

View More author details

Table of Contents (17) Chapters

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

Leave a review - let other readers know what you think

Appendix A

Basic ROCm concepts

In Chapter 3, Setting Up a GPU Computing Platform with NVIDIA and AMD, we discussed AMD ROCm and also compared it with NVIDIA CUDA. Like we did in the previous section of this chapter, let's look into the Radeon Open Compute Platform in a similar manner.

AMD ROCm includes a set of fundamental ways to set up a GPU programming platform for Open Compute. The basic ROCm concepts you need to know to start programming on AMD or NVIDIA GPUs are as follows.

Installation procedure and testing

According to the official documentation, an ROCm installation requires performing the following Terminal-based tasks that are compatible with Ubuntu 18.04:

Update your system and install libnuma-dev using the following...

The rest of the chapter is locked

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at R$50/month. Cancel anytime

Authors (1)

Avimanyu Bandyopadhyay

Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!

See other products by Avimanyu Bandyopadhyay