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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay
LinkedIn
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

Table of Contents (17) Chapters

Preface Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
Introducing GPU Computing Designing a GPU Computing Strategy Setting Up a GPU Computing Platform with NVIDIA and AMD Section 2: Hands-On Development with GPU Programming
Fundamentals of GPU Programming Setting Up Your Environment for GPU Programming Working with CUDA and PyCUDA Working with ROCm and PyOpenCL Working with Anaconda, CuPy, and Numba for GPUs Section 3: Containerization and Machine Learning with GPU-Powered Python
Containerization on GPU-Enabled Platforms Accelerated Machine Learning on GPUs GPU Acceleration for Scientific Applications Using DeepChem Other Books You May Enjoy 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:

  1. Update your system and install libnuma-dev using the following...
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Authors (1)

Profile icon Avimanyu Bandyopadhyay
LinkedIn
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!
Read more
See other products by Avimanyu Bandyopadhyay

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